Joint Technical Support Document:

Final Rulemaking for 2017-2025 

Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate 

Average Fuel Economy Standards
EPA-420-R-12-901

August 2012

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

and

National Highway Traffic Safety Administration 

U.S. Department of Transportation

Joint Technical Support Document:

Final Rulemaking for 2017-2025 

Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate 

Average Fuel Economy Standards
 Contents

i 

Executive Summary ................................................................................................................... i

Chapter 1: The Baseline and Reference Vehicle Fleets ................................................. 1-1

1.1 Why do the agencies establish baseline and reference vehicle fleets? ..................... 1-1

1.2 The 2008 and 2010 based vehicle fleet projections ................................................... 1-2

1.2.1 Why did the agencies develop two fleet projections for the final rule? ........ 1-2

1.3 The 2008 Based Fleet Projection ................................................................................ 1-4

1.3.1 On what data is the MY2008 baseline vehicle fleet based? ........................... 1-4

1.3.2 The MY 2008 Based MY 2017-2025 Reference Fleet ................................... 1-13

1.3.3 What are the sales volumes and characteristics of the MY 2008 based 

reference fleet? ................................................................................................................ 1-25

1.4 The 2010 MY Based Fleet.......................................................................................... 1-31

1.4.1 On what data is the MY 2010 baseline vehicle fleet based? ........................ 1-31

1.4.2 The MY 2010 Based MY 2017-2025 Reference Fleet ................................... 1-40

1.4.3 What are the sales volumes and characteristics of the MY 2010 based 

reference fleet? ................................................................................................................ 1-48

1.5 What are the differences in the sales volumes and characteristics of the MY 2008 

based and the MY 2010 based reference fleets? ............................................................... 1-54

Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting, and 

How Were They Developed? ................................................................................................ 2-1

2.1 Why are standards attribute-based and defined by a mathematical function? ..... 2-1

2.2 What attribute are the agencies adopting, and why? ............................................... 2-2

2.3 What mathematical functions have the agencies previously used, and why? ........ 2-7

2.3.1 NHTSA in MY 2008 and MY 2011 CAFE (constrained logistic) .................. 2-7

2.3.2 MYs 2012-2016 Light Duty GHG/CAFE (constrained/piecewise linear) ..... 2-8

2.3.3 How have the agencies defined the mathematical functions for the MYs 

2017-2025 standards, and why?....................................................................................... 2-8

2.4 What did the agencies propose for the MYs 2017-2025 curves? ............................. 2-9

2.4.1 What concerns were the agencies looking to address that led them to change 

from the approach used for the MYs 2012-2016 curves? ........................................... 2-10

2.4.2 What methodologies and data did the agencies consider in developing the 

2017-2025 curves presented in the proposal? ............................................................... 2-12

2.5 Once the agencies determined the appropriate slope for the sloped part, how did 

the agencies determine the rest of the mathematical function? ...................................... 2-49
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ii

2.5.1 Cutpoints for Passenger Car curve ................................................................ 2-49

2.5.2 Cutpoints for Light Truck curve .................................................................... 2-51

2.5.3 Once the agencies determined the complete mathematical function shape, 

how did the agencies adjust the curves to develop the proposed standards and 

regulatory alternatives? ................................................................................................. 2-54

2.6 What does the agencies’ updated analysis indicate? .............................................. 2-56

Chapter 3: Technologies Considered in the Agencies’ Analysis ................................... 3-2

3.1 What Technologies did the agencies consider for the final 2017-2025 standards? 3-3

3.2 How did the agencies determine the costs of each of these technologies? ............... 3-8

3.2.1 Direct Costs ........................................................................................................ 3-8

3.2.2 Indirect Costs ................................................................................................... 3-13

3.2.3 Cost reduction through manufacturer learning ........................................... 3-23

3.2.4 Costs Updated to 2010 Dollars ....................................................................... 3-28

3.3 How did the agencies determine effectiveness of each of these technologies? ...... 3-29

3.3.1 Vehicle simulation modeling ........................................................................... 3-29

3.3.2 Lumped parameter Modeling ........................................................................ 3-69

3.4 What cost and effectiveness estimates have the agencies used for each technology?

3-76

3.4.1 Engine technologies ......................................................................................... 3-77

3.4.2 Transmission Technologies ........................................................................... 3-101

3.4.3 Vehicle electrification and hybrid electric vehicle technologies ................ 3-112

3.4.4 Hardware costs for charging grid-connected vehicles ............................... 3-203

3.4.5 Other Technologies Assessed that Reduce CO2 and Improve Fuel Economy

3-208

3.5 How did the agencies consider real-world limits when defining the rate at which 

technologies can be deployed? .......................................................................................... 3-249

3.5.1 Refresh and redesign schedules .................................................................... 3-249

3.6 How are the technologies applied in the agencies' respective models? ............... 3-258

3.7 Maintenance and Repair Costs Associated with New Technologies ................... 3-259

3.7.1 Maintenance Costs ......................................................................................... 3-260

3.7.2 Repair Costs ................................................................................................... 3-264

Chapter 4: Economic and Other Assumptions Used in the Agencies’ Analysis .......... 4-1
 Contents

iii

4.1 How the Agencies use the economic and other assumptions in their analyses ....... 4-1

4.2 What assumptions do the agencies use in the impact analyses? .............................. 4-2

4.2.1 The on-road fuel economy “gap” ..................................................................... 4-2

4.2.2 Fuel prices and the value of saving fuel ........................................................... 4-7

4.2.3 Vehicle Lifetimes and Survival Rates .............................................................. 4-9

4.2.4 VMT .................................................................................................................. 4-12

4.2.5 Accounting for the fuel economy rebound effect .......................................... 4-18

4.2.6 Benefits from additional driving .................................................................... 4-26

4.2.7 Added costs from increased vehicle use ......................................................... 4-26

4.2.8 Petroleum and energy security impacts ........................................................ 4-28

4.2.9 Air pollutant emissions .................................................................................... 4-39

4.2.10 Reductions in emissions of greenhouse gases ................................................ 4-48

4.2.11 Benefits due to reduced refueling time .......................................................... 4-53

4.2.12 Discounting future benefits and costs ............................................................ 4-54

4.2.13 Additional Costs of Vehicle Ownership ............................................................ 4-54

Chapter 5: Air Conditioning, Off-Cycle Credits, and Other Flexibilities ................... 5-1

5.1 Air conditioning technologies and credits.................................................................. 5-1

5.1.1 Overview ............................................................................................................. 5-1

5.1.2 Air Conditioner Leakage .................................................................................. 5-3

5.1.3 CO2 Emissions and Fuel Consumption due to Air Conditioners ................ 5-22

5.1.4 Air Conditioner System Costs ........................................................................ 5-58

5.2 Off-Cycle Technologies and Credits......................................................................... 5-62

5.2.1 Reducing or Offsetting Electrical Loads ....................................................... 5-64

5.2.2 Waste Heat Recovery ...................................................................................... 5-65

5.2.3 High Efficiency Exterior Lights ..................................................................... 5-69

5.2.4 Solar Panels ...................................................................................................... 5-73

5.2.5 Definitions for Electrical Load Offsetting and Reduction Technologies .... 5-81

5.2.6 Active Aerodynamic Improvements .............................................................. 5-81

5.2.7 Definition for Active Aerodynamic Improvements ...................................... 5-84

5.2.8 Advanced Load Reductions ............................................................................ 5-84

5.2.9 Thermal (and Solar) Control Technologies ................................................ 5-101
 Contents

iv

5.2.10 Glazing ............................................................................................................ 5-101

5.2.11 Active Seat Ventilation .................................................................................. 5-106

5.2.12 Solar Reflective Paint .................................................................................... 5-107

5.2.13 Passive and Active Cabin Ventilation .......................................................... 5-108

5.2.14 Summary of Thermal (and Solar) Control Credits .................................... 5-109

5.2.15 Definitions for Solar Control Credit Technologies ..................................... 5-110

5.2.16 Summary of Credits ...................................................................................... 5-110

5.3 Full-Size Pickup Truck Credits .............................................................................. 5-111

5.3.1 Full-Size Pick-up Truck Definition .............................................................. 5-112

5.3.2 Hybrid Pickup Truck Technology ............................................................... 5-113

5.3.3 Mild and Strong Hybrid Pickup Truck Definitions ................................... 5-113

5.3.4 Pickup Truck Performance Thresholds for Advanced Technology Credits . 5-

118
 Executive Summary

i 

Executive Summary 

The Environmental Protection Agency (EPA) and the National Highway Traffic 

Safety Administration (NHTSA) are issuing a joint final rule to establish new standards for 

light-duty highway vehicles that will reduce greenhouse gas emissions and improve fuel 

economy. This joint final rulemaking is consistent with the Presidential Memorandum issued 

by President Obama on May 21, 2010, requesting that NHTSA and EPA develop through 

notice and comment rulemaking a coordinated National Program to reduce greenhouse gas 

emissions and improve the fuel economy of light-duty vehicles for model years 2017-2025. 

This final rule, consistent with the President’s request, responds to the country’s critical need 

to address global climate change and to reduce oil consumption. EPA is regulating 

greenhouse gas emissions standards under the Clean Air Act, and NHTSA is regulating 

Corporate Average Fuel Economy standards under the Energy Policy and Conservation Act, 

as amended. These standards apply to passenger cars, light-duty trucks, and medium-duty 

passenger vehicles, covering model years 2017 through 2025. They require these vehicles to 

meet an estimated combined average emissions level of 163 grams of CO2 per mile in MY 

2025 under EPA’s GHG program, and 49.6 mpg in MY 2025 under NHTSA’s CAFE 

program and represent a harmonized and consistent national program (National Program). 

These standards are designed such that compliance can be achieved with a single national 

vehicle fleet whose emissions and fuel economy performance improves each year from 

MY2017 to 2025. This document describes the supporting technical analysis for areas of 

these joint rules which are consistent between the two agencies. 

NHTSA and EPA have coordinated closely to create a nationwide joint fuel economy 

and GHG program based on consistent compliance structures and technical assumptions. To 

the extent permitted under each Agency’s statutes, NHTSA and EPA have incorporated the 

same compliance flexibilities, such as averaging, banking, and trading of credits, off-cycle 

credits, and the same testing protocol for determining the agencies’ respective fleet-wide 

average final standards. In addition, the agencies have worked together to create a common 

baseline fleet and to harmonize most of the costs and benefit inputs used in the agencies’ 

respective modeling processes for this joint final rule. 

Chapter 1 of this joint TSD provides an explanation of the agencies’ methodology 

used to develop the baseline and reference case vehicle fleets, including the technology 

composition of these fleets, and how the agencies projected vehicle sales into the future. One 

of the fundamental features of this technical analysis is the development of these fleets, which 

are used by both agencies in their respective models. In order to determine technology costs 

associated with this joint rulemaking, it is necessary to consider the vehicle fleet absent a 

rulemaking as a “business as usual” comparison. In past CAFE rulemakings, NHTSA has 

used confidential product plans submitted by vehicle manufacturers to develop the reference 

case fleet. In responding to comments from these previous rulemakings that the agencies 

make these fleets available for public review, the agencies created a new methodology for 

creating baseline and reference fleets using data, the vast majority of which is publicly 

available. 
 Executive Summary

ii

 Chapter 2 of this document discusses how NHTSA and EPA developed the 

mathematical functions which provide the bases for the final car and truck standards. NHTSA 

and EPA worked together closely to develop regulatory approaches that are fundamentally the 

same, and have chosen to use an attribute-based program structure based on the footprint 

attribute, similar to the mathematical functions used in the MYs 2012-2016 rule. The 

agencies revisited other attributes as candidates for the standard functions, but concluded that 

footprint remains the best option for balancing the numerous technical and social factors. 

However, the agencies did adjust the shape of the truck footprint curve, in comparison to the 

MYs 2012-2016 rule. The agencies also modified the way the car and truck curves change 

from year to year compared to the MYs 2012-2016 rule. In determining the shape of the 

footprint curve, the agencies considered factors such as the magnitudes of CO2 reduction and 

fuel savings, how much that shape may incentivize manufacturers to comply in a manner 

which circumvents the overall goals of the joint program, whether the standards’ stringencies 

are technically attainable, the utility of vehicles, and the mathematical flexibilities inherent to 

the statistical fitting of such a function. 

Chapter 3 contains a detailed analysis of NHTSA and EPA’s technology assumptions 

on which the final regulations were based. Because the majority of technologies that reduce 

GHG emissions and improve fuel economy are identical, it was crucial that NHTSA and EPA 

use common assumptions for values pertaining to technology availability, cost, and 

effectiveness. The agencies collaborated closely in determining which technologies would be 

considered in the rulemaking, how much these technologies would cost the manufacturers 

(directly) in the time frame of the final rule, how these costs will be adjusted for learning as 

well as for indirect cost multipliers, and how effective the technologies are at accomplishing 

the goals of improving fuel efficiency and GHG emissions. 

Chapter 4 of this document provides a full description and analysis of the economic 

factors considered in this joint final rule. EPA and NHTSA harmonized many inputs 

capturing economic and social factors, such as the discount rates, fuel prices, social costs of 

carbon, the magnitude of the rebound effect, the value of refueling time, and the social cost of 

importing oil and fuel. 

Chapter 5 of this joint TSD discusses adjustments and credits to reflect technologies 

that improve air conditioner efficiency, that improve efficiency under other off-cycle driving 

conditions, and that reduce leakage of air conditioner refrigerants that contribute to global 

warming. The air conditioner credits are similar to the MYs 2012-2016 rule, with two notable 

exceptions: NHTSA is allowing A/C efficiency improvements to help come into compliance 

with fuel economy standards, and a new air conditioner test procedure is introduced to help 

capture efficiency credits. NHTSA is now also allowing off-cycle improvements to help 

manufacturers come into compliance with fuel economy standards. A list of some 

technologies and their credits and a streamlined methodology is provided by the agencies to 

help simplify the credit generating process. Chapter 5 also discusses adjustments to 

encourage “game changing” technologies (such as hybridized powertrains) for full-size 

pickup trucks. 
 The Baseline and Reference Vehicle Fleets

 1-1 

Chapter 1: The Baseline and Reference Vehicle Fleets 

The passenger cars and light trucks sold currently in the United States, and those that 

are anticipated to be sold in the MYs 2017-2025 timeframe, are highly varied and satisfy a 

wide range of consumer needs. From two-seater miniature cars to 11-seater passenger vans to 

large extended cab pickup trucks, American consumers have a great number of vehicle 

options to accommodate their needs and preferences. Recent volatility in oil prices and the 

state of the economy have demonstrated that consumer demand and choice of vehicles within 

this wide range can be sensitive to these factors. Although it is impossible to precisely predict 

the future, the agencies need to characterize and quantify the future fleet in order to assess the 

impacts of rules that would affect that future fleet. The agencies have examined various 

publicly-available sources, and then used inputs from those sources in a series of models to 

project the composition of baseline and reference fleets for purposes of this analysis. This 

chapter describes this process, and the characteristics of each of the two baseline and 

reference fleets. 

The agencies have made every effort to make this analysis transparent and duplicable. 

Because both the input and output sheets from our modeling are public,1

stakeholders can 

verify and check NHTSA’s and EPA’s modeling results, and perform their own analyses with 

these datasets. 

1.1 Why do the agencies establish baseline and reference vehicle fleets? 

In order to calculate the impacts of the final GHG and CAFE standards, it is necessary to 

estimate the composition of the future vehicle fleet absent the new standards. EPA and 

NHTSA have developed a baseline/reference fleet in two parts. The first step was to develop 

a “baseline” fleet. The agencies create a baseline fleet in order to track the volumes and types 

of fuel economy-improving and CO2-reducing technologies that are already present in the 

existing vehicle fleet. Creating a baseline fleet helps to keep, to some extent, the agencies’ 

models from adding technologies to vehicles that already have these technologies, which 

would result in “double counting” of technologies’ costs and benefits. The second step was to 

project the baseline fleet sales into MYs 2017-2025. This is called the “reference” fleet, and 

it represents the fleet volumes (but, until later steps, not additional levels of technology) that 

the agencies believe would exist in MYs 2017-2025 absent any change due to regulation in 

2017-2025. 

After determining the reference fleet, a third step is needed to account for technologies 

(and corresponding increases in cost and reductions in fuel consumption and CO2 emissions) 

that could be added to the baseline technology vehicles in the future, taking into account 

previously-promulgated standards, and assuming MY 2016 standards apply at the same levels 

through MY 2025. This step uses the OMEGA and CAFE models to add technologies to 

vehicles in each of the baseline market forecasts such that each manufacturer’s car and truck 

CAFE and average CO2 levels reflect MY 2016 standards. The models’ output, the 
 The Baseline and Reference Vehicle Fleets

 1-2 

“reference case”, is the light-duty fleet estimated to exist in MYs 2017-2025 without new 

GHG/CAFE standards. All of the agencies’ estimates of emission reductions/fuel economy 

improvements, costs, and societal impacts for purposes of this final rulemaking (FRM) are 

developed in relation to the agencies’ reference cases. This chapter describes the first two 

steps of the development of the baseline and reference fleets. The third step of technology 

addition is developed separately by each agency as the outputs of the OMEGA and CAFE 

models (see Chapter 3 of the TSD for an explanation of how the models apply technologies to 

vehicles in order to evaluate potential paths to compliance). 

1.2 The 2008 and 2010 based vehicle fleet projections 

1.2.1 Why did the agencies develop two fleet projections for the final rule? 

Although much of the discussion in this and following sections describes the 

methodology for creating a single baseline and reference fleet, for this final rule the agencies 

actually developed two baseline and reference fleets. In the NPRM, the agencies used 2008 

MY CAFE certification data to establish the “2008-based fleet projection.” a

 The agencies 

noted that MY 2009 CAFE certification data was not likely to be representative since it was 

so dramatically influenced by the economic recession (Joint Draft TSD section 1.2.1). The 

agencies further noted that MY 2010 CAFE certification data might be available for use in the 

final rulemaking for purposes of developing a baseline fleet (id.). The agencies also stated 

that a copy of the MY 2010 CAFE certification data would be put in the public docket if it 

became available during the comment period. The MY 2010 data was reported by the 

manufacturers throughout calendar year 2011 as the final sales figures were compiled and 

submitted to the EPA database. Due to the lateness of the CAFE data submissionsb, it was not 

possible to submit the new 2010 data into the docket during the public comment period. As 

explained below, however, consistent with the agencies’ expectations at proposal, and with 

the agencies’ standard practice of updating relevant information as practicable between 

proposals and final rules, the agencies are using these data in one of the two fleet-based 

projections we are using to estimate the impacts of the final rules. 

For analysis supporting the NPRM, the agencies developed a forecast of the light 

vehicle market through MY 2025 based on (a) the vehicle models in the MY 2008 CAFE 

certification data, (b) the AEO2011 interim projection of future fleet sales volumes, and (c) 

the future fleet forecast conducted by CSM in 2009. In the proposal, the agencies stated we 

planned to use MY 2010 CAFE certification data, if available, for analysis supporting the 

final rule (Joint Draft TSD, p. 1-2). The agencies also indicated our intention to, for analysis 

 

a

 2008 based fleet projection is a new term that is the same as the reference fleet. The term is added to clarify 

when we are using the 2008 baseline and reference fleet vs. the 2010 baseline and reference fleet. 

b

 Partly due to the earthquake and tsunami in Japan and the significant impact this had on their facilities, some 

manufacturers requested and were granted an extension on the deadline to submit their CAFE data. 
 The Baseline and Reference Vehicle Fleets

 1-3 

supporting the final rule, use the most recent version of EIA’s AEO, and a market forecast 

updated relative to that purchased from CSM (Joint Draft TSD section 1.3.5). 

For this final rulemaking, the agencies have analyzed the costs and benefits of the 

standards using two different forecasts of the light vehicle fleet through MY 2025. The 

agencies have concluded that the significant uncertainty associated with forecasting sales 

volumes, vehicle technologies, fuel prices, consumer demand, and so forth out to MY 2025, 

makes it reasonable and appropriate to evaluate the impacts of the final CAFE and GHG 

standards using two baselines. One market forecast, similar to the one used for the NPRM, 

uses corrected data regarding the MY2008 fleet, information from AEO 2011, and 

information purchased from CSM. The agencies received comments regarding the market 

forecast used in the NPRM suggesting that updates in several respects could be helpful to the 

agencies’ analysis of final standards; given those comments and since the agencies were 

already planning to produce an updated market forecast, the final rule also contains another 

market forecast using MY 2010 CAFE certification data, information from AEO 2012, and 

information purchased from LMC Automotive (formerly JD Power Forecasting). 

The two market forecasts contain certain differences, although as will be discussed 

below, the differences are not significant enough to change the agencies’ decision as to the 

structure and stringency of the final standards. For example, MY 2008 certification data 

represents the most recent model year for which the industry’s offerings were not strongly 

affected by the subsequent economic recession, which may make it reasonable to use if we 

believe that the future vehicle model offerings are more likely to be reflective of pre-recession 

offerings than models produced after MY 2008 (e.g., in MY 2010). Also, the MY 2010-based 

fleet projection employs a future fleet forecast provided by LMC Automotive, which is more 

current than the projection provided by CSM in 2009. However, the CSM forecast, utilized 

for the MY2008-based fleet projection, was influenced by the recession, particularly in 

predicting major declines in market share for some manufacturers (e.g., Chrysler) which the 

agencies do not believe are reasonably reflective of future trends. 

The MY 2010 based fleet projection, which is used in EPA’s alternative analysis and 

in NHTSA’s co-analysis, employs a future fleet forecast provided by LMC Automotive, 

which is more current than the projection provided by CSM in 2009, and which reflects the 

post-proposal MY 2010 CAFE certification data. However, this MY 2010 CAFE data also 

shows strong effects of the economic recession. For example, industry-wide sales were down 

by 20% compared to pre-recession MY 2008 levels. For some companies like Chrysler, 

Mitsubishi, and Subaru, sales were down by 30-40% from MY 2008 levels.c For BMW, 

General Motors, Jaguar/Land Rover, Porsche, and Suzuki, sales were down more than 40% 

from MY 2008 levels.d

Employing the MY 2008 vehicle data avoids using these baseline 

 

c

 These figure are arrived at using Table 1-17 and Table 1-39. 
 The Baseline and Reference Vehicle Fleets

 1-4 

market shifts when projecting the future fleet. On the other hand, it also perpetuates vehicle 

brands and models (and thus, their outdated fuel economy levels and engineering 

characteristics) that have since been discontinued. The MY 2010 CAFE certification data 

accounts for the phase-out of some brands (e.g., Saab, Pontiac, Hummer)eand the 

introduction of some technologies (e.g., Ford’s Ecoboost engine), which may be more 

reflective of the future fleet in this respect. 

Thus, given the volume of information that goes into creating a baseline forecast and 

given the significant uncertainty in any projection out to MY 2025, the agencies think that a 

reasonable way to illustrate the possible impacts of that uncertainty for purposes of this 

rulemaking is the approach taken here of analyzing the effects of the final standards under 

both the MY 2008-based baseline and the MY 2010-based baseline. The agencies’ analyses 

are presented in our respective RIAs and preamble sections. 

1.3 The 2008 Based Fleet Projection 

 Differences between the 2008 MY based fleet used in the final rule compared to that 

used in the NPRM include minor corrections to some of the vehicle footprint data, and minor 

corrections to technology “overrides” and technology class assignments used in DOT’s 

modeling system. A discussion of the changes is in the section below along with a thorough 

description of how the projection was created. 

1.3.1 On what data is the MY2008 baseline vehicle fleet based? 

As part of the CAFE program, EPA measures vehicle CO2 emissions and converts 

them to mpg, and generates and maintains the federal fuel economy database. See 49 U.S.C 

32904 and 40 CFR Part 600. Most of the information about the vehicles that make up the 

2008 fleet was gathered from EPA’s emission certification and fuel economy database, most 

of which is available to the public. These data (by individual vehicle model produced in MY 

2008) include: vehicle production volume, fuel economy rating for CAFE certification (i.e., 

on the 2-cycle city-highway test), carbon dioxide emissions (equivalent to fuel economy 

rating for CAFE certification), fuel type (gasoline, diesel, and/or alternative fuel), number of 

engine cylinders, displacement, valves per cylinder, engine cycle, transmission type, drive 

(rear-wheel, all-wheel, etc.), hybrid type (if applicable), and engine aspiration (naturallyaspirated, turbocharged, etc.). In addition to this information about each vehicle model 

produced in MY 2008, the agencies need additional information about the fuel economyimproving/CO2-reducing technologies already on those vehicle models in order to assess how 

much and which technologies to apply to determine a path toward future compliance. 

However, EPA’s certification database does not include a detailed description of the types of 

technologies considered in this FRM because this level of information was not reported in 

 

e

 Based on our review of the CAFE certification data, the MY 2010-based fleet contains no Saabs, and compared 

to the MY 2008-based fleet, about 90% fewer Hummers and about 75% fewer Pontiacs. 
 The Baseline and Reference Vehicle Fleets

 1-5 

MY 2008 for emission certification or fuel economy testing. Thus, the agencies augmented 

this description with publicly-available data which includes more complete technology 

descriptions from Ward’s Automotive Group.f,g The agencies also required information about 

the footprints of MY 2008 vehicles in order to generate potential target footprint curves (as 

discussed in Chapter 2 of the TSD). In a few instances when relevant vehicle information 

(such as vehicle track width for footprint) was not available from these two sources, the 

agencies obtained this information principally from publicly-accessible internet sites such as 

Motortrend.com or Edmunds.com, and occasionally from other sources (such as articles about 

specific vehicles revealed from internet search engine research).h,i

Between the NPRM and the final rule, the agencies found discrepancies in footprint 

values for a number of vehicles in the MY 2008 CAFE certification data. Specifically, 

contractors to DOT employed to develop a market share model for incorporation into the 

CAFE model noted that out of 1,302 vehicles in the MY 2008-based input file used in the 

agencies’ NPRM analysis, in 554 cases, the wheelbase value in the CAFE certification data 

did not match wheelbase data from Ward’s Automotive that the contractor had obtained 

separately. While wheelbase is not a direct input to the models used in developing the 

standards, it is a component of footprint, which is a key input in the modeling process. 

Of the reported differences, 287 (51.8%) were less than or equal to 0.1 inch, and 115 

(20.8%) were greater than 0.1 inch but less than or equal to 0.5 inch. The former set of 

differences is most likely attributable to differences in the number of significant digits in the 

reported raw data. The latter set of differences may also be due to reporting differences or 

actual measurement differences, but would not have a significant impact on the computed 

footprint value, all other things being equal. These differences were not considered further. 

Of the remaining differences, 14 (2.5%) were greater than 0.5 inch but less than 1 

inch. Most significantly, 138 (24.9%) of the differences were greater than 1 inch, ranging in 

value from 1.1 inch to 23.8 inches. 

To verify these findings, the Ward’s data used by the contractor on wheelbase for the 

152 vehicles with a discrepancy greater than 0.5 inches were compared to wheelbase data 

from Edmunds, cars.com, Motor Trend, and product plans where available, and values 

reflecting the agencies’ best judgment about actual average values was selected. 

Footprint for the 152 vehicles was thus recalculated based on corrected wheelbase. In 

the process of validating the wheelbase data, the agencies noted that there were many 

 

f

 WardsAuto.com: Used as a source for engine specifications shown in Table 1-1. 

g

 Note that WardsAuto.com, where this information was obtained, is a fee-based service, but all information is 

public to subscribers. 

h

 Motortrend.com and Edmunds.com: Used as a source for footprint and vehicle weight data. 

i

 Motortrend.com and Edmunds.com are free, no-fee internet sites. 
 The Baseline and Reference Vehicle Fleets

 1-6 

discrepancies in the track width values, which the agencies also corrected in the calculation of 

the corrected footprints. 

The affected vehicles included those of the following manufacturers: 

Chrysler – 4 (2 large SUV, 2 small SUV) 

Daimler – 19 (1 compact auto, 15 large auto, 1 midsize auto, 2 subcompact auto) 

Ford – 4 (2 large pickup, 2 small pickup) 

General Motors – 29 (18 compact auto, 7 midsize auto, 4 subcompact auto) 

Honda – 17 (3 compact auto, 2 large SUV, 8 midsize auto, 1 small pickup, 3 subcompact auto) 

Hyundai – 2 (2 subcompact auto) 

Kia – 8 (2 compact auto, 4 midsize auto, 2 subcompact auto) 

Mazda – 7 (4 midsize SUV, 2 small pickup, 1subcompact auto) 

Nissan – 11 (4 compact auto, 6 large auto, 1 minivan) 

Subaru – 15 (6 midsize auto, 9 midsize SUV) 

Tata – 2 (2 midsize auto) 

Toyota – 29 (3 compact auto, 6 large pickup, 16 large auto, 4 midsize auto) 

Volkswagen – 5 (4 large auto, 1 midsize auto) 

Table 1-1 shows the change from the NPRM to the FRM in the average footprint for 

all vehicles, cars, and trucks. The average change in footprint was very small, although quite 

a few vehicles’ footprints were updated. 

Table 1-1 2008 MY Footprint changes (Final Rule Values – NPRM Values) 

The baseline vehicle fleet for the analysis informing these final rules is the same 

except for the footprint changes to the baseline vehicle fleet used in the MYs 2012-2016 

rulemaking, and like that baseline, is comprised of publicly-available data to the largest extent 

possible. Some of the technology data included in the MYs 2012-2016 analysis’ baseline 

fleet was based on confidential product plan information about MY 2008 vehicles, 

specifically, data about which vehicles already have low friction lubricants, electric power 

steering, improved accessories, and low rolling resistance tires applied, the agencies no longer 

consider that information as needing to be withheld, because by now all MY 2008 vehicle 

models are already in the on-road fleet. As a result, the agencies are able to make public the 

exact baseline used in this rulemaking analysis. 

As explained in the MYs 2012-2016 TSD, creating the 2008 baseline fleet Excel file 

was an extremely labor-intensive process. EPA in consultation with NHTSA first considered 

using EPA’s CAFE certification data, which contains most of the required information. 

However, since the deadline for manufacturers to report this data did not allow enough time, 

Average Footprint of all Vehicles Average Footprint Cars Average Footprint Trucks

-0.1 -0.2 0
 The Baseline and Reference Vehicle Fleets

 1-7 

in the MYs 2012-2016 rulemaking, for early modeling review, the agencies began to create 

the baseline fleet file using an alternative data source. 

The agencies ultimately relied on a combination of EPA’s vehicle emissions 

certification data, data from a paid subscription to Ward’s Automotive Group, and CAFE 

certification data. EPA’s vehicle emissions certification data contains much of the 

information required for creating a baseline fleet file, but it lacked the production volumes 

that are necessary for the OMEGA and Volpe models, and also contains some vehicle models 

that manufacturers certified but did not produce in MY 2008. The data from Ward’s 

contained production volumes (which were not ultimately used, because they did not have 

volumes for individual vehicles down to the resolution of the specific engine and transmission 

level) and vehicle specifications, and eliminated extraneous vehicles. 

The EPA vehicle emissions certification dataset came in two parts, an engine file and a 

vehicle file, which the agencies combined into one spreadsheet using their common index. 

The more-specific Ward’s data also came in two parts, an engine file and a vehicle file, and 

also required mapping, which was more difficult than combining the EPA vehicle emissions 

certification dataset files because there was no common index between the Ward’s files. A 

new index was implanted in the engine file and a search equation in the vehicle file, which 

identified most of the vehicle and engine combinations. Each vehicle and engine combination 

was reviewed and corrections were made manually when the search routine failed to give the 

correct engine and vehicle combination. The combined Ward’s data was then mapped to the 

EPA vehicle emissions certification data by creating a new index in the combined Ward’s 

data and using the same process that was used to combine the Ward’s engine and vehicle files. 

In the next step, CAFE certification data had to be merged in order to fill out the 

needed production volumes. NHTSA and EPA reviewed the CAFE certification data for MY 

2008 as it became available in the MYs 2012-2016 rulemaking. The CAFE certification set 

could have been used with the Ward’s data without the EPA vehicle emission certification 

data set, but was instead appended to the combined Ward’s and EPA vehicle emission 

certification dataset. That combined dataset was then mapped into the CAFE dataset using 

the same Excel mapping technique described above. Finally EPA and NHTSA obtained the 

remaining attribute and technology data, such as footprint, curb weight, and others (for a 

complete list of data with sources see Table 1-2 below) from other sources, thus completing 

the baseline dataset. 

Another step that was done for the first time in the NPRM (and used in this FRM 

baseline as well) was to disaggregate the footprints of pickup trucks. In the MYs 2012-2016 

rulemaking the agencies aggregated full-size pickup data in the baseline by using average 

values to represent all variants of a given pickup line. While full-size pickups might be 

offered with various combinations of cab style (e.g., regular, extended, crew) and box length 

(e.g., 5 1⁄2’, 6 1⁄2’, 8’), and therefore multiple footprint sizes, CAFE compliance data for MY 

2008 did not contain footprint information, and therefore could not reliably be used to identify 

which pickup entries correspond to footprint values estimable from public or commercial 

sources. Therefore, the agencies used the known production levels of average values to 
 The Baseline and Reference Vehicle Fleets

 1-8 

represent all variants of a given pickup line (e.g., all variants of the F-150, or all variants of 

the Sierra/Silverado) in order to calculate the sales-weighted average footprint/fuel economy 

value for each pickup family. In retrospect, this may have affected how we fit the light truck 

target curve, among other things, so the agencies have since created an expanded version of 

the fleet to account for the variation in footprint/wheelbase for the large pickups of Chrysler, 

Ford, GM, Nissan and Toyota. In MY 2008, large pickups were available from Nissan with 2, 

Chrysler and Toyota with 3, and Ford and GM with 5 wheelbase/footprint combinations. The 

agencies got this footprint data from MY 2008 product plans submitted by the various 

manufacturers, which can be made public at this time because by now all MY 2008 vehicle 

models are already in production, which makes footprint data about them essentially public 

information. 

The agencies created the expanded fleet by replicating original records from a single 

pickup footprint model into multiple pickup models with distinct footprint values, in order to 

reflect the additional pickup model footprints just noted. For example, an F-150 in the MY 

2008 baseline used in the MYs 2012-2016 rulemaking analysis with a footprint value of 67 

square feet, is disaggregated by replicating 2 times in all respects, except with footprint values 

of 58, 67, and 73 square feet. Sales volumes of these pickups from the original record were 

distributed to each of the “58 square feet” and “73 square feet” duplicates based on the 

distribution of MY 2008 sales by these pickups’ wheelbase/footprint, which the agencies took 

from product plan data submitted by the manufacturers in 2008/2009 in response to requests 

to support the MYs 2012-2016 rulemaking analysis. The agencies were able to distribute the 

sales for each of the original pickups by wheelbase/footprint by matching each of the pickups 

in the baseline fleet with pickups in the product plans on the basis of drive type, transmission 

type, and engine displacement, cylinders/configuration and HP, and then sorting and summing 

the sales of the matched pickups in the product plans by wheelbase/footprint. 

Both agencies used this fleet forecast to populate input files for the agencies’ 

respective modeling systems. The structure of the market forecast input file used for the 

Volpe model is described the model documentation.2 To help readers who wish to directly 

examine the baseline fleet file for EPA’s OMEGA model, and to provide some idea of its 

contents for those readers who do not, Table 1-2 shows the columns of the complete fleet file, 

which includes the MY 2008 baseline data that was compiled. Each column has its name, 

definition (description) and source. Most elements shown in Table 1-2 also appear in the 

market forecast input file for DOT’s modeling system, which also accommodates some 

additional data elements discussed in the model documentation. 

Table 1-2 2008 MY Data, Definitions, and Sources 

Data Item Definition Where The Data is From

Index Index Used to link EPA and NHTSA baselines Created

Manufacturer

Common name of company that manufactured 

vehicle. May include more name plates than 

Cert Manufacturer Name. Certification data

CERT 

Manufacturer Name

Certification name of company that 

manufactured vehicle Certification data

Name Plate Name of Division Certification data
 The Baseline and Reference Vehicle Fleets

 1-9 

Model Name of Vehicle Certification data

Reg Class EPA Fuel Economy Class Name Certification data

Our Class

If a car’s Footprint<43 then "SubCmpctAuto"

If a car’s 43<=Footprint<46 then 

"CompactAuto"

If a car’s 46<=Footprint<53 then 

"MidSizeAuto”

If a car’s Footprint >=53 then “LargeAuto”

If a S.U.V.’s Footprint < 43 then “SmallSuv”

If a S.U.V.’s 43<=Footprint<46 then 

"MidSizeSuv”

If a S.U.V’s Footprint >=46 then “LargeSuv”

If a Truck’s Footprint < 50 then “SmallPickup”

If a Truck’s Footprint>=50 then “LargPickup”

If a Van’s Structure is Ladder then 

“CargoVan”

If a Van’s Structure is Unibody then 

“Minivan” Derived From Certification data and Footprint

CSM Class

CSM Worldwide’s class for the vehicle. Used 

to weight vehicles based on CSM data. CSM Worldwide

Vehicle Type 

Number

Vehicle Type Number assigned to a vehicle 

based on its number of cylinders, valves per 

cylinder, and valve actuation technology Defined by EPA staff

Vehicle Index From 

Sum Page

Number to be used as a cross reference with 

the Sum Pages. NA

Traditional 

Car/Truck Traditional Car Truck value for reference. Certification data

NHTSA Defined 

New Car/Truck

New NHTSA Car Truck value as defined in 

2011 Fuel economy regulations. Used in 

calculations. NHTSA

Total Production 

Volume

Total number of vehicles produced for that 

model. Certification data

Fuel Econ.

(mpg) EPA Unadjusted Fuel Economy Certification data

CO2

CO2 calculated from MPG. CO2 weighted 

1.15 times higher for diesel vehicles. Certification data

Area (sf) Average Track x Wheelbase Calculated from track width and wheel base

Fuel Gas or Diesel Wards

Fuel Type Gas or Diesel or Electric Certification data

Disp

(lit.) Engine Cylinder Displacement Size in Liters Wards/Certification data

Effective Cyl

Number of Cylinder + 2 if the engine has a 

turbo or super charger. Derived From Certification data.

Actual Cylinders Actual Number of Engine Cylinders Certification data

Valves Per Cylinder Number of Valves Per Actual Cylinder Certification data

Valve Type Type of valve actuation. Wards (Note: Type E is from Cert Data)

Valve Actuation

Type of valve actuation with values compatible 

with the package file. Wards

VVT

Type of valve timing with values compatible 

with the package file. Wards

VVLT

Type of valve lift with values compatible with 

the package file. Wards

Deac

Cylinder Deactivation with a value that is 

compatible with the package file. Wards

Fuel injection 

system Type of fuel injection. Wards

Boost Type of Boost if any. Wards

Engine Cycle As Defined by EPA Cert. Definition Wards
 The Baseline and Reference Vehicle Fleets

 1-10 

Horsepower Max. Horsepower of the Engine Wards

Torque Max. Torque of the Engine Wards

Trans Type

A=Auto AMT=Automated Manual M=Manual 

CVT= Continuously Variable Transmission Certification data

Trans Type Code with number of Gears Certification data

Num of Gears Number of Gears Certification data

Transmission

Transmission definition. Matches the cost 

definition. Certification data

Structure Ladder or Unibody General Internet Searches

Drive Fwd, Rwd, 4wd Certification data

Drive with AWD Fwd, Rwd, Awd, 4wd Certification data

Wheelbase Length of Wheelbase

Some from Edmunds.com or Motortrend.com, 

Others from product plans with a subset verified 

with Edmunds.com or Motortrend.com for 

accuracy.

Track Width

(front) Length of Track Width in inches

Some from Edmunds.com or Motortrend.com, 

Others from product plans with a subset verified 

with Edmunds.com or Motortrend.com for 

accuracy.

Track Width

(rear) Length of Track Width in inches

Some from Edmunds.com or Motortrend.com, 

Others from product plans with a subset verified 

with Edmunds.com or Motortrend.com for 

accuracy.

Footprint: PU 

Average

Car and Large Truck Footprints are normal 

(Average Track x Wheelbase). Medium and 

Small Truck footprints are the production 

weighted average for each vehicle.

Derived from data from Edmunds.com or 

Motortrend.com. Production volumes or specific 

footprints from product plans.

Threshold FootPrint

Footprint valve that will be set to 41 for values 

less than 41, Will be set to 56 for car values > 

56, and will be set to 74 for truck values >74

Derived from data from Edmunds.com or 

Motortrend.com. Production volumes or specific 

footprints from product plans.

Curb

Weight Curb Weight of the Vehicle

Some from Edmunds.com or Motortrend.com, 

Others from product plans with a subset verified 

with Edmunds.com or Motortrend.com for 

accuracy.

GVWR Gross Vehicle Weight Rating of the Vehicle

Some from Edmunds.com or Motortrend.com, 

Others from product plans with a subset verified 

with Edmunds.com or Motortrend.com for 

accuracy.

StopStart/Hybrid/Full

EV Type of Electrification if any. Blank = None Certification data

Import Car Cars Imported Certification data

Towing Capacity 

(Maximum) Weight a vehicle is rated to tow. Volpe Input File

Engine Oil

Viscosity

Ratio between the applied shear stress and the 

rate of shear, which measures the resistance of 

flow of the engine oil (as per SAE Glossary of 

Automotive Terms) Volpe Input File

Volume 2009 Projected Production Volume for 2009

Calculated based on 2008 volume and Annual 

Energy Outlook and CSM adjustment factors.

Volume 2010 Projected Production Volume for 2010

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2011 Projected Production Volume for 2011

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2012 Projected Production Volume for 2012

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2013 Projected Production Volume for 2013

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.
 The Baseline and Reference Vehicle Fleets

 1-11 

Volume 2014 Projected Production Volume for 2014

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2015 Projected Production Volume for 2015

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2016 Projected Production Volume for 2016

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2017 Projected Production Volume for 2017

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2018 Projected Production Volume for 2018

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2019 Projected Production Volume for 2019

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2020 Projected Production Volume for 2020

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2021 Projected Production Volume for 2021

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2022 Projected Production Volume for 2022

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2023 Projected Production Volume for 2023

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2024 Projected Production Volume for 2024

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Volume 2025 Projected Production Volume for 2025

Calculated based on 2008 volume and AEO and 

CSM adjustment factors.

Low drag brakes See Volpe Documentation Volpe Input File

Electric Power 

steering See Volpe Documentation Volpe Input File

Volpe Index

Number used to reorder the vehicles in the 

EPA baseline in the same order as the Volpe 

input file. Volpe Input File

Notes:

1. For engines not available in the WardsAuto.com 

engine file, an internet search was done to find this 

information.

2. These data were obtained from manufacturer’s product 

plans. They were used to block (where possible) the 

model from adding technology that was already on a 

vehicle.

3. Ward’s Automotive Group data obtained from "2008 

Light Vehicle Engines." 

DOT’s CAFE model also uses a series of inputs—referred to as “overrides”—to 

specify baseline technology content of specific vehicle models (and specific engines and 

transmissions) and to indicate cases where specific technologies are not applicable to specific 

vehicle models. In the MY 2008-based market forecast, DOT has corrected some of these 

settings to indicate that micro-hybrid technology (or more advanced hybrid) is already present 

on hybrid versions of the Altima, Aura, Civic, Camry, Escape, Highlander, Lexus GS and LS, 

Lexus RX, Mariner, Malibu, Prius, Tahoe, Tribute, Vue, and Yukon. The CAFE model also 

uses inputs to assign vehicles to specific “technology classes,” where technology-related 

inputs define the applicability, efficacy, and cost of each technology for vehicles in each 

technology class. In the MY 2008-based market forecast, DOT has reassigned the Altima 
 The Baseline and Reference Vehicle Fleets

 1-12 

(coupe), Audi A4, Corolla, Impala, Matrix, Passat, and Jetta to technology classes that better 

represent these vehicles’ size and performance characteristics. 

The sales volumes for the MY 2008 baseline fleet are included in the section below on 

reference fleet under the MY 2008 columns. Table 1-3 displays the engine technologies 

present in the baseline fleet. Again, the engine technologies for the vehicles manufactured by 

these manufacturers in MY 2008 were largely obtained from Ward’s Auto online. 

Table 1-3 2008 Engine Technology Percentages 

Manufacturer

Vehicle Type

Turbo Charged

Super Charged

Single Overhead Cam

Dual Overhead Cam

Overhead Cam

Variable Valve Timing 

Continuous

Variable Valve Timing 

Discrete

Variable Valve Timing 

Intake Only

Variable Valve Lift and 

Timing Continuous

Variable Valve Lift and 

Timing Discrete

Cylinder Deactivation

Direct Injection

All Both 3% 0% 20% 63% 17% 8% 22% 30% 0% 12% 6% 5%

All Cars 4% 0% 17% 73% 9% 9% 24% 35% 0% 13% 3% 7%

All Trucks 1% 0% 24% 48% 29% 6% 19% 23% 0% 10% 11% 3%

Aston Martin Cars 0% 0% 0% 100% 0% 0% 100% 0% 24% 0% 0% 0%

Aston Martin Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

BMW Cars 33% 1% 14% 86% 0% 14% 86% 0% 0% 13% 0% 33%

BMW Trucks 5% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 6%

Chrysler/Fiat Cars 1% 0% 21% 72% 8% 0% 42% 0% 0% 0% 5% 0%

Chrysler/Fiat Trucks 0% 0% 39% 4% 57% 0% 4% 0% 0% 0% 4% 0%

Daimler Cars 2% 0% 55% 45% 0% 72% 4% 13% 0% 0% 0% 2%

Daimler Trucks 16% 1% 36% 64% 0% 35% 17% 47% 0% 0% 0% 16%

Ferrari Cars 0% 0% 0% 100% 0% 0% 100% 0% 29% 0% 0% 0%

Ferrari Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Ford Cars 0% 1% 15% 85% 0% 4% 0% 47% 0% 0% 0% 0%

Ford Trucks 0% 0% 65% 32% 3% 28% 1% 9% 0% 0% 0% 0%

Geely/Volvo Trucks 0% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0%

Geely/Volvo Cars 49% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0%

GM Trucks 0% 0% 0% 31% 69% 5% 17% 14% 0% 0% 40% 0%

GM Cars 1% 0% 0% 56% 44% 29% 31% 1% 0% 0% 4% 6%

Honda Cars 0% 0% 57% 43% 0% 0% 27% 20% 0% 100% 11% 0%

Honda Trucks 4% 0% 64% 36% 0% 0% 4% 28% 0% 100% 0% 4%

Hyundai Cars 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Hyundai Trucks 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Kia Cars 0% 0% 0% 100% 0% 0% 0% 10% 0% 0% 0% 0%

Kia Trucks 0% 0% 0% 100% 0% 0% 0% 17% 0% 0% 0% 0%

Lotus Cars 0% 77% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0%

Lotus Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Mazda Cars 11% 0% 0% 99% 0% 0% 7% 92% 0% 0% 0% 11%
 The Baseline and Reference Vehicle Fleets

 1-13 

Mazda Trucks 24% 0% 1% 99% 0% 0% 13% 87% 0% 0% 0% 24%

Mitsubishi Cars 6% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0% 0%

Mitsubishi Trucks 0% 0% 100% 0% 0% 38% 0% 0% 0% 0% 0% 0%

Nissan Cars 0% 0% 0% 100% 0% 0% 4% 96% 0% 0% 0% 0%

Nissan Trucks 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Porsche Cars 17% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 17%

Porsche Trucks 12% 0% 0% 100% 0% 0% 100% 0% 0% 100% 0% 100%

Spyker/Saab Cars 100% 0% 0% 100% 0% 0% 17% 0% 0% 0% 0% 0%

Spyker/Saab Trucks 0% 0% 0% 62% 38% 0% 0% 62% 0% 0% 28% 0%

Subaru Cars 15% 0% 69% 31% 0% 0% 0% 31% 0% 1% 0% 0%

Subaru Trucks 3% 0% 70% 30% 0% 0% 23% 7% 0% 27% 0% 0%

Suzuki Cars 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0%

Suzuki Trucks 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0%

Tata/JLR Cars 0% 0% 0% 100% 0% 0% 76% 24% 0% 0% 0% 0%

Tata/JLR Trucks 0% 20% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Tesla Cars 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Tesla Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Toyota Cars 0% 0% 0% 100% 0% 0% 29% 71% 0% 0% 0% 8%

Toyota Trucks 0% 0% 0% 100% 0% 0% 61% 39% 0% 0% 0% 6%

Volkswagen Cars 43% 0% 85% 15% 0% 0% 48% 0% 0% 1% 0% 89%

Volkswagen Trucks 1% 0% 0% 100% 0% 0% 99% 0% 0% 79% 0% 100%

The data in Table 1-3 indicates that manufacturers had already begun implementing a 

number of fuel economy/GHG reduction technologies in the baseline (2008) fleet. For 

example, VW stands out as having a significant number of turbocharged direct injection 

engines, though it is uncertain whether their engines are also downsized. Some of the valve 

and cam technologies are quite common in the baseline fleet: for example, nearly half the 

baseline fleet already has dual cam phasing, while Honda and GM have considerable levels of 

engines with cylinder deactivation. Honda also has already implemented continuously 

variable valve lift on a majority of their engines. Part of the implication of these technologies 

already being present in the baseline is that if manufacturers have already implemented them, 

they are therefore not available in the rulemaking analysis for improving fuel economy and 

reducing CO2 emissions further, requiring the agencies to look toward increasing penetration 

of these and other technologies and increasingly advanced technologies to project continued 

improvements in stringency over time. 

The section below provides further detail on the conversion of the MY 2008 baseline 

into the MYs 2017-2025 reference fleet. It also describes more of the data contained in the 

baseline spreadsheet. 

1.3.2 The MY 2008 Based MY 2017-2025 Reference Fleet 

The reference fleet aims to reflect the current market conditions and expectations 

about conditions of the vehicle fleet during the model years to which the agencies’ rules 
 The Baseline and Reference Vehicle Fleets

 1-14 

apply. Fundamentally, constructing this fleet involved projecting the MY 2008 baseline fleet 

into the MYs 2017-2025 model years. It also included the assumption that none of the vehicle 

models had changes during this period. Projecting this future fleet is a process that is 

necessarily uncertain. NHTSA and EPA therefore relied on many sources of reputable 

information to make these projections. 

1.3.2.1 On what data is the reference vehicle fleet based (using the 2008 baseline)? 

For the MY 2008-based reference fleet, EPA and NHTSA have based the projection of 

total car and light truck sales on the 2011 projections made by the Energy Information 

Administration (EIA). EIA publishes a projection of national energy use annually called the 

Annual Energy Outlook (AEO).3

 EIA issued an early release version of AEO2012 January 

2012. The agencies are continuing to use this AEO data for the MY 2008 baseline consistent 

with the NPRM. EPA and NHTSA are employing the newer version of AEO in projecting the 

reference fleet for the 2010 MY based baseline and reference fleet projection as discussed in 

section 1.4.2.1.

As in the NPRM, the agencies used the Energy Information Administration’s (EIA’s) 

National Energy Modeling System (NEMS) to estimate the future relative market shares of 

passenger cars and light trucks. However, NEMS shifts the market toward passenger cars in 

order to ensure compliance with EISA’s requirement that CAFE standards cause the fleet to 

achieve 35 mpg by 2020. Because we use our market projection as a baseline relative to 

which we measure the effects of new standards, and we attempt to estimate the industry’s 

ability to comply with new standards without changing product mix (i.e., we analyze the 

effects of the final rules assuming manufacturers will not change fleet composition as a 

compliance strategy), using AEO 2011-projected shift in passenger car market share as 

provided by EIA would cause the agencies to understate the cost of achieving compliance 

through additional technology alone. Therefore, for analyses supporting today’s final rule, the 

agencies developed a new projection of passenger car and light truck sales shares by using 

NEMS to run scenarios from the AEO 2011 reference case, after first deactivating the abovementioned sales-volume shifting methodology and holding post-2017 CAFE standards 

constant at MY 2016 levels. Incorporating these changes reduced the projected passenger car 

share of the light vehicle market by an average of about 5% during 2017-2025. This case is 

referred to as the “Unforced Reference Case,” and the values are shown below in Table 1-5. 

Table 1-4 AEO 2011 Reference Case Volumes 

Model Year Cars Trucks Total Vehicles

2017 8,984,200 6,812,000 15,796,100

2018 8,998,200 6,552,200 15,550,400

2019 9,170,900 6,391,300 15,562,200

2020 9,553,600 6,336,200 15,889,800

2021 9,801,100 6,380,000 16,181,100

2022 10,056,600 6,384,600 16,441,200
 The Baseline and Reference Vehicle Fleets

 1-15 

2023 10,244,500 6,396,500 16,641,000

2024 10,483,400 6,407,700 16,891,100

2025 10,739,600 6,470,200 17,209,800

Table 1-5 AEO 2011 Interim Unforced Reference Case Volumes 

Model Year Cars Trucks Total Vehicles

2017 8,440,703 7,365,619 15,806,322

2018 8,376,192 7,200,218 15,576,410

2019 8,464,457 7,114,201 15,578,658

2020 8,725,709 7,170,230 15,895,939

2021 8,911,173 7,277,894 16,189,066

2022 9,123,436 7,316,337 16,439,772

2023 9,344,051 7,311,438 16,655,489

2024 9,580,693 7,353,394 16,934,087

2025 9,836,330 7,414,129 17,250,459

In this 2017 projection, car and light truck sales are expected to get up to 8.4 and 7.4 

million units, respectively. While the total level of sales of 15.8 million units is similar to 

pre-2008 levels, the fraction of car sales in 2017 and beyond is projected to be higher than in 

the 2000-2007 time frame. Note that EIA’s definition of cars and trucks follows that used by 

NHTSA prior to the MY 2011 CAFE final rule. The MY 2011 CAFE final rule reclassified 

approximately 1 million 2-wheel drive sport utility vehicles from the truck fleet to the car 

fleet. EIA’s sales projections of cars and trucks for the 2017-2025 model years under the old 

NHTSA truck definition are shown above in Table 1-4 and Table 1-5. 

In addition to a shift towards more car sales, sales of segments within both the car and 

truck markets have also been changing and are expected to continue to change in the future. 

Manufacturers are continuing to introduce more crossover models which offer much of the 

utility of SUVs but use more car-like designs and unibody structures. In order to reflect these 

changes in fleet makeup, EPA and NHTSA used a custom long range forecast purchased from 

CSM Worldwide (CSM). CSM jk

is a well-known industry analyst that provided the forecast 

used by the agencies for the 2012-2016 final rule. NHTSA and EPA decided to use the 

forecast from CSM in the MY 2008 baseline reference fleet for several reasons. One, CSM 

 

j

 CSM World Wide is a paid service provider. 

k

 As with any long range forecast, CSM World Wide’s forecast out to 2025 has uncertainties since many 

manufacturers do not have full future product plans out that far. 
 The Baseline and Reference Vehicle Fleets

 1-16 

uses a ground up approach (e.g., looking at the number of plants and capacity for specific 

engines, transmissions, and vehicles) for their forecast, which the agencies believe is a robust 

forecasting approachl. Two, CSM agreed to allow us to publish their high level data, on 

which the forecast is based, in the public domain. Three, the CSM forecast covered all the 

timeframe of greatest relevance to this analysis (2017-2025 model years). Four, it provided 

projections of vehicle sales both by manufacturer and by market segment. And five, it 

utilized market segments similar to those used in the EPA emission certification program and 

fuel economy guide, such that the agencies could include only the vehicle types covered by 

the final standards. The agencies note that CSM developed the forecast during a period when 

the United States economy was undergoing significant stress and some automobile 

manufacturers were experiencing a high degree of financial uncertainty. In the time since 

CSM developed its forecast ,industry sales and in particular the sales for some individual 

manufacturers have turned out differently than in the CSM forecast. Because forecasting the 

market out to MY 2025 has uncertainties, the agencies believe there are benefits from using 

the CSM forecast for one of the two analyses cases to reflect some level of uncertainty in the 

final rule analysis. It is feasible that the CSM forecast could represent what might happen in 

the future. 

CSM created a forecast that covered model years 2017-2025. Since the agencies used 

this forecast to generate the reference fleet (i.e., the fleet expected to be sold absent any 

increases in the stringency regulations after the 2016 model year), it is important for the 

forecast not to reflect changes in fleet composition during 2017-2025 attributed to CAFE/ 

GHG standards. However, CSM assumed that CAFE and GHG standards would continue to 

increase in stringency after 2016, although CSM did not use specific future standards as 

quantitative inputs to its model. In its quantitative analysis, CSM used fuel price, industry 

demand, consumer demand and other economic factors to project the composition of the 

future fleet. In response to question by the agencies, CSM indicated that their assumption of 

future standards had a negligible (non-discernible) impact on their forecast since it was not a 

direct quantitative input to the model such that CSM’s forecast would have been essentially 

the same had CSM assumed no stringency increases after 2016. 

The agencies combined the CSM forecast with data from other sources to create the 

reference fleet projections. This process is discussed in sections that follow. 

 

l

 There are other forecasting groups that do similar projections and meet all these criteria. LMC Automotive 

(formerly JD Power Forecasting) is another, and this was used for the alternate reference case projection as 

described below. 
 The Baseline and Reference Vehicle Fleets

 1-17 

1.3.2.2 How do the agencies develop the reference vehicle fleet? 

The process of producing the 2017-2025 reference fleet involved combining the 

baseline fleet with the projection data described above. This was a complex multistep 

procedure, which is described in this section. 

1.3.2.3 How was the 2008 baseline data merged with the CSM data? 

For the NPRM, EPA and NHTSA employed the same methodology as in the 2012-16 

rule for mapping certification vehicles to CSM vehicles; the results were used again for 

analysis supporting today’s final rule. Merging the 2008 baseline data with the 2017-2025 

CSM data required a thorough mapping of certification vehicles to CSM vehicles by 

individual make and model. One challenge that the agencies faced when determining a 

reference case fleet was that the sales data projected by CSM had different market 

segmentation than the data contained in EPA’s database. In order to create a common 

segmentation between the two databases, the agencies performed a side-by-side comparison 

of each vehicle model in both datasets, and created an additional “CSM segment” modifier in 

the spreadsheet to map the two datasets. The reference fleet sales based on the “CSM 

segmentation” was then projected. 

 The baseline data and reference fleet volumes are available to the public. The baseline 

Excel spreadsheet in the docket is the result of the merged files.4 The spreadsheet provides 

specific details on the sources and definitions for the data. The Excel file contains several 

tabs. They are: “Data”, “Data Tech Definitions”, “SUM”, “SUM Tech Definitions”, “Truck 

Vehicle Type Map”, and “Car Vehicle Type Map”. “Data” is the tab with the raw data. 

“Data Tech Definitions” is the tab where each column is defined and its data source named. 

“SUM” is the tab where the raw data is processed to be used in the OMEGA and Volpe 

models. The “SUM” tab minus columns A-F and minus the Generic vehicles is the input file 

for the models. The “Generic” manufacturer (shown in the “SUM” tab) is the sum of all 

manufacturers and is calculated as a reference, and for data verification purposes. It is used to 

validate the manufacturers’ totals. It also gives an overview of the fleet. 

Table 1-6 shows the sum of the models chosen. The number of models is determined 

by the number of unique segment and vehicle type combinations. These combinations of 

segment and vehicle type (the vehicle type number is the same as the technology package 

number) are determined by the technology packages discussed in the EPA RIA. “SUM Tech 

Definitions” is the tab where the columns of the “SUM” tab are defined. The “Truck Vehicle 

Type Map” and “Car Vehicle Type Map” map the number of cylinder and valve actuation 

technology to the “tech package” vehicle type number.

Table 1-6 Models from the SUM Tab Model 

Model

Car Like LargeSuv >=V8 Vehicle Type: 13

Car Like LargeSuv V6 Vehicle Type: 16

Car Like LargeSuv V6 Vehicle Type: 12
 The Baseline and Reference Vehicle Fleets

 1-18 

Car Like LargeSuv V6 Vehicle Type: 9

Car Like LargeSuv I4 and I5 Vehicle Type: 7

Car Like MidSizeSuv V6 Vehicle Type: 8

Car Like MidSizeSuv V6 Vehicle Type: 5

Car Like MidSizeSuv I4 Vehicle Type: 7

Car Like SmallSuv V6 Vehicle Type: 12

Car Like SmallSuv V6 Vehicle Type: 4

Car Like SmallSuv I4 Vehicle Type: 3

LargeAuto >=V8 Vehicle Type: 13

LargeAuto >=V8 Vehicle Type: 10

LargeAuto >=V8 Vehicle Type: 6

LargeAuto V6 Vehicle Type: 12

LargeAuto V6 Vehicle Type: 5

MidSizeAuto >=V8 Vehicle Type: 13

MidSizeAuto >=V8 Vehicle Type: 10

MidSizeAuto >=V8 (7 or >) Vehicle Type: 6

MidSizeAuto V6 Vehicle Type: 12

MidSizeAuto V6 Vehicle Type: 8

MidSizeAuto V6 Vehicle Type: 5

MidSizeAuto I4 Vehicle Type: 3

In the combined EPA certification and CSM database, all 2008 vehicle models were 

assumed to continue out to 2025, though their volumes changed in proportion to CSM 

projections. Also, any new models expected to be introduced within the 2009-2025 

timeframe are not included in the data. These volumes are reassigned to the existing models 

to keep the overall fleet volume the same. All MYs 2017-2025 vehicles are mapped to the 

existing vehicles by a process of mapping to manufacturer market share and overall segment 

distribution. The mappings are discussed in the next section. Further discussion of this 

limitation is discussed below in section 1.3.2.4. The statistics of this fleet will be presented 

below since further modifications were required to the volumes as the next section describes. 

1.3.2.4 How were the CSM forecasts normalized to the AEO forecasts for the 2008- 

based fleet?

The next step in the agencies’ generation of the reference fleet is one of the more 

complicated steps to explain. Here, the projected CSM forecasts for relative sales of cars and 

trucks by manufacturer and by market segment was normalized (set equal) to the total sales 

estimates of the Early Release of the 2011 Annual Energy Outlook (AEO). NHTSA and EPA 

used projected car and truck volumes for this period from Early AEO 2011. However, the 

AEO projects sales only at the car and truck level, not at the manufacturer and model-specific 

level, and the agencies’ analysis requires this further level of detail. The CSM data provided 

year-by-year percentages of cars and trucks sold by each manufacturer as well as the 

percentages of each vehicle segment. Using these percentages normalized to the AEOprojected volumes then provided the manufacturer-specific market share and model-specific 

sales for model years 2017-2025 (it is worth clarifying that the agencies are not using the 
 The Baseline and Reference Vehicle Fleets

 1-19 

model-specific sales volumes from CSM, only the higher-level volumes by manufacturer and 

segment). This process is described in greater detail in the following paragraphs. 

In order to determine future production volumes, the agencies developed multipliers 

by manufacturer and vehicle segment that could be applied to MY 2008 volumes. The 

process for developing the multipliers is complicated, but is easiest to explain as a three-step 

process, though the first step is combined with both the second and third step, so only one 

multiplier per manufacturer and vehicle segment is developed. 

The three steps are: 

1. Adjust total car and truck sales to match AEO projections. 

2. Adjust car sales to match CSM market share projections for each manufacturer 

and car segment. 

3. Adjust truck sales to match CSM market share projections for each 

manufacturer and truck segment. 

The first step is the adjustment of total car and truck sales in 2008 to match AEO 

projections of total car and truck sales in 2017-2025. The volumes for all of the trucks in 

2008 were added up (TruckSum2008), and so were the volumes of all the cars (CarSum2008). 

A multiplier was developed to scale the volumes in 2008 to the AEO projections. The 

example equation below shows the general form of how to calculate a car or truck multiplier. 

The AEO projections are shown above in Table 1-4.

Example Equation :

TruckMultiplier(Year X) = AEOProjectionforTrucks(Year X) / TruckSum2008 

CarMultiplier(Year X) = AEOProjectionforCars(Year X) / CarSum2008 

Where: Year X is the model year of the multiplier. 

The AEO projection is different for each model year. Therefore, the multipliers are 

different for each model year. The multipliers can be applied to each 2008 vehicle as a first 

adjustment, but multipliers based solely on AEO have limited value since those multipliers 

can only give an adjustment that will give the correct total numbers of cars and trucks without 

the correct market share or vehicle mix. A correction factor based on the CSM data, which 

does contain market share and vehicle segment mix, is therefore necessary, so combining the 

AEO multiplier with CSM multipliers (one per manufacturer, segment, and model year) will 

give the best multipliers. 

There were several steps in developing an adjustment for Cars based on the CSM data. 

CSM provided data on the market share and vehicle segment distribution. The first step in 

determining the adjustment for Cars was to total the number of Cars in each vehicle segment 

by manufacturer in MY 2008. A total for all manufacturers in each segment was also 

calculated. The next step was to multiply the volume of each segment for each manufacturer 
 The Baseline and Reference Vehicle Fleets

 1-20 

by the CSM market share. The AEO multiplier was also applied at this time. This gave 

projected volumes with AEO total volumes and market share correction for Cars. This is 

shown in the “Adjusted for 2017AEO and Manufacturer Market Share” column of Table 1-7. 

The next step is to adjust the sales volumes for CSM vehicle segment distribution. 

The process for adjusting for vehicle segment is more complicated than a simple one step 

multiplication. In order to keep manufacturers’ volumes constant and still have the correct 

vehicle segment distribution, vehicles need to move from segment to segment while 

maintaining constant manufacturers’ totals. Six rules and one assumption were applied to 

accomplish the shift. The assumption (based on the shift in vehicle sales in 2008 and 2009) is 

that people are moving to smaller vehicles in the rulemaking time frame independently of 

regulatory requirements. A higher-level (less detailed) example of this procedure is provided 

in Section II of the preamble. 

Vehicles from CSM’s “Luxury Car,” “Specialty Car,” and “Other Car” segments, if 

reduced, will be equally distributed to the remaining four categories (“Full-Size Car,” “MidSize Car,” “Small Car,” “Mini Car”). If these sales increased, they were taken from the 

remaining four categories so that the relative sales in these four categories remained constant. 

Vehicles from CSM’s “Luxury Car,” “Specialty Car,” and “Other Car” segments, if 

increased will take equally from the remaining categories (“Full-Size Car,” “Mid-Size Car,” 

“Small Car,” “Mini Car”). 

All manufacturers have the same multiplier for a given segment shift based on moving 

all vehicles in that segment to achieve the CSM distribution. Table 1-7 shows how the 2017 

vehicles moved and the multipliers that were created for each adjustment. This does not mean 

that new vehicle segments will be added (except for Generic Mini Car described in the next 

step) to manufacturers that do not produce them. Vehicles within each manufacturer will be 

shifted as close to the distribution as possible given the other rules. Table 1-8 has the 

percentages of Cars per CSM segment. These percentages are multiplied by the total number 

of vehicles in a given year to get the total sales in the segment. Table 1-7 shows the totals for 

2017 in the “2017 AEO-CSM Sales Goal” column. 

When “Full-Size Car,” “Mid-Size Car,” “Small Car” are processed, if vehicles need to 

move in or out of the segment, they will move into or out of the next smaller segment. So, if 

Mid-Size Cars are being processed they can only move to or be taken from Small Cars. Note: 

In order to accomplish this, a “Generic Mini Car” segment was added to manufacturers who 

did not have a Mini (type) Car in production in 2008, but needed to shift down vehicles from 

the Small Car segment. 

The data must be processed in the following order: “Luxury Car,” “Specialty Car,” 

“Other Car,” “Full-Size Car,” “Mid-Size Car,” “Small Car.” The “Mini Car” does not need to 

be processed separately. By using this order, it works out that vehicles will always move 

toward the correct distribution. There are two exceptions, BMW and Porsche only have 
 The Baseline and Reference Vehicle Fleets

 1-21 

“Luxury Car,” “Specialty Car,” and “Other Car” vehicles, so their volumes were not changed 

or shifted since these rules did not apply to them. 

When an individual manufacturer multiplier is applied for a segment, the vehicles 

move to or from the appropriate segments as specified in the previous rules and as shown in 

Table 1-7. 

Table 1-7 2017 Model Year Volume Shift* 

CSM Segment

2008 MY 

Sales

Adjusted for 

2017 AEO and 

Manufacturer 

Market Share

Luxury, 

Specialty, 

Other 

Adjustment

Full Size 

Adjustment

Midsize 

Adjustment

Small Car 

Adjustment

2017 

AEOCSM 

Sales Goal

All Full-Size Car 829,896 830,832 818,226 347,034 347,034 347,034 347,034

All Luxury Car 1,048,341 1,408,104 1,423,691 1,423,691 1,423,691 1,423,691 1,423,691

All Mid-Size Car 2,103,108 2,500,723 2,475,267 2,946,459 2,431,715 2,431,715 2,431,715

All Mini Car 617,902 868,339 851,234 851,234 851,234 1,439,985 1,439,985

All Small Car 1,912,736 2,548,393 2,513,350 2,513,350 3,028,094 2,439,343 2,439,343

All Specialty Car 469,324 627,425 702,048 702,048 702,048 702,048 702,048

All Others 0 0 0 0 0 0 0

Number Vehicles that shift and Where

All Full-Size Car (12,606) (471,192) 0 0 

All Luxury Car 15,587 0 0 0 

All Mid-Size Car (25,456) 471,192 (514,744) 0 

All Mini Car (17,105) 0 0 588,751 

All Small Car (35,043) 0 514,744 (588,751)

All Specialty Car 74,623 0 0 0 

All Others 0 0 0 0 

Individual Manufacturer Multiplier

All Full-Size Car 0.42

All Luxury Car 0.973

All Mid-Size Car 0.97

All Mini Car 1.55

All Small Car 0.96

All Specialty Car 0.963

All Others 1
 The Baseline and Reference Vehicle Fleets

 1-22 

Table 1-8 CSM – Percent of Cars per Segment* 

CSM Segment 2017 2018 2019 2020 2021 2022 2023 2024 2025

Compact Car 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Full-Size Car 3.95% 3.56% 3.35% 4.10% 3.59% 3.03% 2.97% 2.46% 2.46%

Luxury Car 16.70% 16.87% 17.14% 17.23% 17.05% 17.02% 17.10% 17.40% 17.40%

Mid-Size Car 27.68% 27.77% 27.47% 26.94% 27.18% 27.82% 28.51% 28.11% 28.11%

Mini Car 15.33% 15.46% 15.45% 15.46% 15.59% 15.67% 15.47% 15.23% 15.23%

Small Car 27.77% 27.57% 27.74% 27.99% 28.29% 28.43% 28.18% 28.49% 28.49%

Specialty Car 8.56% 8.76% 8.84% 8.27% 8.29% 8.03% 7.77% 8.31% 8.31%

Others 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Mathematically, an individual manufacturer multiplier is calculated by making the 

segment the goal and dividing by the previous total for the segment (shown in Table 1-8). If 

the number is greater than 1, the vehicles are entering the segment, and if the number is less 

than 1, the vehicles are leaving the segment. So, for example, if Luxury Cars have an 

adjustment of 1.5, then for a specific manufacturer who has Luxury Cars, a multiplier of 1.5 is 

applied to its luxury car volume, and the total number of vehicles that shifted into the Luxury 

segment is subtracted from the remaining segments to maintain that company’s market share. 

On the other hand, if Large Cars have an adjustment of 0.7, then for a specific manufacturer 

who has Large Cars, a multiplier of 0.7 is applied to its Large Cars, and the total number of 

vehicles leaving that segment is transferred into that manufacturer’s Mid-Size Cars. 

After the vehicle volumes are shifted using the above rules, a total for each 

manufacturer and vehicle segment is maintained. The total for each manufacturer segment for 

a specific model year (e.g., 2017 General Motors Luxury Cars) divided by the MY 2008 total 

for that manufacturer segment (e.g., 2008 General Motors Luxury Cars) is the new multiplier 

used to determine the future vehicle volume for each vehicle model. This is done by taking 

the multiplier (which is for a specific manufacturer and segment) times the MY 2008 volume 

for the specific vehicle model (e.g., 2008 General Motors Luxury Car Cadillac CTS). This 

process is repeated for each model year (2017-2025). 

The method used to adjust CSM Trucks to the AEO market share was different than 

the method used for Cars. The process for Cars is different than Trucks because it is not 

possible to predict how vehicles would shift between segments based on current market 

trends. This is because of the added utility of some trucks that makes their sales more 

insensitive to factors like fuel price. Again, CSM provided data on the market share and 

vehicle segment distribution. The process for having the fleet match CSM’s market share and 

vehicle segment distribution was iterative. 

The following totals were determined: 
 The Baseline and Reference Vehicle Fleets

 1-23 

The total number of trucks for each manufacturer in 2008 model year. 

The total number of trucks in each truck segment in 2008 model year. 

The total number of truck in each segment for each manufacturer in 2008 model 

year. 

The total number of trucks for each manufacturer in a specific future model year 

based on the AEO and CSM data. This is the goal for market share. 

The total number of trucks in each truck segment in a specific future model year 

based on the AEO and CSM data. This is the goal for vehicle segment 

distribution. Table 1-9 has the percentages of Trucks per CSM segment. 

Table 1-9 CSM – Percent of Trucks per Segment 

CSM Segment 2017 2018 2019 2020 2021 2022 2023 2024 2025

Full-Size CUV 5.9% 6.3% 6.8% 7.5% 8.3% 8.8% 9.5% 9.2% 9.1%

Full-Size Pickup 16.8% 16.5% 15.9% 16.1% 15.4% 15.1% 14.3% 13.8% 13.5%

Full-Size SUV 1.9% 1.5% 1.3% 1.0% 0.9% 0.8% 0.5% 0.5% 0.6%

Full-Size Van 1.2% 1.2% 1.1% 1.4% 1.3% 1.3% 1.3% 1.2% 1.2%

Mid-Size CUV 18.0% 17.4% 17.6% 17.2% 16.9% 16.8% 16.8% 17.0% 17.0%

Mid-Size MAV 4.5% 4.6% 4.9% 5.4% 5.9% 6.2% 6.5% 7.1% 7.4%

Mid-Size Pickup 6.1% 6.1% 6.1% 5.6% 5.7% 5.7% 5.8% 5.9% 5.8%

Mid-Size SUV 4.1% 4.8% 4.8% 4.5% 4.7% 4.8% 4.8% 4.6% 4.6%

Mid-Size Van 11.6% 11.9% 11.9% 11.7% 11.6% 11.6% 11.6% 11.3% 11.3%

Small CUV 26.0% 25.9% 25.7% 25.6% 25.1% 24.9% 24.7% 25.3% 25.3%

Small MAV 2.5% 2.6% 2.8% 2.9% 3.0% 3.1% 3.1% 3.2% 3.2%

Small SUV 1.3% 1.2% 1.1% 1.2% 1.1% 1.1% 1.1% 1.0% 1.0%

To start, the agencies created two different types of tables. One table had each 

manufacturer with its total sales for 2008 (similar to Table 1-11). This table will have the 

goal for each manufacturer, and a column added for each iteration with the current total. The 

second table has a truck segment total by manufacturer. The second table starts out with a 

“Generic” manufacturer (Table 1-11) which is the table where the goal resides. Each 

manufacturer (BMW for example is shown in Table 1-12) is then listed below the “Generic” 

manufacturer. With each iteration, a new total is added for each segment that is calculated 

and added to the table. This is not shown in the tables below. The agencies then engaged in a 

process of first adjusting the numbers in the tables to the goal for market share distribution. 

This was followed by adjusting to the goal for vehicle segment distribution. Each time an 

adjustment was done a new column was added. An adjustment was done by creating a 

multiplier (either segment distribution-based or manufacturer distribution-based) and applying 

it to each vehicle segment total in the current iteration. A manufacturer-based multiplier is 

calculated by taking the goal total for a manufacturer and dividing by the current total 

(starting with 2008 model year volumes) for a manufacturer. A segment distribution-based 
 The Baseline and Reference Vehicle Fleets

 1-24 

multiplier is calculated by taking the goal distribution volumes in the Generic manufacturer 

set and dividing them by the current volume. Table 1-10, Table 1-11, and Table 1-12 below 

illustrates two iterations using BMW as an example. 

Table 1-10 Manufacturer Truck Totals 

2008 Model Year Sales Manufacturer Distribution 2017 Volume Goal Multiplier for Iteration 1

BMW 61,324 138.053 138,053/61324=2.25

Table 1-11 Segment Specific Truck Totals for All Manufacturers 

Manufacturer CSM Segment 2008 Model Year Sales

Segment Distribution 2017 

Volume Goal Multipliers

Generic** Full-Size Pickup 1,332,335 1,240,844 0.931

Generic Mid-Size Pickup 452,013 452,017 1.000

Generic Full-Size Van 33,384 85,381 2.558

Generic Mid-Size Van 719,529 855,022 1.188

Generic Mid-Size MAV 110,353 331,829 3.007

Generic Small MAV 231,265 186,637 0.807

Generic Full-Size SUV 559,160 138,821 0.248

Generic Mid-Size SUV 436,080 305,382 0.700

Generic Small SUV 196,424 94,657 0.482

Generic Full-Size CUV 264,717 433,683 1.638

Generic Mid-Size CUV 923,165 1,327,905 1.438

Generic Small CUV 1,612,029 1,913,439 1.187

** Generic means all manufacturers. 
 The Baseline and Reference Vehicle Fleets

 1-25 

Table 1-12 Segment Specific Truck Totals for BMW 

Manufacturer CSM Segment

2008 Model Year 

Sales

Iteration 1 Adjust for 

Market Share

Iteration 2 Adjust for Segment 

Distribution

BMW Full-Size Pickup

BMW Mid-Size Pickup

BMW Full-Size Van

BMW Mid-Size Van

BMW Mid-Size MAV 3,882 2.25*3,882=8,739 2.85*8,739=24,907

BMW Small MAV

BMW Full-Size SUV

BMW Mid-Size SUV

BMW Small SUV

BMW Full-Size CUV

BMW Mid-Size CUV 36,409 2.25*36,409=81,964 1.1*81,964=90,134

BMW Small CUV 21,033 2.25*21,033=47,350 1..02*47,350=48,306

Total BMW Vehicles 61,324 138,053 163,347

Using this process, the numbers will get closer to the goal of matching CSM’s market 

share for each manufacturer and distribution for each vehicle segment after each of the 

iterations. The iterative process is carried out until the totals nearly match the goals. 

After 19 iterations, all numbers were within 0.01% of CSM’s distributions. The 

calculation iterations could have been stopped sooner, but they were continued to observe 

how the numbers would converge. 

After the market share and segment distribution were complete, the totals need to be 

used to create multipliers that could be applied to the original individual 2008 model year 

vehicle volumes (each unique manufacture models volume). The total for each manufacturer 

segment divided by the 2008 model year total for each manufacturer segment gives a 

multiplier that can be applied to each vehicle based on its manufacturer and segment. 

The above process is done for each model year needed (2017-2025). The multipliers 

are then applied to each vehicle in 2008 model year, which gives a volume for each vehicle in 

2017 through 2025 model year. 

1.3.3 What are the sales volumes and characteristics of the MY 2008 based 

reference fleet? 

Table 1-13 and Table 1-15 below contain the sales volumes that result from the 

process above for MY 2008 and 2017-2020. Table 1-14 and Table 1-16 below contain the 

sales volumes that result from the process above for MY 2021-2025. 

Table 1-13 Vehicle Segment Volumesa

Reference Class Segment

Actual and Projected Sales Volume

2008 2017 2018 2019 2020
 The Baseline and Reference Vehicle Fleets

 1-26 

LargeAuto 562,240 376,107 356,768 353,609 394,864

MidSizeAuto 3,098,927 3,311,268 3,290,408 3,303,621 3,381,785

CompactAuto 1,979,461 2,347,980 2,325,393 2,369,301 2,448,021

SubCmpctAuto 1,365,833 2,458,222 2,454,112 2,489,208 2,553,350

LargePickup 1,582,226 1,514,619 1,443,766 1,383,190 1,386,195

SmallPickup 177,497 156,227 157,932 160,752 146,029

LargeSUV 2,783,949 3,194,489 3,150,101 3,177,868 3,203,244

MidSizeSUV 1,263,360 1,358,755 1,309,212 1,267,394 1,285,822

SmallSUV 285,355 148,251 149,933 154,675 162,677

MiniVan 642,055 754,562 739,551 717,065 714,323

CargoVan 110,858 185,841 199,234 201,974 219,628

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 

Table 1-14 Vehicle Segment Volumesa

Reference Class Segment

Projected Sales Volume

2021 2022 2023 2024 2025

LargeAuto 380,192 358,295 362,672 356,173 368,843

MidSizeAuto 3,442,116 3,548,263 3,692,533 3,751,496 3,814,941

CompactAuto 2,520,977 2,592,199 2,632,926 2,744,634 2,843,069

SubCmpctAuto 2,626,364 2,687,167 2,721,102 2,796,061 2,878,288

LargePickup 1,368,301 1,349,421 1,301,293 1,271,751 1,260,389

SmallPickup 150,123 147,138 151,315 154,627 154,838

LargeSUV 3,312,914 3,362,608 3,412,753 3,475,873 3,520,992

MidSizeSUV 1,281,240 1,283,244 1,268,288 1,292,662 1,305,362

SmallSUV 167,223 169,643 170,239 173,191 175,713

MiniVan 729,078 738,982 740,785 720,720 726,256

CargoVan 210,539 202,812 201,585 196,900 201,768

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 

Table 1-15 2011+ NHTSA Car and Truck Definition Based Volumes 

Vehicle Type Actual and Projected Sales Volume

2008 2017 2018 2019 2020

Trucks 5,621,193 5,818,655 5,671,046 5,582,962 5,604,377

Cars 8,230,568 9,987,667 9,905,364 9,995,696 10,291,562

Cars and Trucks 13,851,761 15,806,322 15,576,410 15,578,658 15,895,939

Table 1-16 2011+ NHTSA Car and Truck Definition Based Volumes 

Vehicle Type Projected Sales Volume

2021 2022 2023 2024 2025
 The Baseline and Reference Vehicle Fleets

 1-27 

Trucks 5,683,902 5,703,996 5,687,486 5,675,949 5,708,899

Cars 10,505,165 10,735,777 10,968,003 11,258,138 11,541,560

Cars and Trucks 16,189,066 16,439,772 16,655,489 16,934,087 17,250,459

Table 1-17 and Table 1-18 below contain the sales volumes by manufacturer and 

vehicle type for MY 2008 and 2017-2025. 

Table 1-17 NHTSA Car and Truck Definition Manufacturer Volumes 

Manufacturers Vehicle Type 2008 

Baseline 

Sales

2017 

Projected 

Volume

2018 

Projected 

Volume

2019 

Projected 

Volume

2020 

Projected 

Volume

All Both 13,851,761 15,806,322 15,576,410 15,578,658 15,895,939

All Cars 8,230,568 9,987,667 9,905,364 9,995,696 10,291,562

All Trucks 5,621,193 5,818,655 5,671,046 5,582,962 5,604,377

Aston Martin Cars 1,370 1,035 1,051 1,072 1,034

Aston Martin Trucks - - - - -

BMW Cars 291,796 313,022 322,939 346,075 357,942

BMW Trucks 61,324 138,053 131,942 131,373 128,339

Chrysler/Fiat Cars 703,158 418,763 397,538 391,689 415,319

Chrysler/Fiat Trucks 956,792 409,702 387,858 366,447 360,677

Daimler Cars 208,195 284,847 276,409 281,425 290,989

Daimler Trucks 79,135 86,913 83,651 88,188 92,919

Ferrari Cars 1,450 6,676 6,700 6,794 6,916

Ferrari Trucks - - - - -

Ford Cars 956,699 1,299,899 1,311,467 1,332,039 1,378,789

Ford Trucks 814,194 763,549 748,829 717,773 717,037

Geely/Volvo Cars 32,748 41,887 42,187 43,125 42,615

Geely/Volvo Trucks 65,649 88,234 89,394 91,575 93,003

GM Cars 1,507,797 1,362,761 1,438,355 1,505,025 1,530,755

GM Trucks 1,587,391 1,462,204 1,474,076 1,493,511 1,544,983

HONDA Cars 1,006,639 1,154,600 1,138,087 1,144,639 1,163,666

HONDA Trucks 505,140 596,481 544,619 527,535 525,089

HYUNDAI Cars 337,869 592,027 578,373 582,971 598,283

HYUNDAI Trucks 53,158 152,885 151,461 155,642 154,173

Kia Cars 221,980 322,044 312,370 314,879 323,676

Kia Trucks 59,472 98,702 98,280 100,679 96,535

Lotus Cars 252 240 243 250 266

Lotus Trucks - - - - -

Mazda Cars 246,661 253,540 262,512 266,951 270,078
 The Baseline and Reference Vehicle Fleets

 1-28 

Mazda Trucks 55,885 51,788 57,535 57,494 58,154

Mitsubishi Cars 85,358 65,099 63,671 63,826 65,080

Mitsubishi Trucks 15,371 37,632 36,300 35,454 35,215

Nissan Cars 717,869 870,797 849,678 854,400 882,791

Nissan Trucks 305,546 444,938 412,383 398,559 397,869

PORSCHE Cars 18,909 35,093 35,444 36,116 35,963

PORSCHE Trucks 18,797 13,233 12,001 11,469 11,141

Spyker/Saab Cars 21,706 20,024 20,007 20,144 21,069

Spyker/Saab Trucks 4,250 2,871 3,596 3,826 3,509

Subaru Cars 116,035 224,112 216,598 217,095 223,466

Subaru Trucks 82,546 78,242 75,152 72,832 72,458

Suzuki Cars 79,339 90,708 89,932 90,568 93,548

Suzuki Trucks 35,319 22,109 21,385 20,692 20,675

Tata/JLR Cars 9,596 55,881 56,222 57,267 58,182

Tata/JLR Trucks 55,584 57,579 56,606 57,854 56,213

Tesla Cars 800 27,986 28,435 28,990 27,965

Tesla Trucks - - - - -

Toyota Cars 1,260,364 1,849,196 1,834,181 1,836,306 1,883,734

Toyota Trucks 951,136 1,330,511 1,223,415 1,142,104 1,154,304

Volkswagen Cars 291,483 551,638 540,036 537,114 554,822

Volkswagen Trucks 26,999 128,819 145,491 146,891 146,700

Table 1-18 NHTSA Car and Truck Definition Manufacturer Volumes 

Manufacturers Vehicle Type 2021

Projected 

Volume

2022

Projected 

Volume

2023

Projected 

Volume

2024

Projected 

Volume

2025

Projected 

Volume

All Both 16,189,066 16,439,772 16,655,489 16,934,087 17,250,459

All Cars 10,505,165 10,735,777 10,968,003 11,258,138 11,541,560

All Trucks 5,683,902 5,703,996 5,687,486 5,675,949 5,708,899

Aston Martin Cars 1,058 1,049 1,041 1,141 1,182

Aston Martin Trucks - - - - -

BMW Cars 359,098 360,034 360,561 388,193 405,256

BMW Trucks 128,724 128,899 127,521 146,525 145,409

Chrysler/Fiat Cars 421,013 424,173 423,882 426,017 436,479

Chrysler/Fiat Trucks 348,613 363,008 361,064 344,962 331,762

Daimler Cars 300,378 304,738 312,507 332,337 340,719

Daimler Trucks 99,449 100,935 105,315 107,084 101,067

Ferrari Cars 7,059 7,138 7,227 7,441 7,658

Ferrari Trucks - - - - -
 The Baseline and Reference Vehicle Fleets

 1-29 

Ford Cars 1,401,617 1,415,221 1,474,797 1,503,670 1,540,109

Ford Trucks 714,181 714,266 700,005 688,854 684,476

Geely/Volvo Cars 41,768 41,686 42,031 42,461 42,588

Geely/Volvo Trucks 92,726 92,512 96,840 99,181 101,107

GM Cars 1,530,020 1,507,653 1,496,819 1,493,597 1,524,008

GM Trucks 1,564,277 1,578,556 1,606,495 1,636,805 1,673,936

HONDA Cars 1,198,880 1,237,504 1,265,564 1,307,851 1,340,321

HONDA Trucks 535,916 539,235 536,898 536,994 557,697

HYUNDAI Cars 613,355 627,964 634,308 657,710 677,250

HYUNDAI Trucks 156,466 157,493 161,189 166,092 168,136

Kia Cars 331,319 339,102 342,746 351,882 362,783

Kia Trucks 95,432 94,694 95,688 96,119 97,653

Lotus Cars 278 290 299 308 316

Lotus Trucks - - - - -

Mazda Cars 274,740 281,150 296,910 300,614 306,804

Mazda Trucks 59,227 60,307 61,966 61,971 61,368

Mitsubishi Cars 65,851 67,261 67,680 70,728 73,305

Mitsubishi Trucks 35,309 35,227 35,469 36,001 36,387

Nissan Cars 912,629 937,447 954,340 982,771 1,014,775

Nissan Trucks 408,029 411,883 417,121 422,217 426,454

PORSCHE Cars 36,475 36,607 36,993 39,504 40,696

PORSCHE Trucks 11,242 11,385 11,370 11,409 11,219

Spyker/Saab Cars 21,294 21,709 22,410 22,800 23,130

Spyker/Saab Trucks 3,560 3,461 3,435 3,426 3,475

Subaru Cars 230,780 238,613 241,612 248,283 256,970

Subaru Trucks 72,773 72,736 73,022 74,142 74,722

Suzuki Cars 95,725 97,599 99,263 100,447 103,154

Suzuki Trucks 20,767 20,734 20,803 21,162 21,374

Tata/JLR Cars 58,677 59,349 60,639 63,728 65,418

Tata/JLR Trucks 58,153 58,590 58,865 57,981 56,805

Tesla Cars 28,623 28,369 28,150 30,862 31,974

Tesla Trucks - - - - -

Toyota Cars 1,903,706 1,986,077 2,036,992 2,080,528 2,108,053

Toyota Trucks 1,215,539 1,235,052 1,224,980 1,208,013 1,210,016

Volkswagen Cars 585,607 593,314 596,749 605,336 630,163

Volkswagen Trucks 148,734 146,750 153,927 156,939 154,284

Table 1-19 also shows how the change in fleet make-up may affect the footprint 

distributions over time. The resulting data indicate that footprint will not change significantly 

between 2008 and 2025. There will be an increase in the number of cars sold, which will 

cause the average footprints for cars and trucks combined to be slightly smaller (about 2%). 
 The Baseline and Reference Vehicle Fleets

 1-30 

This is the result of AEO projecting an increased number of cars, and CSM predicting that 

most of that increase will be in the subcompact segment. Again, we note that in order to 

ensure that our baseline inputs were not influenced by the final regulations, agencies re-ran 

AEO to hold standards constant after 2016 (the reader will remember from the text above that 

CSM had indicated that its projections were not sensitive to assumptions about new 

standards). 

Table 1-19 Production Weighted Foot Print Mean 

Model 

Year

Average Footprint of all 

Vehicles

Average Footprint 

Cars

Average Footprint 

Trucks

2008 48.8 45.2 53.9

2017 48.0 44.6 53.8

2018 47.9 44.6 53.7

2019 47.8 44.6 53.6

2020 47.8 44.6 53.7

2021 47.8 44.6 53.6

2022 47.7 44.6 53.6

2023 47.7 44.6 53.5

2024 47.5 44.6 53.3

2025 47.5 44.6 53.3

Table 1-20 below shows the changes in engine cylinders over the model years. The 

current assumptions show that engines will be downsized over the model years to which these 

final rules apply. This shift is a projected consequence of the expected changes in class and 

segment mix as predicted by AEO and CSM, and does not represent engine downsizing 

attributable to the 2012-2016 light-duty CAFE and GHG standards. 

Table 1-20 Percentages of 4, 6, 8 Cylinder Engines by Model Year 

Trucks Cars

Model 

Year

4 

Cylinders

6 

Cylinders

8 

Cylinders

4 

Cylinders

6 

Cylinders

8 

Cylinders

2008 10.3% 56.4% 33.3% 56.9% 37.8% 5.3%

2017 10.9% 63.7% 25.4% 60.6% 34.5% 5.0%

2018 10.6% 64.5% 24.8% 60.7% 34.4% 5.0%

2019 10.4% 65.5% 24.1% 60.7% 34.3% 5.0%

2020 10.3% 65.6% 24.1% 60.3% 34.7% 5.0%

2021 10.3% 66.3% 23.4% 60.6% 34.4% 4.9%

2022 10.3% 66.7% 23.0% 61.1% 34.2% 4.8%
 The Baseline and Reference Vehicle Fleets

 1-31 

2023 10.3% 67.7% 22.0% 60.9% 34.3% 4.8%

2024 10.5% 68.1% 21.4% 61.0% 34.1% 4.8%

2025 10.5% 68.2% 21.3% 61.1% 34.0% 4.8%

As discussed above, the agencies also developed a second market forecast using updated data. 

The following section describes those efforts and their results. 

1.4 The 2010 MY Based Fleet 

The 2010 MY based fleet is similar to the 2008 MY based fleet in that it was created 

with similar types of information. The 2010 MY based fleet uses interim AEO 2012 total car 

and truck volumes, a long range forecast from LMC Automotive (formerly J.D. Powers 

Forecasting) used for manufacturer market share and product mix, and 2010 CAFE 

certification data for 2010 model volumes and technology. The 2008 MY based fleet, in 

contrast, uses interim AEO 2011, a long range forecast from CSM World Wide, and 2008 

CAFE certification data. The remainder of section 1.4 describes the 2010 based fleet 

projection and how it was created. 

1.4.1 On what data is the MY 2010 baseline vehicle fleet based? 

Similar to the 2008 baseline, most of the information about the vehicles that make up 

the 2010 fleet was gathered from EPA’s emission certification and fuel economy database,

most of which is available to the public. These data included, by individual vehicle model 

produced in MY 2010, vehicle production volume, fuel economy rating for CAFE 

certification, carbon dioxide emissions, fuel type, fuel injection type, EGR, number of engine 

cylinders, displacement, intake valves per cylinder, exhaust valves per cylinder, variable valve 

timing, variable valve lift, engine cycle, cylinder deactivation, transmission type, drive (rearwheel, all-wheel, etc.), hybrid type (if applicable), and aspiration (naturally-aspirated, 

turbocharged, etc.). In addition to this information about each vehicle model produced in MY 

2010, the agencies augmented this description with publicly-available data which includes 

more complete technology descriptions from Ward’s Automotive Group.m,n

 As with the 

2008 baseline, the agencies also used Edmunds.com and Motortrend.como,p,q

Like the MY 

2008 baseline fleet and the baseline vehicle fleet used in the MYs 2012-2016 rulemaking, the 

MY 2010 baseline vehicle fleet is developed using publicly-available data to the largest 

extent possible. 

 

o

 Motortrend.com and Edmunds.com: Used as a source for footprint and vehicle weight data. 

p

 Motortrend.com and Edmunds.com are free, no-fee internet sites. 

q

 A small amount of footprint data from manufacturers’ MY 2008 product plans submitted to the agencies was 

used in the development of the baseline. 
 The Baseline and Reference Vehicle Fleets

 1-32 

The process for creating the 2010 baseline fleet Excel file was streamlined when 

compared with the past rulemaking. EPA and NHTSA worked together to create the baseline 

using 2010 CAFE certification data from EPA’s Verify database. EPA contracted LMC 

Automotive (formerly JD Power Forecasting) to produce an up to date long range forecast of 

volumes for the future fleet. Using information sources discussed below, NHTSA identified 

technology and footprint information for every vehicle model in the 2010 CAFE certification 

data. EPA used the forecast from LMC Automotive to project the future fleet’s volume 

projections (a detailed discussion of the method used to project the future fleet volumes is in 

1.4.2.1 of this chapter.) 

Both agencies used the previously mentioned data to populate input files for the 

agencies’ respective modeling systems. The structure of the market forecast input file used 

for DOT’s CAFE Compliance and Effects Modeling System (a.k.a. “the CAFE model”) is 

described in the model documentation.5 To help readers who wish to directly examine the 

baseline fleet file for EPA’s OMEGA model, and to provide some idea of its contents for 

those readers who do not, Table 1-21 shows the columns of the complete fleet file, which 

includes the MY 2008 baseline data that was compiled. Each column has its name, definition 

(description) and source. Most elements shown in Table 1-21 also appear in the market 

forecast input file for DOT’s modeling system, which accommodates some additional data 

elements discussed in the model documentation. 

Table 1-21 Data, Definitions, and Sources 

Data Item Definition Data Type

Wards 

Engine 

Acronyms 

Where The 

Data is From

Index Index Used to link EPA and NHTSA baselines Number NA Created

Manufacturer

Common name of company that manufactured 

vehicle. May include more name plates than Cert 

Manufacturer Name.

Name 

(Ex.Chrysler) NA Certification data

CERT 

Manufacturer 

Name

Certification name of company that manufactured 

vehicle

Name 

(Ex.Chrysler) NA Certification data

Name Plate Name of Division Name (Ex. Dodge) NA Certification data

Model Name of Vehicle Name (Ex.Viper) NA Certification data

Reg Class EPA Fuel Economy Class Name

EPA Class Name 

(Ex. 

SUBCOMPACT 

CARS)

NA Certification data

Our Class

If a car’s Footprint<43 then "SubCmpctAuto"

If a car’s 43<=Footprint<46 then "CompactAuto"

If a car’s 46<=Footprint<53 then "MidSizeAuto”

If a car’s Footprint >=53 then “LargeAuto”

If a S.U.V.’s Footprint < 43 then “SmallSuv”

If a S.U.V.’s 43<=Footprint<46 then 

"MidSizeSuv”

If a S.U.V’s Footprint >=46 then “LargeSuv”

If a Truck’s Footprint < 50 then “SmallPickup”

If a Truck’s Footprint>=50 then “LargPickup”

If a Van’s Structure is Ladder then “CargoVan”

If a Van’s Structure is Unibody then “Minivan”

Name(Ex. 

SmallSuv) NA

Derived From 

Certification data 

and Footprint
 The Baseline and Reference Vehicle Fleets

 1-33 

NEW 

SEGMENT

LMC Automotive (formerly J. D. Powers) new 

segmentation for the vehicle.

Name (Ex. 

Compact Sporty,

Large Pickup, etc.)

NA LMC Automotive

Vehicle Type 

Number

Vehicle Type Number assigned to a vehicle based 

on its number of cylinders, valves per cylinder, and 

valve actuation technology. See Truck Vehicle 

Type Map and Car Vehicle Type Map sheets for 

details.

Number NA Mapped by EPA 

staff

Generic 

Vehicle Index 

From Sum 

Page

Number to be used as a cross reference with the 

Sum Pages. Number NA NA

Vehicle Index 

From Sum 

Page

Number to be used as a cross reference with the 

Sum Pages. Number NA NA

Pre 2011 

NHTSA 

Defined C/T

C= Car, T=Truck. As defined in the certification 

database. Letter(C or T) NA Certification data

Our Class C/T C= Car, T=Truck. As defined in the certification 

database. Not used in calculations. Letter(C or T) NA Created

Traditional 

Car/Truck

DP=Domestic Passenger Cars, I=Import Passenger 

Car, LT= Light duty Truck. As defined in the 

certification database. Not used in calculations.

IP,DP,LT NA Certification data

NHTSA 

Defined New 

NHTSA 

Car/Truck

New NHTSA Car Truck value as determined by 

NHTSA. Used in calculations. Letter(C or T) NA Certification data

Total 

Production 

Volume

Total number of vehicles produced for that model. number(ex.5500) NA Certification data

Fuel Econ. 

(mpg) EPA Unadjusted Fuel Economy number(ex.25) NA Certification data

CO2 CO2 calculated from MPG. CO2 weighted 1.15 

times higher for diesel vehicles. Number NA Certification data

Fuel (G,D,C) Gas or Diesel or CNG G,D,C NA Certification data

Fuel Type Gas or Diesel or CNG Gas or Diesel or 

CNG NA Certification data

Disp (lit.) Engine Cylinder Displacement Size in Liters number(ex. 4) NA Wards/Certificati

on data

Effective Cyl Number of Cylinder + 2 if the engine has a turbo or 

super charger. number(ex. 6) NA Derived From Certification data.

Actual 

Cylinders Actual Number of Engine Cylinders number(ex. 4) NA Certification data

Valves Per 

Cylinder Number of Valves Per Actual Cylinder number(ex. 4) NA Certification data

Valve Type Type of valve actuation.

Acronym(Ex. 

DOHC, SOHC, 

OHV, E, R)

DOHC, SOHC, 

OHV, E, R

Wards (Note: 

Type E is from 

Cert Data)

Valve 

Actuation

Type of valve actuation with values compatible 

with the package file.

DOHC, SOHC, 

OHV

DOHC, SOHC, 

OHV Wards

VVT Type of valve timing with values compatible with 

the package file.

VVTC,VVTD, 

VVTI

VVTC,VVTD, 

VVTI Wards

VVLT Type of valve lift with values compatible with the 

package file. VVTLC, VVTLD VVTLC, VVTLD Wards
 The Baseline and Reference Vehicle Fleets

 1-34 

Deac Cylinder Deactivation with a value that is 

compatible with the package file. Deac CD Wards

Inline or V 

Engine Configuration of the Engine I or V I or V Wards

Fuel injection 

system 

Type of fuel injection. DI, MPI DI, SFI, EFI, 

MPI Wards

Boost Type of Boost if any.

Super Charged 

(Single), Turbo 

(Single)

TRB,SPR Wards

Engine Cycle As Defined by EPA Cert. Definition Letter Ex. G for 

Gas) NA Wards

Horsepower Max. Horsepower of the Engine number(ex. 125) NA Wards

Torque Max. Torque of the Engine number(ex. 125) NA Wards

Cooled EGR Cooled Exhaust Gas Recirculation Y or N NA Certification data

Trans Type A=Auto AMT=Automated Manual M=Manual 

CVT= Continuously Variable Transmission letter(ex. A) NA Certification data

Tran Type Code with number of Gears

letters and possible 

a number(ex.A5, 

ex. CVT)

NA Certification data

Num of Gears Number of Gears number(ex. 4) NA Certification data

Structure Ladder or Unibody Unibody or Ladder 

(Ex. Ladder) NA Volpe Input File

Drive Fwd, Rwd, 4wd Acronym(Ex. Rwd) NA Certification data

Drive with 

AWD Fwd, Rwd, Awd, 4wd Acronym(Ex. Awd) NA Certification data

Wheelbase Length of Wheelbase number(ex. 125) NA

From 

Edmonds.com or 

Motortrend.com,

Track Width 

(front) Length of Track Width in inches number(ex. 45) NA

From 

Edmonds.com or 

Motortrend.com

Track Width 

(rear) Length of Track Width in inches number(ex. 45) NA

From

Edmonds.com or 

Motortrend.com

Footprint

Car and Large Truck Footprints are normal 

(Average Track x Wheelbase). Medium and Small 

Truck footprints are the production weighted 

average for each vehicle.

Number NA

From

Edmonds.com or 

Motortrend.com

Threshold 

FootPrint

Footprint valve that will be set to 41 for values less 

than 41, Will be set to 56 for car values > 56, and 

will be set to 66 for truck values >66

Number NA

Derived from 

data from 

Edmunds.com or 

Motortrend.com

Curb

Weight Curb Weight of the Vehicle number(ex.4500) NA Certification data

ITW Inertia Test Weight number(ex.4500) NA Certification data

GVWR Gross Vehicle Weight Rating of the Vehicle number(ex.4500) NA Volpe Input File

Stop-Start/

Hybrid/ Full 

EV

Type of Electrification if any. Blank = None

EV75,2-ModeIMA,PowerSplit,Stop-Start

NA Certification data

Towing 

Capacity 

(Maximum)

Weight a vehicle is rated to tow. Number (in 

Pounds) NA Volpe Input File

Engine Oil 

Viscosity

Ratio between the applied shear stress and the rate 

of shear, which measures the resistance of flow of 

the engine oil (as per SAE Glossary of Automotive 

Terms)

Text (Ex. 0W20; 

5W20) NA Volpe Input File

Low drag brakes See Volpe Documentation See Volpe NA Volpe Input File
 The Baseline and Reference Vehicle Fleets

 1-35 

Documentation

Power steering See Volpe Documentation See Volpe 

Documentation NA Volpe Input File

Technology 

Class

For technology application purposes only and 

should not be confused with vehicle classification 

for regulatory purposes. Defined by DOT.

Text (Ex. 

Subcompact, 

Subcompact 

Performance, 

Compact, Compact 

Performance, 

Midsize, Midsize 

Performance, 

Large, Large 

Performance, 

Minivan, Small LT, 

Midsize LT, Large 

LT; (LT = 

SUV/Pickup/Van))

NA Volpe Input File

Safety Class See Volpe Documentation See Volpe 

Documentation NA Volpe Input File

Safety Class 

Number See Volpe Documentation See Volpe 

Documentation NA Volpe Input File

Volume 2010 Projected Production Volume for 2010 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2011 Projected Production Volume for 2011 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2012 Projected Production Volume for 2012 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2013 Projected Production Volume for 2013 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2014 Projected Production Volume for 2014 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2015 Projected Production Volume for 2015 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2016 Projected Production Volume for 2016 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.
 The Baseline and Reference Vehicle Fleets

 1-36 

Volume 2017 Projected Production Volume for 2017 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2018 Projected Production Volume for 2018 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2019 Projected Production Volume for 2019 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2020 Projected Production Volume for 2020 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2021 Projected Production Volume for 2021 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2022 Projected Production Volume for 2022 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2023

Projected Production Volume for 2023 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2024

Projected Production Volume for 2024 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Volume 2025

Projected Production Volume for 2025 Number NA

Calculated based 

on MY2010

volume and AEO 

and LMC 

adjustment 

factors.

Table 1-22 displays the engine technologies present in the MY 2010 baseline fleet. 

Again, the engine technologies for the vehicles manufactured by these manufacturers in MY 

2010 were largely obtained from data found on Ward’s Auto online. 
 The Baseline and Reference Vehicle Fleets

 1-37 

Table 1-22 2010 Engine Technology Percentages 

Manufacturers

Vehicle Type

Turbo Charged

Super Charged

Single Overhead Cam

Dual Over Head Cam

Over Head Cam

Variable Valve Timing 

Continuous

Variable Valve Timing 

Discrete

Variable Valve Timing 

Intake Only

Variable Valve Lift and 

Timing Continuous

Variable Valve Lift and 

Timing Discrete

Cylinder Deactivation

Direct Injection

All Both 3% 0% 22% 68% 10% 41% 26% 39% 6% 2% 4% 9%

All Cars 4% 0% 18% 78% 4% 11% 26% 48% 6% 2% 3% 9%

All Trucks 2% 0% 29% 50% 21% 17% 27% 22% 5% 2% 7% 9%

Aston Martin Cars 0% 0% 0% 100% 0% 0% 0% 38% 0% 0% 0% 0%

Aston Martin Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

BMW Cars 38% 0% 0% 100% 0% 28% 70% 0% 0% 45% 0% 38%

BMW Trucks 33% 0% 0% 100% 0% 0% 82% 0% 0% 67% 0% 33%

Chrysler/Fiat Cars 0% 0% 42% 49% 9% 0% 41% 0% 0% 0% 5% 0%

Chrysler/Fiat Trucks 0% 0% 30% 4% 66% 0% 4% 0% 0% 0% 13% 0%

Daimler Cars 0% 0% 50% 50% 0% 52% 46% 0% 0% 46% 0% 0%

Daimler Trucks 8% 1% 24% 76% 0% 24% 69% 0% 0% 76% 0% 8%

Ferrari Cars 0% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 90%

Ferrari Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Ford Cars 1% 0% 12% 88% 0% 2% 0% 69% 0% 0% 0% 1%

Ford Trucks 1% 0% 70% 30% 0% 50% 0% 26% 0% 0% 0% 1%

Geely Trucks 38% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0%

Geely Cars 25% 0% 0% 100% 0% 0% 100% 0% 0% 0% 0% 0%

General Motors Trucks 0% 0% 0% 45% 55% 0% 42% 0% 0% 0% 4% 37%

General Motors Cars 0% 0% 0% 75% 25% 0% 73% 1% 0% 0% 3% 31%

Honda Cars 1% 0% 58% 42% 0% 58% 42% 0% 42% 0% 17% 0%

Honda Trucks 2% 0% 63% 37% 0% 63% 37% 0% 37% 0% 45% 0%

Hyundai Cars 3% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Hyundai Trucks 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Kia Cars 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Kia Trucks 0% 0% 0% 100% 0% 0% 0% 73% 0% 0% 0% 0%

Lotus Cars 0% 16% 0% 100% 0% 0% 100% 0% 23% 0% 0% 0%

Lotus Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Mazda Cars 4% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 2%

Mazda Trucks 11% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Mitsubishi Cars 6% 0% 100% 0% 0% 96% 0% 0% 0% 0% 0% 0%

Mitsubishi Trucks 0% 0% 100% 0% 0% 74% 0% 0% 0% 0% 0% 0%

Nissan Cars 0% 0% 0% 100% 0% 0% 0% 100% 9% 0% 0% 0%

Nissan Trucks 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0%

Porsche Cars 16% 0% 0% 100% 0% 0% 0% 100% 100% 0% 0% 83%

Porsche Trucks 1% 0% 0% 100% 0% 0% 0% 100% 100% 0% 0% 100%

Spyker Cars 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Spyker Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Subaru Cars 6% 0% 92% 8% 0% 0% 2% 6% 2% 0% 0% 0%
 The Baseline and Reference Vehicle Fleets

 1-38 

Subaru Trucks 0% 0% 87% 13% 0% 0% 13% 0% 13% 0% 0% 0%

Suzuki Cars 0% 0% 0% 100% 0% 0% 2% 98% 0% 0% 0% 0%

Suzuki Trucks 0% 0% 0% 100% 0% 0% 50% 50% 0% 0% 0% 0%

Tata Cars 0% 22% 0% 100% 0% 0% 67% 33% 45% 7% 0% 67%

Tata Trucks 0% 15% 0% 100% 0% 0% 67% 33% 64% 0% 0% 67%

Tesla Cars 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Tesla Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Toyota Cars 0% 0% 0% 100% 0% 0% 16% 84% 0% 0% 0% 4%

Toyota Trucks 0% 0% 0% 100% 0% 0% 62% 38% 0% 0% 0% 0%

Volkswagen Cars 62% 4% 68% 32% 0% 47% 32% 0% 1% 0% 0% 68%

Volkswagen Trucks 33% 0% 21% 79% 0% 21% 67% 0% 52% 0% 0% 100%

The data in Table 1-22 indicate that manufacturers had already begun implementing a 

number of fuel economy/GHG reduction technologies in the baseline (2010) fleet. For 

example, as in the 2008 baseline fleet, VW stands out as having a significant number of 

turbocharged direct injection engines. Some of the valve and cam technologies are quite 

common in the baseline fleet: for example, nearly half the baseline fleet already has dual cam 

phasing, while Honda and Chrysler have considerable levels of engines with cylinder 

deactivation. Honda also has already implemented continuously variable valve lift on a 

majority of their engines. Part of the implication of these technologies already being present 

in the baseline is that if manufacturers have already implemented them, they are therefore not 

available in the rulemaking analysis for improving fuel economy and reducing CO2 emissions 

further, requiring the agencies to look toward increasing penetration of these and other 

technologies and increasingly advanced technologies to project continued improvements in 

stringency over time. 

The data in Table 1-23 shows the changes between the 2010 engine technology 

penetrations and the 2008 engine technology penetrations. Perhaps to increase fuel economy, 

manufacturers applied considerable additional technology between 2008 and 2010. 

Volkswagen’s trucks have direct injection increased to 100 percent (although VW’s cars had a 

21% decrease). Manufacturers changed variable valve timing, presumably based on enginespecific design considerations. For example, Honda replaced discrete valve timing with 

continuous valve lift or timing, and Kia added variable valve lift and timing to 90% of its cars 

and 56% of its trucks. 

 
 The Baseline and Reference Vehicle Fleets

 1-39 

Table 1-23 The difference (2010-2008) in Engine Technology Percentages 

Manufacturer

Vehicle Type

Turbo Charged

Super Charged

Single Overhead Cam

Dual Overhead Cam

Overhead Cam

Variable Valve Timing 

Continuous

Variable Valve Timing 

Discrete

Variable Valve Timing 

Intake Only

Variable Valve Lift and 

Timing Continuous

Variable Valve Lift and 

Timing Discrete

Cylinder Deactivation

Direct Injection

All Both 0% 0% 2% 5% -7% 33% 4% 9% 6% -10% -2% 4%

All Cars 0% 0% 1% 5% -5% 2% 2% 13% 6% -11% 0% 2%

All Trucks 1% 0% 5% 2% -8% 11% 8% -1% 5% -8% -4% 6%

Aston Martin Cars 0% 0% 0% 0% 0% 0% -100% 38% -24% 0% 0% 0%

Aston Martin Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

BMW Cars 5% -1% -14% 14% 0% 14% -16% 0% 0% 32% 0% 5%

BMW Trucks 28% 0% 0% 0% 0% 0% -18% 0% 0% 67% 0% 27%

Chrysler/Fiat Cars -1% 0% 21% -23% 1% 0% -1% 0% 0% 0% 0% 0%

Chrysler/Fiat Trucks 0% 0% -9% 0% 9% 0% 0% 0% 0% 0% 9% 0%

Daimler Cars -2% 0% -5% 5% 0% -20% 42% -13% 0% 46% 0% -2%

Daimler Trucks -8% 0% -12% 12% 0% -11% 52% -47% 0% 76% 0% -8%

Ferrari Cars 0% 0% 0% 0% 0% 0% 0% 0% -29% 0% 0% 90%

Ferrari Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Ford Cars 1% -1% -3% 3% 0% -2% 0% 22% 0% 0% 0% 1%

Ford Trucks 1% 0% 5% -2% -3% 22% -1% 17% 0% 0% 0% 1%

Geely/Volvo Trucks 38% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Geely/Volvo Cars -24% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

GM Trucks 0% 0% 0% 14% -14% -5% 25% -14% 0% 0% -36% 37%

GM Cars -1% 0% 0% 19% -19% -29% 42% 0% 0% 0% -1% 25%

Honda Cars 1% 0% 1% -1% 0% 58% 15% -20% 42% -100% 6% 0%

Honda Trucks -2% 0% -1% 1% 0% 63% 33% -28% 37% -100% 45% -4%

Hyundai Cars 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Hyundai Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Kia Cars 0% 0% 0% 0% 0% 0% 0% 90% 0% 0% 0% 0%

Kia Trucks 0% 0% 0% 0% 0% 0% 0% 56% 0% 0% 0% 0%

Lotus Cars 0% -61% 0% 0% 0% 0% 0% 0% 23% 0% 0% 0%

Lotus Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Mazda Cars -7% 0% 0% 1% 0% 0% -7% 8% 0% 0% 0% -9%

Mazda Trucks -13% 0% -1% 1% 0% 0% -13% 13% 0% 0% 0% -24%

Mitsubishi Cars 0% 0% 0% 0% 0% -4% 0% 0% 0% 0% 0% 0%

Mitsubishi Trucks 0% 0% 0% 0% 0% 36% 0% 0% 0% 0% 0% 0%

Nissan Cars 0% 0% 0% 0% 0% 0% -4% 4% 9% 0% 0% 0%

Nissan Trucks 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Porsche Cars -1% 0% 0% 0% 0% 0% -100% 100% 100% 0% 0% 66%

Porsche Trucks -11% 0% 0% 0% 0% 0% -100% 100% 100% -100% 0% 0%

Spyker/Saab Cars NA NA NA NA NA NA NA NA NA NA NA NA

Spyker/Saab Trucks NA NA NA NA NA NA NA NA NA NA NA NA

Subaru Cars -9% 0% 23% -23% 0% 0% 2% -25% 2% -1% 0% 0%

Subaru Trucks -3% 0% 17% -17% 0% 0% -10% -7% 13% -27% 0% 0%

Suzuki Cars 0% 0% 0% 0% 0% 0% 2% 98% 0% 0% 0% 0%
 The Baseline and Reference Vehicle Fleets

 1-40 

Suzuki Trucks 0% 0% 0% 0% 0% 0% 50% 50% 0% 0% 0% 0%

Tata/JLR Cars 0% 22% 0% 0% 0% 0% -9% 9% 45% 7% 0% 67%

Tata/JLR Trucks 0% -5% 0% 0% 0% 0% 67% -67% 64% 0% 0% 67%

Tesla Cars NA NA NA NA NA NA NA NA NA NA NA NA

Tesla Trucks NA NA NA NA NA NA NA NA NA NA NA NA

Toyota Cars 0% 0% 0% 0% 0% 0% -13% 13% 0% 0% 0% -4%

Toyota Trucks 0% 0% 0% 0% 0% 0% 1% -1% 0% 0% 0% -6%

Volkswagen Cars 19% 4% -17% 17% 0% 47% -16% 0% 1% -1% 0% -21%

Volkswagen Trucks 1% 0% 0% 100% 0% 0% 99% 0% 0% 79% 0% 100%

The section below provides further detail on the conversion of the MY 2010 baseline 

into the MYs 2017-2025 reference fleet. It also describes more of the data contained in the 

baseline spreadsheet. 

1.4.2 The MY 2010 Based MY 2017-2025 Reference Fleet 

The reference fleet aims to reflect the current market conditions and expectations 

about conditions of the vehicle fleet during the model years to which the agencies’ rules 

apply. Fundamentally, constructing this fleet involved projecting the MY 2010 baseline fleet 

into the MYs 2017-2025 model years. It also included the assumption that none of the vehicle 

models had changes during this period. Projecting this future fleet is a process that is 

necessarily uncertain. As with the MY 2008-based MY 2017-2025 reference fleet, NHTSA 

and EPA relied on many sources of reputable information to make these projections. 

1.4.2.1 On what data is the reference vehicle fleet based (using the MY2010 baseline)? 

EPA and NHTSA have based the projection of total car and light truck sales on the 

most recent projections available made by the Energy Information Administration (EIA). 

EIA’s Annual Energy Outlook (AEO) projects future energy production, consumption and 

prices.6

 EIA issued an “early release” version of AEO 2012 in January 2012. The complete 

final version of AEO 2012 was released June 25, 2012, but by that time EPA/NHTSA had 

already completed analyses supporting the final 2017-2025 standards using the interim data 

release. Similar to the analyses supporting the MYs 2012-2016 rulemaking and for the 2008 

based fleet projection, the agencies have used the Energy Information Administration’s 

(EIA’s) National Energy Modeling System (NEMS) to estimate the future relative market 

shares of passenger cars and light trucks. However, as explained above, NEMS shifts the 

market toward passenger cars in order to ensure compliance with EISA’s requirement that 

CAFE standards cause the fleet to achieve 35 mpg by 2020. Because we use our market 

projection as a baseline relative to which we measure the effects of new standards, and we 

attempt to estimate the industry’s ability to comply with new standards without changing 

product mix (i.e., we analyze the effects of the final rules assuming manufacturers will not 

change fleet composition as a compliance strategy), using the Interim AEO 2012-projected 

shift in passenger car market share as provided by EIA would cause the agencies to understate 

the cost of achieving compliance through additional technology, alone. Therefore, for the 

current analysis, the agencies developed a new projection of passenger car and light truck 

sales shares by using NEMS to run scenarios from the Interim AEO 2012 reference case, after 
 The Baseline and Reference Vehicle Fleets

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first deactivating the above-mentioned sales-volume shifting methodology and holding post2017 CAFE standards constant at MY 2016 levels. Incorporating these changes reduced the 

projected passenger car share of the light vehicle market by an average of about 5% during 

2017-2025. As with the comparable exercise for the 2008 MY baseline fleet, this case is 

referred to as the “Unforced Reference Case,” and the values are shown below in Table 1-24. 

Table 1-24 AEO 2012 Interim Unforced Reference Case Values used in the 2010 Market Fleet Projection 

Model Year Cars Trucks Total Vehicles

2017 8,713,800 7,098,300 15,812,100

2018 8,631,900 6,973,500 15,605,400

2019 8,688,600 6,973,500 15,662,100

2020 8,774,500 6,855,700 15,630,200

2021 8,898,400 6,831,700 15,730,100

2022 9,033,900 6,853,300 15,887,200

2023 9,179,600 6,827,600 16,007,200

2024 9,368,800 6,878,200 16,247,000

2025 9,525,700 6,929,100 16,454,800

In 2017, car and light truck sales are projected to be 8.7 and 7.1 million units, 

respectively (compared to 8.4 and 7.4 million in the 2010 AEO projection). While the total 

level of sales of 15.8 million units is similar to pre-2008 levels, the fraction of car sales in 

2017 and beyond is projected to be higher than in the previous AEO projections. 

In addition to a shift towards more car sales, sales of segments within both the car and 

truck markets have also been changing and are expected to continue to change in the future. 

The agencies also wanted to use the most updated information on Chrysler projections, as the 

older NPRM projection conducted by CSM showed Chrysler sales to be very low in 2025. 

The agencies agree with the Chrysler comments that the NPRM projections are most likely 

outdated and too low with respect to Chrysler’s market share. In order to reflect these 

changes in fleet makeup, EPA and NHTSA used a custom long range forecast purchased from 

LMC Automotive (formerly J.D. Powers Forecasting). J.D. Powers is a well-known industry 

analyst. NHTSA and EPA decided to use the forecast from LMC Automotive (J.D. Powers 

Forecasting) for MY2010-based market forecast for several reasons. First, Like CSM, LMC 

Automotive uses a ground up approach (e.g., looking at the number of plants and capacity for 

specific engines, transmissions, and vehicles) for their forecast, which the agencies believe is 

a robust forecasting approach. Second, LMC Automotive allows us to publish their entire 

forecast in the public domain. Third, the LMC Automotive forecast covered all the timeframe 

of greatest relevance to this analysis (2017-2025 model years). Fourth, it provided projections 

of vehicle sales both by manufacturer and by market segment. Fifth, it utilized market 

segments similar to those used in the EPA emission certification program and fuel economy 
 The Baseline and Reference Vehicle Fleets

 1-42 

guide, such that the agencies could include only the vehicle types covered by the final 

standards. And finally, it had a more updated projection of Chrysler sales. 

LMC Automotive created a forecast that covered model years 2010-2025. Since the 

agencies used this forecast to generate the reference fleet (i.e., the fleet expected to be sold 

absent any increases in the stringency regulations after the 2016 model year), it is important 

for the forecast to be independent of increases during 2017-2025 in the stringency of CAFE/ 

GHG standards. LMC Automotive does not use the CAFE or GHG standard as an input to 

their model, and specifically had no assumption of increase in stringency in the 2017-2025 

time frame. 

The agencies combined the LMC Automotive forecast with data from other sources to 

create the 2010 baseline reference fleet projections. This process is discussed in sections that 

follow. 

1.4.2.2 How do the agencies develop the 2010 baseline 2017-2025 reference vehicle 

fleet? 

The process of producing the MY 2010 baseline 2017-2025 reference fleet involved 

combining the baseline fleet with the projection data described above. This was a complex 

multistep procedure, which is described in this section. The procedure is new and some of the 

steps are different than those used with the MY2008 baseline fleet projection. 

1.4.2.3 How was the 2010 baseline data merged with the LMC Automotive data? 

EPA and NHTSA employed a different method from the method used in the NPRM 

for mapping certification vehicles to LMC Automotive (LMC) vehicles. Merging the 2010 

baseline data with the 2017-2025 LMC data required a thorough mapping of certification 

vehicles to LMC vehicles by individual make and model. One challenge that the agencies 

faced when determining a reference case fleet was that the sales data projected by LMC had 

different market segmentation than the data contained in EPA’s internal database. In order to 

create a common segmentation between the two databases, the agencies performed a side-byside comparison of each vehicle model in both datasets, and created an additional “NEW 

SEGMENT” modifier in the spreadsheet to map the two datasets. The reference fleet sales 

based on the “NEW SEGMENT” was then projected. 

The baseline data and reference fleet volumes are available to the public. The baseline 

Excel spreadsheet in the docket is the result of the merged files.7 The spreadsheet provides 

specific details on the sources and definitions for the data. The Excel file contains several 

tabs. They are: “Data”, “Data Tech Definitions”, “SUM”, “SUM Tech Definitions”, “Truck 

Vehicle Type Map”, and “Car Vehicle Type Map”. “Data” is the tab with the raw data. 

“Data Tech Definitions” is the tab where each column is defined and its data source named. 

“SUM” is the tab where the raw data is processed to be used in the OMEGA and Volpe 

models. The “SUM” tab minus columns A-F and minus the Generic vehicles is the input file 

for the models. The “Generic” manufacturer (shown in the “SUM” tab) is the sum of all 
 The Baseline and Reference Vehicle Fleets

 1-43 

manufacturers and is calculated as a reference, and for data verification purposes. It is used to 

validate the manufacturers’ totals. It also gives an overview of the fleet. 

Table 1-6 shows some of the unique models chosen from the “SUM” tab. A model is 

made up of a unique combination of segment and vehicle type. The number of models is 

determined by the number of unique segment and vehicle type combinations. These 

combinations of segment and vehicle type (the vehicle type number is the same as the 

technology package number) are determined by the technology packages discussed in the 

EPA RIA. “SUM Tech Definitions” is the tab where the columns of the “SUM” tab are 

defined. 

Table 1-25 Models from the SUM Tab Model 

Model

Car Like LargeSuv I4 Vehicle Type: 7

Car Like LargeSuv I4, V6 Vehicle Type: 8

Car Like LargeSuv V6 Vehicle Type: 9

Car Like MidSizeSuv I4 Vehicle Type: 7

Car Like MidSizeSuv I4, V6 Vehicle Type: 8

Car Like MidSizeSuv V6 Vehicle Type: 9

Car Like SmallSuv V6 Vehicle Type: 10

LargeAuto V6 Vehicle Type: 3

LargeAuto V6 Vehicle Type: 4

LargeAuto >=V6 Vehicle Type: 5

LargeAuto >=V8 Vehicle Type: 6

MidSizeAuto I4 Vehicle Type: 2

MidSizeAuto I4, V6 Vehicle Type: 3

MidSizeAuto V6 Vehicle Type: 4

MidSizeAuto >=V6 Vehicle Type: 5

MidSizeAuto V8 Vehicle Type: 6

In the combined EPA certification and LMC data, all 2010 vehicle models were 

assumed to continue out to 2025, though their volumes changed in proportion to LMC 

projections. Also, any new models expected to be introduced within the 2011-2025 

timeframe are not included in the data. These volumes are reassigned to the existing models 

to keep the overall fleet volume the same. All MYs 2017-2025 vehicles are mapped to the 

existing vehicles by a process of mapping to manufacturer’s future segment volumes. The 

mappings are discussed in the next section. Further discussion of this limitation is discussed 

below in section 1.4.2.4. The statistics of this fleet will be presented below since further 

modifications were required to the volumes as the next section describes. 
 The Baseline and Reference Vehicle Fleets

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1.4.2.4 How were the LMC forecast and the AEO forecast used to project the future 

fleet volumes? 

As with the comparable step in the MY 2008 baseline 2017-2025 reference fleet 

process, the next step in the agencies’ generation of the reference fleet is one of the more 

complicated steps to explain. First, the 2010 CAFE data was mapped to the LMC segments. 

Second, the breakdown of segment volumes by manufacturer was compared between the 

LMC and CAFE data sets. Third, a correction was applied for Class 2B vehicles (Large 

Pickup Trucks) in the LMC data. Fourth, the individual manufacturer segment multipliers 

were created by year. And finally, the absolute volumes of cars and trucks were normalized 

(set equal) to the total sales estimates of the Early Release of the 2012 Annual Energy 

Outlook (AEO). 

The process started with mapping the LMC segments to the CAFE data. The process 

was simple yet time consuming. The mapping required determining the LMC segment by 

looking at each of the 1171 vehicles in the LMC quarter forecast, and labeling it in the “New 

Segment” column of the new data spreadsheet. The segments were somewhat different from 

the ones employed by CSM. LMC has 27 segments and CSM has 18 segments. Table 1-26

has both the LMC Segments and the CSM segments for reference. Table 1-27 shows some of 

the Chrysler/Fiatrvehicles in the CAFE data with their “New Segment” identified. 

Table 1-26 List of LMC Segments and CSM Segments 

LMC Segments CSM Class

Compact Conventional Full-Size Car

Compact CUV Full-Size CUV

Compact MPV Full-Size Pickup

Compact Premium Conventional Full-Size SUV

Compact Premium CUV Full-Size Van

Compact Premium Sporty Luxury Car

Compact Sporty Mid-Size Car

Compact Utility Mid-Size CUV

Large Conventional Mid-Size MAV

Large Pickup Mid-Size Pickup

Large Premium Conventional Mid-Size SUV

Large Premium Sporty Mid-Size Van

Large Premium Utility Mini Car

Large Utility Small Car

Large Van Small CUV

Midsize Conventional Small MAV

Midsize CUV Small SUV

Midsize Pickup Specialty Car

Midsize Premium Conventional

Midsize Premium CUV

Midsize Premium Sporty

Midsize Premium Utility

 

r

 Chrysler/Fiat is being used as an example throughout this section to make the example calculations easier to 

follow. 
 The Baseline and Reference Vehicle Fleets

 1-45 

Midsize Sporty

Midsize Utility

Midsize Van

Table 1-27 Example of Chrysler/Fiat vehicles being mapped to segments based on the LMC Forecast 

Manufacturer Name Plate Model NEW SEGMENT

Chrysler/Fiat Chrysler 300 AWD Large Conventional

Chrysler/Fiat Chrysler PT Cruiser Compact MPV

Chrysler/Fiat Chrysler Sebring Midsize Conventional

Chrysler/Fiat Chrysler Town & Country FWD Midsize Van

Chrysler/Fiat Dodge Caliber Compact Conventional

Chrysler/Fiat Dodge Challenger Midsize Sporty

Chrysler/Fiat Dodge Charger Large Conventional

Chrysler/Fiat Dodge Dakota Pickup 2wd Midsize Pickup

Chrysler/Fiat Dodge Grand Caravan FWD Midsize Van

Chrysler/Fiat Dodge Journey 2wd Midsize CUV

In this next step, segment volume by manufacturer was compared between the LMC 

and CAFE data sets. This is necessary to determine if all of the segments a manufacturer will 

produce in the future are currently represented by the 2010 CAFE data. Almost all the future 

segments matched the current segments with the exception of some premium vs. standard 

class vehicles. In cases where there was not a vehicle model in a premium class (such as 

Compact Premium CUV) in the future, but there was a model in the standard class (Compact 

CUV), the future premium class volume was added to the standard class volume. The same 

thing was done if the opposite was true, i.e. if there was not a vehicle in a standard class (such 

as Compact CUV) in the future, but there was one in the premium class (Compact Premium 

CUV), the future standard class volume was added to the premium class volume. Table 1-28

shows the New Segments, the LMC 2010 Volumes, and the LMC 2018 Volumes for 

Chrysler/Fiat. The Compact Premium Conventional, Compact Premium CUV, and Compact 

Premium Sporty were not available from Chrysler/Fiat in 2010, but are available in 2018. As 

mentioned, the volumes from all three of those premium segments were added to the standard 

segments Compact Conventional, Compact CUV, and Compact Sporty in years were the 

premium segments were produced. 

Table 1-28 Example Chrysler/Fiat 2010 Volumes by Segment from the LMC Forecast 

NEW SEGMENT LMC 2010 Volume LMC 2018 Volume

Compact Conventional 45,082 91,136

Compact CUV 54,514 78,307

Compact MPV 9,440 61,461

Compact Premium Conventional - 35,027

Compact Premium CUV - 12,783

Compact Premium Sporty - 209

Compact Utility 166,492 210,979

Large Conventional 112,513 185,553

Large Pickup 199,652 284,583

Large Van - -
 The Baseline and Reference Vehicle Fleets

 1-46 

Midsize Conventional 89,508 88,007

Midsize CUV 48,577 91,880

Midsize Pickup 13,047 27,141

Midsize Premium Conventional - 9,309

Midsize Premium CUV - 12,476

Midsize Premium Sporty 392 3,014

Midsize Sporty 36,791 -

Midsize Utility 93,352 154,401

Midsize Van 215,598 155,408

Sub-Compact Conventional - 97,342

A step that is related to the comparison step is the filtering of Class 2b vehicles from 

the LMC forecast. LMC includes Class 2b vehicles (vans and large pickup trucks) in its lightduty forecast. Class 2b vans are all appropriately classified as MDPVs (Medium Duty 

Passenger Vehicles) and must be included in the forecast since they are regulated under the 

light-duty CAFE and GHG programs. Class 2b large pickup trucks, however, are not 

regulated under the light-duty CAFE and GHG programs (rather under the medium- and 

heavy-duty fuel efficiency and GHG programs, see 76 FR at 57120), and must therefore be 

removed from the forecast. This is accomplished by a creating a multiplier for each 

manufacturer’s large pickup trucks and applying it to each manufacturer’s large pickup truck 

volume every model year in the LMC forecast; specifically, by taking a manufacturer’s 2010 

model year large pickup CAFE volume and dividing its 2010 model year large pickup LMC 

volume. Table 1-29 shows the volumes and the resulting multiplier for Chrysler/Fiat, while 

Table 1-30 shows the 2025 LMC volume, the multiplier and the result of applying the 

multiplier to the original volume for Chrysler/Fiat.

Table 1-29 Example Values Used to Determine the Class 2b Truck Multiplier for Chrysler/Fiat 

Manufacturer NEW SEGMENT

LMC 2010 

Volume

2010 CAFE 

Volume

Truck 

Multiplier 

Chrysler/Fiat Large Pickup 199,652 120,645 0.60

Table 1-30 Example Values Used to Determine Chrysler/Fiat’s 2025 Truck Volume 

Manufacturer NEW SEGMENT

Original 2025 

Volume

Truck 

Multiplier 

2025 

Volume 

after 

Multiplier

Chrysler/Fiat Large Pickup 382,492 0.60 231,131

 After correcting for Class 2b vehicles being in the LMC forecast, it was time to create 

individual manufacturer segment multipliers to be used with the individual 2010 CAFE 

vehicle volumes to create projections for the future fleet. The individual manufacturer 
 The Baseline and Reference Vehicle Fleets

 1-47 

segment multipliers are created by dividing each year of the LMC forecast’s individual 

manufacturer segment volume by the manufacturer’s individual segment volume determined 

using 2010 CAFE data. Table 1-31 has the 2010 CAFE Volume, the 2025 LMC large pickup 

volume after Class 2b vehicles were removed, and the individual manufacturer volume for 

large pickup trucks. The multiplier is the result of dividing the 2025 volume by the 2010 

volume. 

Table 1-31 Example Values Used to Determine Chrysler/Fiat 2025 Individual Large Pickup Multiplier 

Manufacturer NEW SEGMENT 2010 Cafe Volume 2025 Volume after Multiplier

Fiat/Chrysler Individual Large Pickup 

Multiplier for 2025

Chrysler/Fiat Large Pickup 120,645 231,131 192%

 Now that the individual manufacturer segment multipliers are calculated, they can be 

applied to each vehicle in the 2010 CAFE data. The segment multipliers are applied by 

multiplying the 2010 CAFE volume for a vehicle by the multiplier for its manufacturer and 

segment. Table 1-32 shows the 2010 CAFE volumes, the individual manufacturer segment 

multipliers, and the result of multiplying the multiplier and the volume for 2025 project 

volumes for many of Chrysler/Fiat’s large pickup trucks.

Table 1-32 Example Applying the Individual Large Pickup Multiplier for Chrysler/Fiat 

Manufacturer Model NEW SEGMENT 2010 CAFE Volume

Fiat/Chrysler 

Individual Large 

Pickup Multiplier 

for 2025

2025 Project Volume 

Before AEO 

Normalization

Chrysler/Fiat Ram 1500 Pickup 2wd Large Pickup 23,686 192% 45,377

Chrysler/Fiat Ram 1500 Pickup 2wd Large Pickup 938 192% 1,797

Chrysler/Fiat Ram 1500 Pickup 2wd Large Pickup 3,029 192% 5,803

Chrysler/Fiat Ram 1500 Pickup 2wd Large Pickup 16,505 192% 31,620

Chrysler/Fiat Ram 1500 Pickup 2wd Large Pickup 7,698 192% 14,748

Chrysler/Fiat Ram 1500 Pickup 4wd Large Pickup 1,162 192% 2,226

Chrysler/Fiat Ram 1500 Pickup 4wd Large Pickup 51,417 192% 98,504

Chrysler/Fiat Ram 1500 Pickup 4wd Large Pickup 15,498 192% 29,691

Chrysler/Fiat Ram 1500 Pickup 4wd Large Pickup 712 192% 1,364

Normalizing to AEO forecast for cars and trucks must be done once the individual 

manufacturer segment multipliers have been applied to all vehicles across every year (2011-

2025) of the LMC forecast. In order to normalize a year, the number of trucks and the 

number of cars produced must be determined. Then, the truck and car totals from AEO are 

used to determine a normalizing multiplier. Table 1-33 has the 2025 car and truck totals 
 The Baseline and Reference Vehicle Fleets

 1-48 

before normalization, the 2025 AEO car and truck total, and the multipliers which are the 

result of dividing the AEO totals by totals before normalization.

Table 1-33 Example 2025 AEO Truck and Car Multipliers 

Vehicle Type 2025 Total before Normalization 2025 AEO Total

AEO 2025 

Normalizing 

Multiplier

Trucks 8,242,936 6,929,100 84%

Cars 8,954,382 9,525,700 106%

 The final step in creating the reference volumes is applying the AEO multipliers. The 

AEO multipliers are applied by vehicle type. Table 1-34 shows the normalized volume, the 

AEO 2025 truck multiplier, and the final resulting volume for a number of Chrysler/Fiat 

pickups. 

Table 1-34 Example Applying the AEO Truck Multiplier to Chrysler/Fiat Pickups 

Manufacturer Model Vehicle Type

2025 Project 

Volume Before 

AEO 

Normalization

AEO 2025 Truck 

Multiplier

2025 Project 

Volume with 

AEO 

Normalization

Chrysler/Fiat Ram 1500 Pickup 2wd Truck 45,377 84% 38,145

Chrysler/Fiat Ram 1500 Pickup 2wd Truck 1,797 84% 1,511

Chrysler/Fiat Ram 1500 Pickup 2wd Truck 5,803 84% 4,878

Chrysler/Fiat Ram 1500 Pickup 2wd Truck 31,620 84% 26,580

Chrysler/Fiat Ram 1500 Pickup 2wd Truck 14,748 84% 12,397

Chrysler/Fiat Ram 1500 Pickup 4wd Truck 2,226 84% 1,871

Chrysler/Fiat Ram 1500 Pickup 4wd Truck 98,504 84% 82,804

Chrysler/Fiat Ram 1500 Pickup 4wd Truck 29,691 84% 24,959

Chrysler/Fiat Ram 1500 Pickup 4wd Truck 1,364 84% 1,147

1.4.3 What are the sales volumes and characteristics of the MY 2010 based 

reference fleet? 

Table 1-35 and Table 1-37 below contain the sales volumes that result from the 

process above for MY 2010 and 2017-2020. 

Table 1-36 and Table 1-38 below contain the sales volumes that result from the process above 

for MY 2021-2025. 

Table 1-35 Vehicle Segment Volumesa

Reference Class

Actual and Projected Sales 

Volume

Segment 2010 2017 2018 2019 2020

Large Auto 393,049 567,514 579,808 598,784 617,135
 The Baseline and Reference Vehicle Fleets

 1-49 

Mid-Size Auto 2,189,552 3,446,643 3,413,476 3,523,692 3,577,767

Compact Auto 1,894,017 2,561,669 2,525,760 2,524,658 2,537,591

Sub-Compact Auto 1,615,536 2,258,243 2,231,633 2,161,935 2,169,551

Large Pickup 1,201,518 1,747,062 1,723,045 1,773,581 1,757,204

Small Pickup 74,780 39,095 39,793 49,185 55,481

Large SUV 2,066,629 3,259,969 3,208,284 3,157,778 3,086,726

Mid-Size SUV 1,058,340 1,068,111 1,036,455 1,058,492 1,037,464

Small SUV 113,716 148,142 143,413 142,957 142,894

Mini Van 565,527 686,492 674,803 641,731 618,567

Cargo Van 17,516 29,160 28,929 29,308 29,821

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 

Table 1-36 Vehicle Segment Volumesa 

Reference Class Projected Sales Volume

Segment 2021 2022 2023 2024 2025

Large Auto 627,571 641,252 657,367 665,152 678,652

Mid-Size Auto 3,644,746 3,684,993 3,763,193 3,819,396 3,902,811

Compact Auto 2,571,913 2,613,050 2,649,239 2,709,562 2,750,233

Sub-Compact Auto 2,188,554 2,236,339 2,256,403 2,334,855 2,359,545

Large Pickup 1,759,426 1,761,341 1,763,299 1,770,423 1,787,445

Small Pickup 58,848 62,556 66,735 71,587 75,596

Large SUV 3,067,335 3,064,546 3,043,294 3,049,618 3,064,625

Mid-Size SUV 1,026,207 1,040,034 1,031,240 1,047,527 1,052,812

Small SUV 143,576 145,165 146,476 148,201 152,103

Mini Van 612,054 607,502 599,255 600,002 599,779

Cargo Van 29,868 30,422 30,699 30,678 31,198

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 

Table 1-37 2011+ NHTSA Car and Truck Definition Based Volumes 

Vehicle Type

Actual and Projected Sales Volume

2010 2017 2018 2019 2020

Cars 7,176,330 10,213,312 10,088,966 10,139,761 10,194,353

Trucks 4,013,850 5,598,788 5,516,434 5,522,339 5,435,847

Cars and Trucks 11,190,180 15,812,100 15,605,400 15,662,100 15,630,200
 The Baseline and Reference Vehicle Fleets

 1-50 

Table 1-38 2011+ NHTSA Car and Truck Definition Based Volumes 

Vehicle Type

Projected Sales Volume

2021 2022 2023 2024 2025

Cars 10,310,594 10,455,061 10,593,727 10,811,530 10,981,082

Trucks 5,419,506 5,432,139 5,413,473 5,435,470 5,473,718

Cars and Trucks 15,730,100 15,887,200 16,007,200 16,247,000 16,454,800

Table 1-40 and Table 1-40 below contain the sales volumes by manufacturer and 

vehicle type for MY 2010 and 2017-2025. Tesla did not report any vehicle sales in 2010 so 

their projected volume is zero. Spyker/Saab sold no vehicles under the Spyker brand in 2010 

so their volume is also zero. 

Table 1-39 NHTSA Car and Truck Definition Manufacturer Volumes 

Manufacturers

Vehicl

e Type

2010 

Baseline 

Sales

2017 

Projected 

Volume

2018 

Projected 

Volume

2019 

Projected 

Volume

2020 

Projected 

Volume

All Both 11,190,180 11,190,180 15,605,400 15,662,100 15,630,200

All Cars 7,176,330 10,213,312 10,088,966 10,139,761 10,194,353

All Trucks 4,013,850 5,598,788 5,516,434 5,522,339 5,435,847

Aston Martin Cars 601 634 617 620 620

Aston Martin Trucks - - - - -

BMW Cars 143,638 320,634 318,821 327,091 329,304

BMW Trucks 26,788 106,150 104,625 105,104 101,805

Chrysler/Fiat Cars 496,998 728,817 736,022 769,256 786,344

Chrysler/Fiat Trucks 665,806 774,065 743,375 749,206 740,640

Daimler Cars 157,453 252,820 240,222 245,807 245,888

Daimler Trucks 72,393 99,125 108,510 108,294 108,598

Ferrari Cars 1,780 1,878 1,828 1,836 1,837

Ferrari Trucks - - - - -

Ford Cars 940,241 1,348,543 1,347,544 1,341,628 1,347,596

Ford Trucks 858,798 1,035,400 1,023,955 1,016,328 995,702

Geely Cars 28,223 60,422 57,655 60,338 60,040

Geely Trucks 29,719 35,087 32,438 33,299 32,149

General Motors Cars 1,010,524 1,652,946 1,616,449 1,611,415 1,612,666

General Motors Trucks 735,367 1,213,192 1,201,479 1,217,167 1,211,435

Honda Cars 845,318 1,122,558 1,139,856 1,147,055 1,167,627

Honda Trucks 390,028 536,998 525,327 527,814 517,268

Hyundai Cars 375,656 865,069 849,727 857,497 861,062

Hyundai Trucks 35,360 131,912 127,289 122,193 118,265

Kia Cars 226,157 345,314 339,180 328,872 327,694
 The Baseline and Reference Vehicle Fleets

 1-51 

Kia Trucks 21,721 43,374 43,209 41,648 40,270

Lotus Cars 354 374 364 365 365

Lotus Trucks - - - - -

Mazda Cars 249,489 254,270 249,048 247,203 248,350

Mazda Trucks 61,451 59,862 59,114 55,108 53,334

Mitsubishi Cars 54,263 61,058 58,152 60,387 60,619

Mitsubishi Trucks 9,146 13,701 13,840 14,276 14,262

Nissan Cars 619,918 889,039 867,771 873,076 874,098

Nissan Trucks 255,566 305,943 306,537 309,179 304,196

Porsche Cars 11,937 18,430 18,138 17,255 17,065

Porsche Trucks 3,978 20,105 19,647 19,573 18,851

Spyker/Saab Cars - - - - -

Spyker/Saab Trucks - - - - -

Subaru Cars 184,587 209,137 205,550 205,868 205,749

Subaru Trucks 73,665 96,938 94,441 92,177 90,751

Suzuki Cars 25,002 43,253 42,515 43,399 44,081

Suzuki Trucks 3,938 3,399 3,347 3,690 3,676

Tata/JLR Cars 11,279 28,012 27,188 28,194 28,430

Tata/JLR Trucks 37,475 54,033 53,423 52,682 51,461

Tesla Cars - - - - -

Tesla Trucks - - - - -

Toyota Cars 1,508,866 1,528,208 1,501,492 1,509,270 1,515,051

Toyota Trucks 696,324 966,417 955,281 951,691 932,267

Volkswagen Cars 284,046 481,894 470,826 463,329 459,868

Volkswagen Trucks 36,327 103,088 100,596 102,910 100,916

Table 1-40 NHTSA Car and Truck Definition Manufacturer Volumes 

Manufacturers Vehicle Type

2021 

Projected 

Volume

2022

Projected 

Volume

2023

Projected 

Volume

2024

Projected 

Volume

2025

Projected 

Volume

All Both 15,730,100 15,887,200 16,007,200 16,247,000 16,454,800

All Cars 10,310,594 10,455,061 10,593,727 10,811,530 10,981,082

All Trucks 5,419,506 5,432,139 5,413,473 5,435,470 5,473,718

Aston Martin Cars 623 626 630 634 639

Aston Martin Trucks - - - - -

BMW Cars 335,753 341,613 346,903 357,948 363,380

BMW Trucks 101,238 100,345 99,084 101,174 101,013

Chrysler/Fiat Cars 805,113 828,656 850,402 877,751 899,843

Chrysler/Fiat Trucks 733,257 735,937 731,269 722,213 726,403

Daimler Cars 249,219 251,461 253,688 258,742 261,242

Daimler Trucks 110,235 112,133 113,550 116,867 119,090

Ferrari Cars 1,845 1,853 1,865 1,878 1,894

Ferrari Trucks - - - - -

Ford Cars 1,359,990 1,377,947 1,394,907 1,418,568 1,441,350

Ford Trucks 990,243 990,827 985,782 991,767 997,694
 The Baseline and Reference Vehicle Fleets

 1-52 

Geely Cars 61,433 62,399 63,076 65,157 65,883

Geely Trucks 31,977 31,598 31,007 31,796 31,528

General Motors Cars 1,624,561 1,638,066 1,652,324 1,676,558 1,696,474

General Motors Trucks 1,218,265 1,226,184 1,232,502 1,244,178 1,261,546

Honda Cars 1,187,756 1,212,900 1,238,278 1,267,745 1,295,234

Honda Trucks 512,800 515,656 509,628 505,534 504,020

Hyundai Cars 873,625 887,004 899,936 918,938 935,619

Hyundai Trucks 117,565 116,208 115,339 116,430 117,662

Kia Cars 330,416 335,846 338,791 346,828 350,765

Kia Trucks 39,205 38,857 38,203 38,034 37,957

Lotus Cars 367 368 371 374 377

Lotus Trucks - - - - -

Mazda Cars 249,288 252,522 254,751 259,488 262,732

Mazda Trucks 52,946 52,752 52,158 52,998 53,183

Mitsubishi Cars 61,785 63,390 63,937 67,026 67,925

Mitsubishi Trucks 14,307 14,778 14,824 15,229 15,464

Nissan Cars 879,450 884,816 893,622 907,823 919,920

Nissan Trucks 303,616 304,381 304,703 308,510 312,005

Porsche Cars 17,289 17,216 17,292 17,517 17,609

Porsche Trucks 18,863 18,598 18,562 18,861 19,091

Spyker Cars - - - - -

Spyker Trucks - - - - -

Subaru Cars 206,863 209,828 211,621 215,567 218,870

Subaru Trucks 91,673 91,940 92,337 94,300 96,326

Suzuki Cars 44,765 45,769 46,590 47,824 48,710

Suzuki Trucks 3,760 3,879 3,939 4,085 4,173

Tata/JLR Cars 28,977 29,416 29,898 30,546 30,949

Tata/JLR Trucks 50,984 50,767 50,280 50,340 50,369

Tesla Cars - - - - -

Tesla Trucks - - - - -

Toyota Cars 1,530,699 1,548,354 1,567,676 1,598,715 1,622,242

Toyota Trucks 927,227 925,277 918,749 918,479 921,183

Volkswagen Cars 460,777 465,011 467,170 475,903 479,423

Volkswagen Trucks 101,344 102,022 101,558 104,673 105,009

Table 1-41 also shows how the change in fleet make-up may affect the footprint 

distributions over time. The resulting data indicate that footprint will not change significantly 

between 2010 and 2025. The footprints are somewhat larger than in the 2008 based fleet 

projection (Table 1-19).

Table 1-41 Production Weighted Foot Print Mean 

Model Average Footprint of all Average Footprint Average Footprint 
 The Baseline and Reference Vehicle Fleets

 1-53 

Year Vehicles Cars Trucks

2010 48.6 45.2 54.5

2017 48.7 45.4 54.9

2018 48.8 45.4 54.9

2019 48.8 45.5 55.0

2020 48.8 45.5 55.0

2021 48.8 45.5 55.0

2022 48.7 45.5 55.0

2023 48.7 45.5 55.0

2024 48.6 45.5 54.9

2025 48.6 45.5 55.0

Table 1-42 below shows the changes in engine cylinders over the model years. The 

current assumptions show that engines will increase in size between 2010 and 2017 and then 

remain relatively constant over the model years to which these final rules apply. 

Table 1-42 Percentages of 4, 6, 8 Cylinder Engines by Model Year 

Trucks Cars

Model 4 

Cylinders

6 

Cylinders

8 

Cylinders

4 

Cylinders

6 

Cylinders

8 

Year Cylinders

2010 15.7% 52.5% 31.8% 69.2% 26.6% 4.1%

2017 13.9% 50.2% 35.9% 66.3% 29.0% 4.7%

2018 13.7% 50.3% 36.0% 66.2% 29.1% 4.7%

2019 13.6% 50.0% 36.4% 65.7% 29.6% 4.7%

2020 13.5% 49.9% 36.7% 65.7% 29.6% 4.7%

2021 13.4% 49.8% 36.8% 65.7% 29.6% 4.7%

2022 13.5% 49.7% 36.8% 65.8% 29.5% 4.8%

2023 13.5% 49.6% 36.9% 65.7% 29.5% 4.8%

2024 13.7% 49.6% 36.8% 65.9% 29.4% 4.7%

2025 13.6% 49.5% 36.8% 65.9% 29.4% 4.8%
 The Baseline and Reference Vehicle Fleets

 1-54 

1.5 What are the differences in the sales volumes and characteristics of the MY 2008 

based and the MY 2010 based reference fleets? 

 This section compares some of the differences between the fleet based on MY 2008 

CAFE and the fleet based on MY 2010 CAFE data. As stated before, the 2008 fleet 

projection is based on MY 2008 CAFE data, a long range forecast provided by CSM, and 

interim AEO 2011. The 2010 fleet projection is based on MY 2010 CAFE, a long range 

forecast provided by LMC Automotive, and interim AEO 2012. 

Table 1-43, Table 1-44, Table 1-45 and Table 1-46 below contain the sales volume 

differences between the two fleets, from subtracting the 2008 MY based fleet projection from 

the 2010 MY based fleet projection. 

The sales in MY 2010 are significantly lower (by 2,661,581 vehicles) than in MY 

2008 (reflecting the continued economic recession, as noted earlier). The sales in MY 2010 

are depressed but sales are expected to recover to their MY 2008 levels before 2017. 

 There is an increase in the number of large trucks, midsize autos, and large autos by 

2025. There is also decreased volume in the remaining segment in 2025. These differences 

are due to the LMC forecast and the newer AEO projection. 

Table 1-43 Vehicle Segment Volumes Differencesa 

Reference Class 

Segment

Actual 

Sales 

Volume Projected Sales Volume

2010-2008 2017 2018 2019 2020

LargeAuto -169,191 191,407 223,040 245,175 222,271

MidSizeAuto -909,375 135,375 123,068 220,071 195,982

CompactAuto -85,444 213,689 200,367 155,357 89,570

SubCmpctAuto 249,703 -199,979 -222,479 -327,273 -383,799

LargePickup -380,708 232,443 279,279 390,391 371,009

SmallPickup -102,717 -117,132 -118,139 -111,567 -90,548

LargeSUV -717,320 65,480 58,183 -20,090 -116,518

MidSizeSUV -205,020 -290,644 -272,757 -208,902 -248,358

SmallSUV -171,639 -109 -6,520 -11,718 -19,783

MiniVan -76,528 -68,070 -64,748 -75,334 -95,756

CargoVan -93,342 -156,681 -170,305 -172,666 -189,807

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 
 The Baseline and Reference Vehicle Fleets

 1-55 

Table 1-44 Vehicle Segment Volumes Differencesa 

Reference Class 

Segment

Projected Sales Volume

2021 2022 2023 2024 2025

LargeAuto 247,379 282,957 294,695 308,979 309,809

MidSizeAuto 202,630 136,730 70,660 67,900 87,870

CompactAuto 50,936 20,851 16,313 -35,072 -92,836

SubCmpctAuto -437,810 -450,828 -464,699 -461,206 -518,743

LargePickup 391,125 411,920 462,006 498,672 527,056

SmallPickup -91,275 -84,582 -84,580 -83,040 -79,242

LargeSUV -245,579 -298,062 -369,459 -426,255 -456,367

MidSizeSUV -255,033 -243,210 -237,048 -245,135 -252,550

SmallSUV -23,647 -24,478 -23,763 -24,990 -23,610

MiniVan -117,024 -131,480 -141,530 -120,718 -126,477

CargoVan -180,671 -172,390 -170,886 -166,222 -170,570

a

 Volumes in this table are based on the pre-2011 NHTSA definition of Cars and Trucks. 

Table 1-45 2011+ NHTSA Car and Truck Definition Based Volumes Differences 

Vehicle Type

Actual Sales 

Volume Projected Sales Volume

2010 - 2008 2017 2018 2019 2020

Cars -1,054,238 225,645 183,602 144,065 -97,209

Trucks -1,607,343 -219,867 -154,612 -60,623 -168,530

Cars and Trucks -2,661,581 5,778 28,990 83,442 -265,739

Table 1-46 2011+ NHTSA Car and Truck Definition Based Volumes Differences 

Vehicle Type

Projected Sales Volume

2021 2022 2023 2024 2025

Cars -194,571 -280,716 -374,276 -446,608 -560,478

Trucks -264,396 -271,857 -274,013 -240,479 -235,181

Cars and Trucks -458,966 -552,572 -648,289 -687,087 -795,659
 The Baseline and Reference Vehicle Fleets

 1-56 

Table 1-47 and Table 1-48 below contain the differences in sales volumes by 

manufacturer and vehicle type between the 2008 MY based fleet and the 2010 MY based 

fleet. Table 1-48 shows that Chrysler/Fiat cars and trucks, Ford trucks, Hyundai cars, and 

Porsche trucks are projected to have significant increases in volume in MY 2025, though 

Table 1-48 also shows the market down overall in MY 2025 by 795,659 vehicles. 

Table 1-47 NHTSA Car and Truck Definition Manufacturer Volumes Differences 

Manufacturers Vehicle Type

2010-2008 

Difference 

in Sales

2017 

Difference 

in Volume

2018 

Difference 

in Volume

2019 

Difference 

in Volume

2020 

Difference 

in Volume

All Both -2,661,581 -4,616,142 28,990 83,442 -265,739

All Cars -1,054,238 225,645 183,602 144,065 -97,209

All Trucks -1,607,343 -219,867 -154,612 -60,623 -168,530

Aston Martin Cars -769 -401 -434 -452 -414

Aston Martin Trucks NA NA NA NA NA

BMW Cars -148,158 7,612 -4,118 -18,984 -28,638

BMW Trucks -34,536 -31,903 -27,317 -26,269 -26,534

Chrysler/Fiat Cars -206,160 310,054 338,484 377,567 371,025

Chrysler/Fiat Trucks -290,986 364,363 355,517 382,759 379,963

Daimler Cars -50,742 -32,027 -36,187 -35,618 -45,101

Daimler Trucks -6,742 12,212 24,859 20,106 15,679

Ferrari Cars 330 -4,798 -4,872 -4,958 -5,079

Ferrari Trucks NA NA NA NA NA

Ford Cars -16,458 48,644 36,077 9,589 -31,193

Ford Trucks 44,604 271,851 275,126 298,555 278,665

Geely/Volvo Cars -4,525 18,535 15,468 17,213 17,425

Geely/Volvo Trucks -35,930 -53,147 -56,956 -58,276 -60,854

GM Cars -497,273 290,185 178,094 106,390 81,911

GM Trucks -852,024 -249,012 -272,597 -276,344 -333,548

HONDA Cars -161,321 -32,042 1,769 2,416 3,961

HONDA Trucks -115,112 -59,483 -19,292 279 -7,821

HYUNDAI Cars 37,787 273,042 271,354 274,526 262,779

HYUNDAI Trucks -17,798 -20,973 -24,172 -33,449 -35,908

Kia Cars 4,177 23,270 26,810 13,993 4,018

Kia Trucks -37,751 -55,328 -55,071 -59,031 -56,265

Lotus Cars 102 134 121 115 99

Lotus Trucks NA NA NA NA NA

Mazda Cars 2,828 730 -13,464 -19,748 -21,728

Mazda Trucks 5,566 8,074 1,579 -2,386 -4,820

Mitsubishi Cars -31,095 -4,041 -5,519 -3,439 -4,461

Mitsubishi Trucks -6,225 -23,931 -22,460 -21,178 -20,953
 The Baseline and Reference Vehicle Fleets

 1-57 

Nissan Cars -97,951 18,242 18,093 18,676 -8,693

Nissan Trucks -49,980 -138,995 -105,846 -89,380 -93,673

PORSCHE Cars -6,972 -16,663 -17,306 -18,861 -18,898

PORSCHE Trucks -14,819 6,872 7,646 8,104 7,710

Spyker/Saab Cars NA NA NA NA NA

Spyker/Saab Trucks NA NA NA NA NA

Subaru Cars 68,552 -14,975 -11,048 -11,227 -17,717

Subaru Trucks -8,881 18,696 19,289 19,345 18,293

Suzuki Cars -54,337 -47,455 -47,417 -47,169 -49,467

Suzuki Trucks -31,381 -18,710 -18,038 -17,002 -16,999

Tata/JLR Cars 1,683 -27,869 -29,034 -29,073 -29,752

Tata/JLR Trucks -18,109 -3,546 -3,183 -5,172 -4,752

Tesla Cars NA NA NA NA NA

Tesla Trucks NA NA NA NA NA

Toyota Cars 248,502 -320,988 -332,689 -327,036 -368,683

Toyota Trucks -254,812 -364,094 -268,134 -190,413 -222,037

Volkswagen Cars -7,437 -69,744 -69,210 -73,785 -94,954

Volkswagen Trucks 9,328 -25,731 -44,895 -43,981 -45,784

Table 1-48 NHTSA Car and Truck Definition Manufacturer Volumes Differences 

Manufacturers Vehicle Type

2021

Difference 

in Volume

2022

Difference 

in Volume

2023

Difference 

in Volume

2024

Difference 

in Volume

2025

Difference 

in Volume

All Both -458,966 -552,572 -648,289 -687,087 -795,659

All Cars -194,571 -280,716 -374,276 -446,608 -560,478

All Trucks -264,396 -271,857 -274,013 -240,479 -235,181

Aston Martin Cars -435 -423 -411 -507 -543

Aston Martin Trucks NA NA NA NA NA

BMW Cars -23,345 -18,421 -13,658 -30,245 -41,876

BMW Trucks -27,486 -28,554 -28,437 -45,351 -44,396

Chrysler/Fiat Cars 384,100 404,483 426,520 451,734 463,364

Chrysler/Fiat Trucks 384,644 372,929 370,205 377,251 394,641

Daimler Cars -51,159 -53,277 -58,819 -73,595 -79,477

Daimler Trucks 10,786 11,198 8,235 9,783 18,023

Ferrari Cars -5,214 -5,285 -5,362 -5,563 -5,764

Ferrari Trucks NA NA NA NA NA

Ford Cars -41,627 -37,274 -79,890 -85,102 -98,759

Ford Trucks 276,062 276,561 285,777 302,913 313,218

Geely/JLR Cars 19,665 20,713 21,045 22,696 23,295

Geely/JLR Trucks -60,749 -60,914 -65,833 -67,385 -69,579

GM Cars 94,541 130,413 155,505 182,961 172,466

GM Trucks -346,012 -352,372 -373,993 -392,627 -412,390
 The Baseline and Reference Vehicle Fleets

 1-58 

HONDA Cars -11,124 -24,604 -27,286 -40,106 -45,087

HONDA Trucks -23,116 -23,579 -27,270 -31,460 -53,677

HYUNDAI Cars 260,270 259,040 265,628 261,228 258,369

HYUNDAI Trucks -38,901 -41,285 -45,850 -49,662 -50,474

Kia Cars -903 -3,256 -3,955 -5,054 -12,018

Kia Trucks -56,227 -55,837 -57,485 -58,085 -59,696

Lotus Cars 89 78 72 66 61

Lotus Trucks NA NA NA NA NA

Mazda Cars -25,452 -28,628 -42,159 -41,126 -44,072

Mazda Trucks -6,281 -7,555 -9,808 -8,973 -8,185

Mitsubishi Cars -4,066 -3,871 -3,743 -3,702 -5,380

Mitsubishi Trucks -21,002 -20,449 -20,645 -20,772 -20,923

Nissan Cars -33,179 -52,631 -60,718 -74,948 -94,855

Nissan Trucks -104,413 -107,502 -112,418 -113,707 -114,449

PORSCHE Cars -19,186 -19,391 -19,701 -21,987 -23,087

PORSCHE Trucks 7,621 7,213 7,192 7,452 7,872

Spyker/Saab Cars NA NA NA NA NA

Spyker/Saab Trucks NA NA NA NA NA

Subaru Cars -23,917 -28,785 -29,991 -32,716 -38,100

Subaru Trucks 18,900 19,204 19,315 20,158 21,604

Suzuki Cars -50,960 -51,830 -52,673 -52,623 -54,444

Suzuki Trucks -17,007 -16,855 -16,864 -17,077 -17,201

Tata/JLR Cars -29,700 -29,933 -30,741 -33,182 -34,469

Tata/JLR Trucks -7,169 -7,823 -8,585 -7,641 -6,436

Tesla Cars NA NA NA NA NA

Tesla Trucks NA NA NA NA NA

Toyota Cars -373,007 -437,723 -469,316 -481,813 -485,811

Toyota Trucks -288,312 -309,775 -306,231 -289,534 -288,833

Volkswagen Cars -124,830 -128,303 -129,579 -129,433 -150,740

Volkswagen Trucks -47,390 -44,728 -52,369 -52,266 -49,275

Table 1-49 shows the difference in footprint distributions between the 2010 based fleet 

projection and the 2008 based fleet projection. The differences between MYs 2010 and 2008 

are small and are just the result of the manufacturers’ product mix in those model years. MY 

2025 shows an increase in both the average truck and average car footprints. This is due to 

the increased number of large cars and large trucks forecast in the 2010 based fleet projection. 

Also, in several MYs, the change in the average footprint of all vehicles is outside the range 

between the changes in the corresponding car and truck fleets. This is due to production 

weighting. Because the total numbers of cars and trucks differs, production weighting can 

affect the average for the whole fleet as compared to the averages for cars and trucks. This 

can cause a counterintuitive effect when taking the difference of the averages. 
 The Baseline and Reference Vehicle Fleets

 1-59 

Table 1-49 Production Weighted Foot Print Mean Difference* 

Model 

Year

Average Footprint of all 

Vehicles

Average Footprint 

Cars

Average Footprint 

Trucks

2010-2008 48.6 - 48.8 = -0.2 45.2 – 45.2 = 0.0 54.5 - 53.9 = 0.6

2017 48.7 - 48.0 = 0.7 45.4 - 44.6 = 0.8 54.9 - 53.8 = 1.1

2018 48.8 - 47.9 = 0.9 45.4 - 44.6 = 0.8 54.9 - 53.7 = 1.2

2019 48.8 - 47.8 = 1.0 45.5 - 44.6 = 0.9 55.0 - 53.6 = 1.4

2020 48.8 - 47.8 = 1.0 45.5 - 44.6 = 0.9 55.0 - 53.7 = 1.3

2021 48.8 - 47.7 = 1.0 45.5 - 44.6 = 0.9 55.0 - 53.6 = 1.4

2022 48.7 - 47.7 = 1.0 45.5 - 44.6 = 0.9 55.0 - 53.6 = 1.4

2023 48.7 - 47.7 = 1.0 45.5 - 44.6 = 0.9 55.0 - 53.5 = 1.5

2024 48.6 - 47.5 = 1.1 45.5 - 44.6 = 0.9 54.9 - 53.3 = 1.6

2025 48.6 - 47.5 = 1.1 45.5 - 44.6 = 0.9 55.0 - 53.3 = 1.7

*Note: This table is the difference calculated from Table 1-19 and Table 1-41. 

Table 1-50 shows the difference in engine cylinders distribution between the 2010 MY 

based fleet and the 2008 MY based fleet. MY 2010 has fewer vehicles with 6 cylinder 

engines. Fewer 6 cylinders in the baseline fleet along with vehicle mix changes results in 

more 4 and 8 cylinder engines in trucks and more 4 cylinder cars by 2025. 

Table 1-50 Differences in Percentages of 4, 6, 8 Cylinder Engines by Model Year 

Trucks Cars

Model 4 

Cylinders

6 

Cylinders

8 

Cylinders

4 

Cylinders

6 

Cylinders

8 

Year Cylinders

2010-2008 5.40% -3.90% -1.50% 12.30% -11.20% -1.20%

2017 3.00% -13.50% 10.50% 5.70% -5.50% -0.30%

2018 3.10% -14.20% 11.20% 5.50% -5.30% -0.30%

2019 3.20% -15.50% 12.30% 5.00% -4.70% -0.30%

2020 3.20% -15.70% 12.60% 5.40% -5.10% -0.30%

2021 3.10% -16.50% 13.40% 5.10% -4.80% -0.20%

2022 3.20% -17.00% 13.80% 4.70% -4.70% 0.00%

2023 3.20% -18.10% 14.90% 4.80% -4.80% 0.00%

2024 3.20% -18.50% 15.40% 4.90% -4.70% -0.10%

2025 3.10% -18.70% 15.50% 4.80% -4.60% 0.00%
 The Baseline and Reference Vehicle Fleets

 1-60 

References:

1

EPA’s Omega Model and input sheets are available at 

http://www.epa.gov/oms/climate/models.htm; DOT/NHTSA’s CAFE Compliance and Effects 

Modeling System (commonly known as the “Volpe Model”) and input and output sheets are 

available at http://www.nhtsa.gov/fuel-economy. It is also available in the docket (Docket 

EPA-HQ-OAR-2010-0799) 

2

http://www.nhtsa.gov/Laws+&+Regulations/CAFE+-

+Fuel+Economy/CAFE+Compliance+and+Effects+Modeling+System:+The+Volpe+Model 

3 Department of Energy, Energy Information Administration, Annual Energy Outlook (AEO) 

2011, Early Release. Available at http://www.eia.gov/forecasts/aeo/. 

4

The baseline Excel file (“2008-2025 Production Summary Data _Definitions Docket 

08_27_2009”) is available in the docket (Docket EPA-HQ-OAR-2010-0799). 

5

http://www.nhtsa.gov/Laws+&+Regulations/CAFE+-

+Fuel+Economy/CAFE+Compliance+and+Effects+Modeling+System:+The+Volpe+Model 

6 Department of Energy, Energy Information Administration, Annual Energy Outlook (AEO) 

20112012, Early Release. Available at http://www.eia.gov/forecasts/aeo/ (last accessed Aug. 

15, 2011April 9, 2012). 

7

The baseline Excel file (“2010-2025 Production Summary Data_Definitions Docket 

05.01.2012”) is available in the docket (Docket EPA-HQ-OAR-2010-0799). 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-1 

Chapter 2: What are the Attribute-Based Curves the Agencies 

are Adopting, and How Were They Developed? 

2.1 Why are standards attribute-based and defined by a mathematical function? 

As in the MYs 2012-2016 CAFE/GHG rules, and as NHTSA did in the MY 2011 

CAFE rule, NHTSA and EPA are promulgating attribute-based CAFE and CO2 standards that 

are defined by a mathematical function. EPCA, as amended by EISA, expressly requires that 

CAFE standards for passenger cars and light trucks be based on one or more vehicle attributes 

related to fuel economy, and be expressed in the form of a mathematical function.1 The CAA 

has no such requirement, although such an approach is permissible under section 202 (a) and 

EPA has used the attribute-based approach in issuing standards under both section 202 (a) and 

under analogous provisions of the CAA (e.g., criteria pollutant standards for non-road diesel 

engines using engine size as the attribute,2

in the recent GHG standards for heavy duty 

pickups and vans using a work factor attribute,3and in the MYs 2012-2016 GHG rule which 

used vehicle footprint as the attribute). Public comments on the MYs 2012-2016 rulemaking 

widely supported attribute-based standards for both agencies’ standards. Comments received 

on the MY 2017 and later proposal also generally supported an attribute-based standard, as 

further discussed in section 2.2.

Under an attribute-based standard, every vehicle model has a performance target (fuel 

economy and CO2 emissions for CAFE and CO2 emissions standards, respectively), the level 

of which depends on the vehicle’s attribute (for this rule, footprint, as discussed below). The 

manufacturers’ fleet average performance is determined by the production-weighteda

average 

(for CAFE, harmonic average) of those targets. 

The agencies believe that an attribute-based standard is preferable to a single-industrywide average standard in the context of CAFE and CO2 standards for several reasons. First, if

the shape is chosen properly, every manufacturer is more likely to be required to continue 

adding more fuel efficient technology each year across their fleet, because the stringency of 

the compliance obligation will depend on the particular product mix of each manufacturer. 

Therefore a maximum feasible attribute-based standard will tend to require greater fuel 

savings and CO2 emissions reductions overall than would a maximum feasible flat standard 

(that is, a single mpg or CO2 level applicable to every manufacturer). 

Second, depending on the attribute, attribute-based standards reduce the incentive for 

manufacturers to respond to CAFE and CO2 standards in ways harmful to safety.

b

 Because 

 

a

 Production for sale in the United States. 

b

 The 2002 NAS Report described at length and quantified the potential safety problem with average fuel 

economy standards that specify a single numerical requirement for the entire industry. See 2002 NAS Report at 

5, finding 12. Ensuing analyses, including by NHTSA, support the fundamental conclusion that standards 

structured to minimize incentives to downsize all but the largest vehicles will tend to produce better safety 

outcomes than flat standards.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-2 

each vehicle model has its own target (based on the attribute chosen), properly fitted attributebased standards provide little, if any, incentive to build smaller vehicles simply to meet a 

fleet-wide average, because the smaller vehicles will be subject to more stringent compliance 

targets.c

Third, attribute-based standards provide a more equitable regulatory framework for 

different vehicle manufacturers.d A single industry-wide average standard imposes 

disproportionate cost burdens and compliance difficulties on the manufacturers that need to 

change their product plans to meet the standards, and puts no obligation on those 

manufacturers that have no need to change their plans. As discussed above, attribute-based 

standards help to spread the regulatory cost burden for fuel economy more broadly across all 

of the vehicle manufacturers within the industry. 

Fourth, attribute-based standards better respect economic conditions and consumer 

choice, as compared to single-value standards. A flat, or single value, standard encourages a 

certain vehicle size fleet mix by creating incentives for manufacturers to use vehicle 

downsizing as a compliance strategy. Under a footprint-based standard, manufacturers have 

greater incentive (compared to under a flat standard) to invest in technologies that improve 

the fuel economy of the vehicles they sell rather than shifting product mix, because reducing 

the size of the vehicle is generally a less viable compliance strategy given that smaller 

vehicles have more stringent regulatory targets. 

2.2 What attribute are the agencies adopting, and why? 

As in the MYs 2012-2016 CAFE/GHG rules, and as NHTSA did in the MY 2011 

CAFE rule, NHTSA and EPA are promulgating CAFE and CO2 standards that are based on 

vehicle footprint, which has an observable correlation to fuel economy and emissions. There 

are several policy and technical reasons why NHTSA and EPA believe that footprint is the 

most appropriate attribute on which to base the standards for the vehicles covered by this 

rulemaking, even though some other light-duty vehicle attributes (notably curb weight) are 

better correlated to fuel economy and emissions. 

First, in the agencies’ judgment, from the standpoint of vehicle safety, it is important 

that the CAFE and CO2 standards be set in a way that does not encourage manufacturers to 

respond by selling vehicles that are less safe. NHTSA’s research of historical crash data has 

found that reductions in vehicle size and reductions in the mass of lighter vehicles tend to 

compromise overall highway safety, while reductions in the mass of heavier vehicles tend to 

improve overall highway safety. If footprint-based standards are defined in a way that creates 

relatively uniform burden for compliance for vehicles of all sizes, then footprint-based 

standards will not incentivize manufacturers to downsize their fleets as a strategy for 

 

c

 Assuming that the attribute is related to vehicle size. 

d

Id. at 4-5, finding 10. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-3 

compliance which could compromise societal safety, or to upsize their fleets which might 

reduce the program’s fuel savings and GHG emission reduction benefits. Footprint-based 

standards also enable manufacturers to apply weight-efficient materials and designs to their 

vehicles while maintaining footprint, as an effective means to improve fuel economy and 

reduce GHG emissions. On the other hand, depending on their design, weight-based 

standards can create disincentives for manufacturers to apply weight-efficient materials and 

designs. This is because weight-based standards would become more stringent as vehicle 

mass is reduced. The agencies discuss mass reduction and its relation to safety in more detail 

in Preamble section II.G. 

Further, although we recognize that weight is better correlated with fuel economy and 

CO2 emissions than is footprint, we continue to believe that there is less risk of “gaming” 

(changing the attribute(s) to achieve a more favorable target) by increasing footprint under 

footprint-based standards than by increasing vehicle mass under weight-based standards—it is 

relatively easy for a manufacturer to add enough weight to a vehicle to decrease its applicable 

fuel economy target a significant amount, as compared to increasing vehicle footprint. We 

also continue to agree with concerns raised in 2008 by some commenters on the MY 2011 

CAFE rulemaking that there would be greater potential for gaming under multi-attribute 

standards, such as those that also depend on weight, torque, power, towing capability, and/or 

off-road capability. The agencies agree with the assessment first presented in NHTSA’s MY 

2011 CAFE final rule4that the possibility of gaming an attribute-based standard is lowest 

with footprint-based standards, as opposed to weight-based or multi-attribute-based standards. 

Specifically, standards that incorporate weight, torque, power, towing capability, and/or offroad capability in addition to footprint would not only be more complex, but by providing 

degrees of freedom with respect to more easily-adjusted attributes, they could make it less 

certain that the future fleet would actually achieve the average fuel economy and CO2

reduction levels projected by the agencies.e

This is not to say that a footprint-based system 

will eliminate gaming, or that a footprint-based system will eliminate the possibility that 

manufacturers will change vehicles in ways that compromise occupant protection. In the 

agencies’ judgment, footprint-based standards achieved the best balance among affected 

considerations. 

The agencies recognize that based on economic and consumer demand factors that are 

external to this rule, the distribution of footprints in the future may be different (either smaller 

or larger) than what is projected in this rule. However, the agencies continue to believe that 

there will not be significant shifts in this distribution as a direct consequence of this rule. We 

note that comments by CBD, ACEEE, and NACAA referenced a 2011 study by Whitefoot 

and Skerlos, “Design incentives to increase vehicle size created from the U.S. footprint-based 

fuel economy standards.”f

 This study concluded that the proposed MY 2014 standards 

 

eHowever, for heavy-duty pickups and vans not covered by today’s standards, the agencies determined that use 

of footprint and work factor as attributes for heavy duty pickup and van GHG and fuel consumption standards 

could reasonably avoid excessive risk of gaming. See 76 FR 57106, 57161-62 (Sept. 15, 2011) 

f Available at Docket ID: EPA-HQ-OAR-2010-0799. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-4 

“create an incentive to increase vehicle size except when consumer preference for vehicle size 

is near its lower bound and preference for acceleration is near its upper bound.”g The 

commenters who cited this study generally did so as part of arguments in favor of flatter 

standards (i.e., curves that are flatter across the range of footprints) for MYs 2017-2025. 

While the agencies consider the concept of the Whitefoot and Skerlos analysis to have some 

potential merits, it is also important to note that, among other things, the authors assumed 

different inputs than the agencies actually used in the MYs 2012-2016 rules regarding the 

baseline fleet, the cost and efficacy of potential future technologies, and the relationship 

between vehicle footprint and fuel economy. Were the agencies to use the Whitefoot and 

Skerlos methodology (e.g., methods to simulate manufacturers’ potential decisions to increase 

vehicle footprint) with the actual inputs to the MYs 2012-2016 rules, the agencies would 

likely obtain different findings. Underlining the potential uncertainty, considering a range of 

scenarios, the authors obtained a wide range of results in their analyses. The agencies discuss 

this study more fully in the Section II of the preamble, the NHTSA RIA, and the EPA 

response to comments document. 

The agencies also recognize that some international attribute-based standards use 

attributes other than footprint and that there could be benefits for a number of manufacturers 

if there was greater international harmonization of fuel economy and GHG standards for lightduty vehicles, but this is largely a question of how stringent standards are and how they are 

tested and enforced. It is entirely possible that footprint-based and weight-based systems can 

coexist internationally and not present an undue burden for manufacturers if they are carefully 

crafted. Different countries or regions may find different attributes appropriate for basing 

standards, depending on the particular challenges they face—from fuel prices, to family size 

and land use, to safety concerns, to fleet composition and consumer preference, to other 

environmental challenges besides climate change. The agencies anticipate working more 

closely with other countries and regions in the future to consider how to address these issues 

in a way that least burdens manufacturers while respecting each country’s need to meet its 

own particular challenges. 

In the proposal, the agencies found that footprint was the most appropriate attribute 

upon which to base the proposed standards. Recognizing strong public interest in this issue, 

the agencies sought comment on whether a different attribute or combination of attributes 

should be considered in setting standards for the final rule. The agencies specifically 

requested that the commenters address the concerns raised in the proposal regarding the use of 

other attributes, and explain how standards should be developed using the other attribute(s) in 

a way that contributes more to fuel savings and CO2 reductions than the footprint-based 

standards, without compromising safety. 

The agencies received several comments regarding the attribute(s) upon which new 

CAFE and GHG standards should be based. NADAhand the Consumer Federation of 

 

g

Ibid., page 410 

h

 NADA, Docket No. NHTSA-2010-0131-0261, at 11. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-5 

America (CFA)iexpressed support for attribute-based standards, generally, indicating that 

such standards accommodate consumer preferences, level the playing field between 

manufacturers, and remove the incentive to push consumers into smaller vehicles. Many 

commenters, including automobile manufacturers, NGOs, trade associations and parts 

suppliers (e.g., General Motors,j

Ford,k American Chemistry Council,l Alliance of 

Automobile Manufacturers,m International Council on Clean Transportation,nInsurance 

Institute for Highway Safety,oSociety of the Plastics Industry,p Aluminum Association,q

Motor and Equipment Manufacturers Association,rand others) expressed support for the 

continued use of vehicle footprint as the attribute upon which to base CAFE and CO2

standards, citing advantages similar to those mentioned by NADA and CFA. Conversely, the 

Institute for Policy Integrity (IPI) at the New York University School of Law questioned 

whether non-attribute-based (flat) or an alternative attribute basis would be preferable to 

footprint-based standards as a means to increase benefits, improve safety, reduce “gaming,” 

and/or equitably distribute compliance obligations.s IPI argued that, even under flat standards, 

credit trading provisions would serve to level the playing field between manufacturers. IPI 

acknowledged that NHTSA, unlike EPA, is required to promulgate attribute-based standards, 

and agreed that a footprint-based system could have much less risk of gaming than a weightbased system. IPI suggested that the agencies consider a range of options, including a fuelbased system, and select the approach that maximizes net benefits. Ferrari and BMW 

suggested that the agencies consider weight-based standards, citing the closer correlation 

between fuel economy and footprint, and BMW further suggested that weight-based standards 

might facilitate international harmonization (i.e., between U.S. standards and related standards 

in other countries).t Porsche commented that the footprint attribute is not well suited for 

manufacturers of high performance vehicles with a small footprint.u

Regarding the comments from IPI, as IPI appears to acknowledge, EPCA/EISA 

expressly requires that CAFE standards be attribute-based and defined in terms of 

mathematical functions. Also, NHTSA has, in fact, considered and reconsidered options 

other than footprint, over the course of multiple CAFE rulemakings conducted throughout the 

past decade. When first contemplating attribute-based systems, NHTSA considered attributes 

such as weight, “shadow” (overall area), footprint, power, torque, and towing capacity. 

NHTSA also considered approaches that would combine two or potentially more than two 

 

i

 CFA, Docket No. EPA-HQ-OAR-2010-0799-9419at 8, 44. 

j

 GM, Docket No. NHTSA-2010-0131-0236, at 2. 

k

 Ford, Docket No. NHTSA-2010-0131-0235, at 8. 

l

 ACC, Docket No. EPA-HQ-OAR-2010-0799-9517at 2. 

m

 Alliance, Docket No. NHTSA-2010-0131-0262, at 85. 

n

 ICCT, Docket No. NHTSA-2010-0131-0258, at 48. 

o

 IIHS, Docket No. NHTSA-2010-0131-0222, at 1. 

p

 SPI, Docket No. EPA-HQ-OAR-2010-0799-9492, at 4. 

q

 Aluminum Association, Docket No. NHTSA-2010-0131-0226, at 1. 

r

 MEMA, Docket No. EPA-HQ-OAR-2010-0799-9478], at 1. 

s

 IPI, Docket No. EPA-HQ-OAR-2010-0799-11485at 13-15.

t

 BMW, Docket No. NHTSA-2010-0131-0250, at 3. 

u

 Porsche, EPA-HQ-OAR-2010-0799-9264
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-6 

such attributes. To date, every time NHTSA (more recently, with EPA) has reconsidered 

options, the agency has concluded that a properly designed footprint-based approach provides 

the best means of achieving the basic policy goals (i.e., better balancing compliance burdens 

among full-line and limited-line manufacturers and reducing incentives for manufacturers to 

respond to standards by reducing vehicle size in ways that could compromise overall highway 

safety) involved in applying an attribute-based standards, and at the same time structuring 

footprint-based standards in a way that furthers the energy and environmental policy goals of 

EPCA and the CAA by controlling incentives to increase vehicle size in ways that could 

increase fuel consumption and GHG emissions.v

 In response to IPI’s suggestion to use fuelbased standards as a type of attribute, although neither NHTSA nor EPA have presented 

quantitative analysis of standards that differentiate between fuel type for light-duty vehicles, 

such standards would effectively use fuel type to identify different subclasses of vehicles, thus 

requiring mathematical functions—not addressed by IPI’s comments—to recombine these 

fuel types into regulated classes.w Insofar as EPCA/EISA already specifies how different fuel 

types are to be treated for purposes of calculating fuel economy and CAFE levels, and 

moreover, insofar as the EISA revisions to EPCA removed NHTSA’s previously-clear 

authority to set separate CAFE standards for different classes of light trucks, using fuel type 

to further differentiate subclasses of vehicles could conflict with the intent, and possibly the 

letter, of NHTSA’s governing statute. Finally, in the agencies’ judgment, while regarding 

IPI’s suggestion that the agencies select the attribute-based approach that maximizes net 

benefits may have merit, net benefits are but one of many considerations which lead to the 

setting of the standard. Also, such an undertaking would be impracticable at this time, 

considering that the mathematical forms applied under each attribute-based approach would 

also need to be specified, and that the agencies lack methods to reliably quantify the relative 

potential for induced changes in vehicle attributes. 

Regarding Ferrari’s and BMW’s comments, as stated previously, in the agencies’ 

judgment, footprint-based standards (a) discourage vehicle downsizing that might 

compromise occupant protection, (b) encourage the application of technology, including 

weight-efficient materials (e.g., high-strength steel, aluminum, magnesium, composites, etc.), 

and (c) are less susceptible than standards based on other attributes to “gaming” that could 

lead to less-than-projected energy and environmental benefits. It is also important to note that 

there are many differences between both the standards and the on-road light-duty vehicle 

 

v

See 71 FR 17566, at17595-17596 (April 6, 2006); 74 FR 14196, at14359 (March 30, 2009); 75 FR 25324 at 

25333 (May 7, 2010). 

w

 The agencies did adopt separate standards for gasoline and diesel heavy-duty pickups and vans based on 

technological differences between gasoline and diesel engines. See 76 FR at 57163-65. However, the agencies 

stated that “standards that do not distinguish between fuel types are generally preferable where technological and 

market-based reasons do not strongly argue otherwise. These technological differences exist presently between 

gasoline and diesel engines for GHGs ... The agencies emphasize, however, that they are not committed to 

perpetuating separate GHG standards for gasoline and diesel heavy-duty vehicles and engines, and expect to 

reexamine the need for separate gasoline/diesel standards in the next rulemaking.” 76 FR at 57165. IPI did not 

suggest that there were any such technological distinctions justifying separate fuel-based attributes for light duty 

vehicles, and the agencies note that EPCA/EISA already specifies how different fuels are to be treated for 

purposes of CAFE 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-7 

fleets in Europe and the United States. The stringency of standards, independent of the 

attribute used, is another factor that influences harmonization. While the agencies agree that 

international harmonization of test procedures, calculation methods, and/or standards could be 

a laudable goal, again, harmonization is not simply a function of the attribute upon which the 

standards are based. Given the differences in the on-road fleet (including vehicle 

classification and use), in fuel composition and availability, in regional consumer preferences 

for different vehicle characteristics, in other vehicle regulations besides for fuel economy/CO2

emissions, it would not necessarily be expected that the CAFE and GHG emission standards 

would align with standards of other countries. Thus, the agencies continue to judge vehicle 

footprint to be a preferable attribute for the same reasons enumerated in the proposal and 

reiterated above. 

Finally, as explained in section III.B.6 and documented in section III.D.6 below, EPA 

agrees with Porsche that the MY 2017 GHG standards, and the GHG standards for the 

immediately succeeding model years, pose special challenges of feasibility and (especially) 

lead time for intermediate volume manufacturers, in particular for limited-line manufacturers 

of smaller footprint, high performance passenger cars. It is for this reason that EPA has 

provided additional lead time to these manufacturers. NHTSA, however, is providing no such 

additional lead time. Under EISA/EPCA, manufacturers continue—as since the 1970s—to 

have the option of paying civil penalties in lieu of achieving compliance with the standards. 

2.3 What mathematical functions have the agencies previously used, and why? 

2.3.1 NHTSA in MY 2008 and MY 2011 CAFE (constrained logistic) 

For the MY 2011 CAFE rule, NHTSA estimated fuel economy levels after 

normalization for differences in technology, but did not make adjustments to reflect other 

vehicle attributes (e.g., power-to-weight ratios).x Starting with the technology adjusted 

passenger car and light truck fleets, NHTSA used minimum absolute deviation (MAD) 

regression without sales weighting to fit a logistic form as a starting point to develop 

mathematical functions defining the standards. NHTSA then identified footprints at which to 

apply minimum and maximum values (rather than letting the standards extend without limit) 

and transposed these functions vertically (i.e., on a gpm basis, uniformly downward) to 

produce the promulgated standards. In the preceding rule, for MYs 2008-2011 light truck 

standards, NHTSA examined a range of potential functional forms, and concluded that, 

compared to other considered forms, the constrained logistic form provided the expected and 

appropriate trend (decreasing fuel economy as footprint increases), but avoided creating 

“kinks” the agency was concerned would provide distortionary incentives for vehicles with 

neighboring footprints.y

 

x

See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA discussion of curve fitting in the MY 2011 CAFE 

final rule. 

y

See 71 FR 17556, 17609-17613 (Apr. 6, 2006) for NHTSA discussion of “kinks” in the MYs 2008-2011 light 

truck CAFE final rule (there described as “edge effects”). A “kink,” as used here, is a portion of the curve where 

a small change in footprint results in a disproportionally large change in stringency. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-8 

2.3.2 MYs 2012-2016 Light Duty GHG/CAFE (constrained/piecewise linear) 

For the MYs 2012-2016 rules, NHTSA and EPA re-evaluated potential methods for 

specifying mathematical functions to define fuel economy and GHG standards. The agencies 

concluded that the constrained logistic form, if applied to post-MY 2011 standards, would 

likely contain a steep mid-section that would provide undue incentive to increase the footprint 

of midsize passenger cars.5

 The agencies judged that a range of methods to fit the curves 

would be reasonable, and used a minimum absolute deviation (MAD) regression without sales 

weighting on a technology-adjusted car and light truck fleet to fit a linear equation. This 

equation was used as a starting point to develop mathematical functions defining the standards 

as discussed above. The agencies then identified footprints at which to apply minimum and 

maximum values (rather than letting the standards extend without limit) and transposed these 

constrained/piecewise linear functions vertically (i.e., on a gpm or CO2 basis, uniformly 

downward) to produce the fleetwide fuel economy and CO2 emission levels for cars and light 

trucks described in the final rule.6 

2.3.3 How have the agencies defined the mathematical functions for the MYs 2017-

2025 standards, and why? 

By requiring NHTSA to set CAFE standards that are attribute-based and defined by a 

mathematical function, NHTSA interprets Congress as intending that the post-EISA standards 

to be data-driven – a mathematical function defining the standards, in order to be “attributebased,” should reflect the observed relationship in the data between the attribute chosen and 

fuel economy.z EPA is also setting attribute-based CO2 standards defined by similar 

mathematical functions, for the reasonable technical and policy grounds discussed below and 

in section II of the preamble to the rule, and to harmonize with the CAFE standards. 

The relationship between fuel economy (and GHG emissions) and footprint, though 

directionally clear (i.e., fuel economy tends to decrease and CO2 emissions tend to increase 

with increasing footprint), is theoretically vague and quantitatively uncertain; in other words, 

not so precise as to a priori yield only a single possible curve.aa There is thus a range of 

legitimate options open to the agencies in developing curve shapes. The agencies may of 

course consider statutory objectives in choosing among the many reasonable alternatives since 

the statutes do not dictate a particular mathematical function for curve shape. For example, 

curve shapes that might have some theoretical basis could lead to perverse outcomes contrary 

 

z

 A mathematical function can be defined, of course, that has nothing to do with the relationship between fuel 

economy and the chosen attribute – the most basic example is an industry-wide standard defined as the 

mathematical function average required fuel economy = X, where X is the single mpg level set by the agency. 

Yet a standard that is simply defined as a mathematical function that is not tied to the attribute(s) would not meet 

the requirement of EISA. 

aa In fact, numerous manufacturers have confidentially shared with the agencies what they describe as “physics 

based” curves, with each OEM showing significantly different shapes, and footprint relationships. The sheer 

variety of curves shown to the agencies further confirm the lack of an underlying principle of “fundamental 

physics” driving the relationship between CO2 emission or fuel consumption and footprint, and the lack of an 

underlying principle to dictate any outcome of the agencies’ establishment of footprint-based standards. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-9 

to the intent of the statutes to conserve energy and reduce GHG emissions. .bb Thus, the 

decision of how to set the target curves cannot always be just about most “clearly” using a 

mathematical function to define the relationship between fuel economy and the attribute; it 

often has to have reflect legitimate policy judgments, where the agencies adjust the function 

that would define the relationship in order to achieve environmental goals, reduce petroleum 

consumption, encourage application of fuel-saving technologies, not adversely affect highway 

safety, reduce disparities of manufacturers’ compliance burdens (thereby increasing the 

likelihood of improved fuel economy and reduced GHG emissions across the entire spectrum 

of footprint targets), preserve consumer choice, etc. This is true both for the decisions that 

guide the mathematical function defining the sloped portion of the target curves, and for the 

separate decisions that guide the agencies’ choice of “cutpoints” (if any) that define the fuel 

economy/CO2 levels and footprints at each end of the curves where the curves become flat. 

Data informs these decisions, but how the agencies define and interpret the relevant data, and 

then the choice of methodology for fitting a curve to the data, must include a consideration of 

both technical data and policy goals. Supporting the consideration and selection of 

mathematical functions upon which to base new CAFE and GHG standards, the agencies 

conducted a broad-ranging analysis spanning different techniques for adjusting data and 

fitting linear functions. The next sections examine the policy concerns that the agencies 

considered in developing the target curves that define the MYs 2017-2025 CAFE and CO2

standards, technical work (expanding on similar analyses performed by NHTSA when the 

agency proposed MY 2011-2015 standards, and by both agencies during consideration of 

options for MY 2012-2016 CAFE and GHG standards) that was completed in the process of 

reexamining potential mathematical functions for this rulemaking, how the agencies have 

defined the data, and how the agencies explored statistical curve-fitting methodologies in 

order to arrive at proposed and final curves. Because the agencies are finalizing the target 

curves for MYs 2017-2025 as proposed, the following discussion largely mirrors the 

discussion in the version of the TSD that accompanied the proposal; it is repeated here for the 

reader’s convenience. 

2.4 What did the agencies propose for the MYs 2017-2025 curves? 

The mathematical functions for the proposed MYs 2017-2025 standards were 

somewhat changed from the functions for the MYs 2012-2016 standards, in response to 

comments received from stakeholders both pre-proposal and during the public comment 

period and in order to address technical concerns and policy goals that the agencies judged 

more significant in this nine-model year rulemaking than in the prior one, which only 

included five model years.cc

 This section (2.4) discusses the methodology the agencies 

 

bb For example, if the agencies set weight-based standards defined by a steep function, the standards might 

encourage manufacturers to keep adding weight to their vehicles to obtain less stringent targets. 

cc We note that although, due to statutory constraints, NHTSA is finalizing standards for only MYs 2017-2021 

and presenting augural standards for MYs 2022-2025, the joint analysis was conducted by NHTSA and EPA 

with respect to shapes of target curves for all nine model years – both because EPA is indeed finalizing all nine 

years of standard curves, and because NHTSA’s augural standards for MYs 2022-2025 represent the agency’s 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-10 

selected as best addressing those technical concerns and policy goals for this rulemaking, 

given the various technical inputs to the agencies’ current analyses. Section 2.5 discusses 

how the agencies determined the cutpoints and the flat portions of the MYs 2017-2025 target 

curves. We note that both of these sections address only how the target curves were fit to fuel 

consumption and CO2 emission values determined using the city and highway test procedures, 

and that in determining respective regulatory alternatives, the agencies made further 

adjustments to the resultant curves in order to account for adjustments for improvements to 

mobile air conditioners.

Thus, recognizing that there are many reasonable statistical methods for fitting curves 

to data points that define vehicles in terms of footprint and fuel economy, the agencies chose 

for the proposed rule to fit curves using an ordinary least-squares formulation, on salesweighted data, using a fleet that has had technology applied, and after adjusting the data for 

the effects of weight-to-footprint, as described below. This represents a departure from the 

statistical approach for fitting the curves in the MYs 2012-2016 rules, as explained in the next 

section (2.4.1). The agencies considered a wide variety of reasonable statistical methods in 

order to better understand the range of uncertainty regarding the relationship between fuel 

consumption (the inverse of fuel economy), CO2 emission rates, and footprint, thereby 

providing a range within which decisions about standards would be potentially supportable. 

2.4.1 What concerns were the agencies looking to address that led them to change 

from the approach used for the MYs 2012-2016 curves? 

Before the MY 2017 and later proposal was issued, NHTSA and EPA received a 

number of comments from stakeholders on how curves should be fitted to the passenger car 

and light truck fleets.dd Some limited-line manufacturers argued that curves should generally 

be flatter in order to avoid discouraging production of small vehicles, because steeper curves 

tend to result in more stringent targets for smaller vehicles. Most full-line manufacturers 

argued that a passenger car curve similar in slope to the MY 2016 passenger car curve would 

be appropriate for future model years, but that the light truck curve should be revised to be 

less stringent for manufacturers selling the largest full-size pickup trucks. These 

manufacturers argued that the MY 2016 light truck curve was not “physics-based,” and that in 

order for future tightening of standards to be feasible for full-line manufacturers, the truck 

curve for later model years should be steeper and extended further (i.e., made less stringent) 

into the larger footprints. As stated in the TSD accompanying the proposal, the agencies do 

not agree that the MY 2016 light truck curve was somehow deficient in lacking a “physics 

basis,” or that it was somehow overly stringent for manufacturers selling large pickups—

manufacturers making these arguments presented no “physics-based” model to explain how 

fuel economy should depend on footprint.ee

 The same manufacturers indicated that they 

 

best estimate, based on the information currently before it, of the standards that the agency would finalize had it 

the authority to do so. NHTSA will fully revisit all aspects of the MYs 2022-2025 standards as part of the later 

rulemaking concurrent with the mid-term evaluation. 

dd See 75 FR at 76341 for a general summary. 

ee See footnote aa. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-11 

believed that the light truck standard should be somewhat steeper after MY 2016, primarily 

because, after more than ten years of progressive increases in the stringency of applicable 

CAFE standards, large pickups would be less capable of achieving further improvements 

without compromising load carrying and towing capacity. 

In developing the curve shapes for the proposed rule, the agencies were aware of the 

current and prior technical concerns raised by OEMs concerning the effects of the stringency 

on individual manufacturers and their ability to meet the standards with available 

technologies, while producing vehicles at a cost that allowed them to recover the additional 

costs of the technologies being applied. Although we continue to believe that the 

methodology for fitting curves for the MYs 2012-2016 standards was technically sound, we 

recognize manufacturers’ technical concerns regarding their abilities to comply with a 

similarly shallow curve after MY 2016 given the anticipated mix of light trucks in MYs 2017-

2025. As in the MYs 2012-2016 rules, the agencies considered these concerns in the analysis 

of potential curve shapes for the MYs 2017-2025 proposal. The agencies also considered 

safety concerns which could be raised by curve shapes creating an incentive for vehicle 

downsizing, as well as the potential loss to consumer welfare should vehicle upsizing be 

unduly disincentivized. In addition, the agencies sought to improve the balance of 

compliance burdens among manufacturers, and thereby increase the likelihood of improved 

fuel economy and reduced GHG emissions across the entire spectrum of footprint targets. 

Among the technical concerns and resultant policy trade-offs the agencies considered were 

the following: 

Flatter standards (i.e., curves) increase the risk that both the weight and size of 

vehicles will be reduced, potentially compromising highway safety. 

Flatter standards potentially impact the utility of vehicles by providing an incentive for 

vehicle downsizing. 

Steeper footprint-based standards may create incentives to upsize vehicles, thus 

increasing the possibility that fuel economy and greenhouse gas reduction benefits will 

be less than expected. 

Given the same industry-wide average required fuel economy or CO2 standard, flatter 

standards tend to place greater compliance burdens on full-line manufacturers 

Given the same industry-wide average required fuel economy or CO2 standard, steeper 

standards tend to place greater compliance burdens on limited-line manufacturers 

(depending of course, on which vehicles are being produced). 

If cutpoints are adopted, given the same industry-wide average required fuel economy, 

moving small-vehicle cutpoints to the left (i.e., up in terms of fuel economy, down in 

terms of CO2 emissions) discourages the introduction of small vehicles, and reduces 

the incentive to downsize small vehicles in ways that could compromise overall 

highway safety. 

If cutpoints are adopted, given the same industry-wide average required fuel economy, 

moving large-vehicle cutpoints to the right (i.e., down in terms of fuel economy, up in 

terms of CO2 emissions) better accommodates the design requirements of larger 

vehicles—especially large pickups—and extends the size range over which 

downsizing is discouraged. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-12 

All of these were policy goals that required weighing and consideration. Ultimately, 

the agencies rejected the argument that the MY 2017 target curves for the proposal, on a 

relative basis, should be made significantly flatter than the MY 2016 curve,ff as we believed 

that this would undo some of the safety-related incentives and balancing of compliance 

burdens among manufacturers—effects that attribute-based standards are intended to provide. 

Nonetheless, the agencies recognized full-line OEM concerns and tentatively 

concluded that further increases in the stringency of the light truck standards would be more 

feasible if the light truck curve is made steeper than the MY 2016 truck curve and the right 

(large footprint) cut-point is extended over time to larger footprints. This conclusion was 

supported by the agencies’ technical analyses of regulatory alternatives defined using the 

curves developed in the manner described below. 

2.4.2 What methodologies and data did the agencies consider in developing the 

2017-2025 curves presented in the proposal? 

In considering how to address the various policy concerns discussed in the previous 

sections, the agencies revisited the data and performed a number of analyses using different 

combinations of the various statistical methods, weighting schemes, adjustments to the data 

and the addition of technologies to make the fleets less technologically heterogeneous. As 

discussed in 2.3.3, in the agencies’ judgment, there is no single “correct” way to estimate the 

relationship between CO2 or fuel consumption and footprint – rather, each statistical result is 

based on the underlying assumptions about the particular functional form, weightings and 

error structures embodied in the representational approach. These assumptions are the subject 

of the following discussion. This process of performing many analyses using combinations of 

statistical methods generated many possible outcomes, each embodying different potentially 

reasonable combinations of assumptions and each thus reflective of the data as viewed 

through a particular lens. The choice of a standard developed by a given combination of these 

statistical methods was consequently a decision based upon the agencies’ determination of 

how, given the policy objectives for this rulemaking and the agencies’ MY 2008-based 

forecast of the market through MY 2025, to appropriately reflect the current understanding of 

the evolution of automotive technology and costs, the future prospects for the vehicle market, 

and thereby establish curves (i.e., standards) for cars and light trucks. 

2.4.2.1 For the MYs 2017-2025 standards, what information did the agencies use to 

estimate a relationship between fuel economy, CO2 and footprint? 

For each fleet, the agencies began with the MY 2008-based market forecast developed 

to support the proposal (i.e., the baseline fleet), with vehicles’ fuel economy levels and 

technological characteristics at MY 2008 levels.gg

 The development, scope, and content of 

 

ff While “significantly” flatter is subjective qualitative description, the year over year change in curve shapes is 

discussed in greater detail in Section 2.5.3.1. 

gg While the agencies jointly conducted this analysis, the coefficients ultimately used in the slope setting analysis 

are from the CAFE model.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-13 

this market forecast is discussed in detail in Chapter 1 of the joint Technical Support 

Document supporting the proposed rulemaking.

Figure 2-1 shows the MY 2008 CO2 by car and truck class as it existed in the EPA 

OMEGA and NHTSA CAFE NPRM model data files (for a gasoline-only fleet, fuel 

consumption—the inverse of fuel economy—is directly proportional to CO2). This fleet was 

the starting point for all analysis in the proposal. 

Figure 2-1 2008 CO2 vs. Footprint by Car and Truck 

Although the agencies are finalizing the target curves as proposed, the agencies have 

also revisited and updated their analyses for this final rule, and found that the proposed curves 

are well within the ranges spanned by the final rule analyses. See section 2.6 below. As 

discussed in Chapter 1 of this TSD, the agencies have used two different market forecasts to 

conduct additional analyses supporting this final rule. The first, referred to here as the “MY 

2008-Based Fleet Projection,” is largely identical to that used for analysis supporting the 

NPRM, but includes some corrections to the footprint of some vehicle models discussed in 

Chapter 1, as well as other minor changes. The second, referred to here as the “MY 2010-

Based Fleet Projection,” is a post-proposal market forecast based on the MY 2010 fleet of 

vehicles. Using both of these projected fleets, the agencies repeated the analyses described 

below, and obtained broadly similar results, details of which are presented in a memorandum 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-14 

available in NHTSA’s docket.hh Because the agencies are promulgating target curve standards 

identical to those proposed in the NPRM, the remainder of this chapter reviews results 

supporting the development of those proposed standards. This chapter concludes with a 

summary of results of the agencies’ updated analysis, and discussion of the consideration that 

analysis was given in selecting mathematical functions upon which to base the standards in 

the final rules.

2.4.2.2 What adjustments did the agencies evaluate? 

As indicated in the TSD supporting the NPRM, one possible approach is to fit curves to 

the minimally adjusted data shown above (the approach still includes sales mix adjustments, 

which influence results of sales-weighted regressions), much as DOT did when it first began 

evaluating potential attribute-based standards in 2003.7

 However, the agencies found, as in 

prior rulemakings, that the data are so widely spread (i.e., when graphed, they fall in a loose 

“cloud” rather than tightly around an obvious line) that they indicate a relationship between 

footprint and CO2 and fuel consumption that is real but not particularly strong (Figure 2-1). 

Therefore, as discussed below, the agencies also explored possible adjustments that could 

help to explain and/or reduce the ambiguity of this relationship, or could help to produce 

policy outcomes the agencies judged to be more desirable. 

2.4.2.3 Adjustment to reflect differences in technology 

As in prior rulemakings, the agencies considered technology differences between vehicle 

models to be a significant factor producing uncertainty regarding the relationship between 

CO2/fuel consumption and footprint. Noting that attribute-based standards are intended to 

encourage the application of additional technology to improve fuel efficiency and reduce CO2

emissions, the agencies, in addition to considering approaches based on the unadjusted 

engineering characteristics of MY 2008 vehicle models, therefore also considered approaches 

in which, as for previous rulemakings, technology is added to vehicles for purposes of the 

curve fitting analysis in order to produce fleets that are less varied in technology content. 

This approach helps to reduce “noise” (i.e., dispersion) in the plot of vehicle footprints and 

fuel consumption levels and to identify a more technology-neutral relationship between 

footprint and fuel economy / CO2 emissions. 

For the analysis supporting the NPRM, the agencies adjusted the NPRM baseline fleet for 

technology by adding all technologies considered, except for, diesel engines, integrated starter 

generators, strong HEVs, PHEVs, EVs, FCVs, and the most advanced high-BMEP (brake 

 

hh Docket No. NHTSA-2010-0131. As with the NPRM analysis, EPA and NHTSA jointly analyzed the fleet 

projections used in this final rulemaking. While the proposal and final rulemaking analyses shown in this 

chapter are from the NHTSA CAFE model, the EPA OMEGA results are generally similar, and support the same 

conclusions. A memo containing the OMEGA results for the FRM can be found in EPA docket EPA-HQ-OAR2010-0799. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-15 

mean effective pressure) gasoline engines.ii The agencies included 15 percent mass 

reduction on all vehicles. Figure 2-2 shows the same fleet, with technology adjustment and 

2021 sales applied, and the baseline diesel fueled vehicles, HEV and EVs removed from the 

fleet. Of note, the fleet is now more closely clusteredjj (and lower in emissions), but the same 

basic pattern emerges; in both figures, the CO2 emission rate (which, as mentioned above, is 

directly proportional to fuel consumption for a gasoline-only fleet) increases with increasing 

footprint, although the relationship is less pronounced for larger light trucks. 

Figure 2-2 2008 CO2 vs. Footprint by Car and Truck, after Adjustment Reflecting Technology 

Differences, and removing diesel fueled vehicles, HEVs and EVs 

Updating this analysis using the current MY2008- and MY2010-based fleet projection 

yielded results generally similar to those shown above. Detailed results of the analyses with 

 

ii As described in the preceding paragraph, applying technology in this manner serves to reduce the effect of 

technology differences across the vehicle fleet. The particular technologies used for the normalization were 

chosen as a reasonable selection of technologies which could potentially be used by manufacturer over this time 

period. 

jj For cars, the standard deviation of the CO2 data is reduced from 81 to 54 through the technology normalization. 

For trucks, the standard deviation is reduced from 62 to 36. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-16 

the final rulemaking fleet projections are presented in a memorandum available in NHTSA’s 

docket.kk

2.4.2.4 Adjustments reflecting differences in performance and “density” 

As discussed in Section 2.4.1, during stakeholder meetings the agencies held while 

developing the NPRM,ll some manufacturers indicated that they believed that the light truck 

standard should be somewhat steeper after MY 2016. As a means to produce a steeper light 

truck curve, the agencies considered adjustments for other differences between vehicle 

models (i.e., inflating or deflating the fuel economy of each vehicle model based on the extent 

to which one of the vehicle’s attributes, such as power, is higher or lower than average). 

Previously, NHTSA had rejected such adjustments because they imply that a multi-attribute 

standard may be necessary, and as explained above, the agencies judged most multi-attribute 

standards to be more subject to gaming than a footprint-only standard.mm,8 Having considered 

this issue again for purposes of this rulemaking, NHTSA and EPA concluded the need to 

accommodate in the target curves the challenges faced by manufacturers of large pickups 

currently outweighs these prior concerns (comments on this topic are discussed in Section 0

and 2.4.2.11 and in Section II.C of the preamble). Therefore, the agencies also evaluated 

curve fitting approaches through which fuel consumption and CO2 levels were adjusted with 

respect to weight-to-footprint alone, and in combination with power-to-weight. While the 

agencies examined these adjustments for purposes of fitting curves, the agencies did not 

propose a multi-attribute standard; the proposed fuel economy and CO2 targets for each 

vehicle were still functions of footprint alone. The agencies are not promulgating a multiattribute standard, and no adjustment will be used in the compliance process. 

The agencies also examined some differences between the technology-adjusted car 

and truck fleets in order to better understand the relationship between footprint and CO2/fuel 

consumption in the agencies’ MY 2008 based forecast. More direct measures (such as 

coefficients of drag and rolling resistance), while useful for vehicle simulation, were not 

practical or readily available at the fleet level. Given this issue, and based on analysis 

published in the MYs 2012-2016 rule,9

 the agencies investigated a sales-weighted (i.e., 

treating every vehicle unit sold as a separate observation) regression equation involving 

power to weight ratio and vehicle weight (Equation 2-1).nn This equation provides for a 

 

kk Docket No. NHTSA-2010-0131.

ll See Preamble I.A.2 for a discussion of the stakeholder meetings before the NPRM. 

mm For example, in comments on NHTSA’s 2008 NPRM regarding MY 2011-2015 CAFE standards, Porsche 

recommended that standards be defined in terms of a “Summed Weighted Attribute”, wherein the fuel economy 

target would calculated as follows: target = f(SWA), where target is the fuel economy target applicable to a 

given vehicle model and SWA = footprint + torque1/1.5 + weight1/2.5. (NHTSA-2008-0089-0174). While the 

standards the agencies proposed for MY 2017-2025 are not multi-attribute standards, that is the target is only a 

function of footprint, we proposed curve shapes that were developed considering more than one attribute. 

nn These parameters directly relate to the amount of energy required to move the vehicle. As compared to a 

lighter vehicle, more energy is required to move a heavier vehicle the same distance. Similarly, a more powerful 

engine, when technology adjusted, is less efficient than a less powerful engine. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-17 

strong correlation between HP/WT, weight and CO2 emissions (R2=0.78, Table 2-1) after 

accounting for technology adjustments.oo 

 

Equation 2-1 – Relationship between vehicle attributes and emissions or fuel consumption 

Where: 

HP/Weight= the rated horsepower of the vehicle divided by the curb weight 

Weight = the curb weight of the vehicle in pounds 

C = a constant. 

Table 2-1 – Physical Regression Coefficients against Technology Adjusted CO2

* 

Cars Light Trucks

R

2

0.78 0.78

F-test p <0.01 <0.01

1.09*1031.13*103

3.29*10-23.45*10-2

C -3.29 2.73

*In this gasoline only fleet, these coefficients can be divided by 8887 (the amount of 

CO2 produced by the combustion of a gallon of the fuel used to certify the fuel 

economy and emissions of gasoline vehicles) to yield the corresponding fuel 

consumption coefficients. 

Updating this analysis using the MY 2008- and MY 2010-based fleet projections 

yielded results generally similar to those shown above. Detailed results of the analyses with 

the final rulemaking fleet projections are presented in a memorandum available in NHTSA’s 

docket.pp

The coefficients above show, for the agencies’ MY 2008-based market forecast as 

developed for the NPRM, strong correlation between these vehicle attributes and the fuel 

consumption and emissions of the vehicle, as well as strong similarity between car and truck 

coefficients. (As explained in section 2.6 below, our analysis using the corrected version of 

the MY 2008 based market forecast used for the final rule, as well as the alternative 2010 

based market forecast, is consistent with these results.) Given these very similar parameters, 

 

oo As R2

 does not equal 1, there are remaining unaccounted for differences beyond technology, power and 

weight. These may include gear ratios, axle ratios, aerodynamics, and other vehicle features not captured in this 

equation. 

pp Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-18 

similar distributions of power and weight would be expected to produce similarly arrayed 

plots of CO2 (or equivalently, fuel consumption) by footprint, regardless of car or truck class. 

Based on the differences seen in the technology-adjusted plot (Figure 2-2), the agencies 

further investigated these particular attributes and their relationship to footprint in the 

agencies’ MY 2008-based market forecast developed for the NPRM, to examine the 

differences across the footprint distribution. 

Figure 2-3 shows vehicle curb weight charted against footprint, with sales weighted 

ordinary least squares sales fit (blue) and sales-weighted LOESS fit (red) imposed. For cars, 

the LOESS fit, which weights nearby points more heavily, qq is nearly identical to the linear fit 

in the data filled region between about 40 and 56 sq ft (with the gray bar showing standard 

error on the Loess fit). For this market forecast, average car curb weight is linearly 

proportional to car footprint between 40 and 56 sq ft, or in other words, cars progress in 

weight in a regular fashion as they get larger (Figure 2-3). 

Figure 2-3 By contrast, a linear fit does not overlap with the LOESS fit on the truck 

side, which indicates that for this market forecast, truck curb weight does not linearly increase 

with footprint, at least not across the entire truck fleet. The LOESS fit shows that larger 

trucks (those on the right side of the data bend in Figure 2-2) have a different trend than 

smaller trucks, and after about 55 sq ft, no longer proportionally increases in weight. The 

same pattern is seen in Figure 2-1 and Figure 2-2 above. 

 

qq: In a LOESS regression, “fitting is done locally. That is, for the fit at point x, the fit is made using points in a 

neighborhood of x, weighted by their distance from x (with differences in ‘parametric’ variables being ignored 

when computing the distance). The size of the neighborhood is controlled by α For α < 1, the neighborhood 

includes proportion α of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3. 

For α > 1, all points are used, with the ‘maximum distance’ assumed to be α^1/p times the actual maximum 

distance for p explanatory variables.” 

A span of 1 was used in these images. http://cran.r-project.org/doc/manuals/fullrefman.pdf, p. 1406. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-19 

Figure 2-3 Relationship between Weight and Footprint in Agencies’ MY2008-Based Market 

Forecast 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available in 

NHTSA’s docket.

rr

To further pursue this topic, weight divided by footprint (WT/FP) can be thought of as 

a “density” of a vehicle (although dimensionally it has units of pressure). As seen in Figure 

2-4, the trend in WT/FP in the agencies’ MY2008-based market forecast is different in trucks 

than in cars. The linear trend on cars is an increase in WT/FP as footprint increases (Figure 

2-4). In contrast, light trucks do not consistently increase in WT/FP ratio as the vehicles grow 

larger, but WT/FP actually decreases (Figure 2-4). 

 

rr Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-20 

Figure 2-4 Relationship between Weight/FP and Footprint in Agencies’ MY2008-Based Market Forecast 

Updating this analysis using the current MY 2008- and MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.ss

The heterogeneity of the truck fleet explains part of the WT/FP trend, where the 

pickup truck fleet is largest in footprint, but is also relatively light for its size due to the flat 

bed (Figure 2-5). Note that the two light truck classes with the smallest WT/FP ratios are 

small and large pickups. Further, as the only vehicle class with a sales-weighted average 

footprint above 60 square feet, the large pickup trucks have a strong influence on the slope of 

the truck curve. As the correlation between weight and CO2 is strong (Table 2-1), having 

proportionally lighter vehicles at one extreme of the footprint distribution can bias a curve fit 

to these vehicles. If no adjustment is made to the curve fitted to the truck fleet, and no other 

compensating flexibilities or adjustments are made available, manufacturers selling 

significant numbers of vehicles at the large end of the truck distribution will face compliance 

burdens that are comparatively more challenging that those faced by manufacturers not 

serving this part of the light truck market. As noted further below, this consideration 

 

ss Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-21 

provided the basis for the agencies’ proposal to change the cutpoint for larger light trucks 

from 66 feet to 74 feet, and to steepen the slope of the light truck curve for larger light trucks. 

Figure 2-5 Class and the WT/FP distribution 

Updating this analysis using the revised MY 2008- and the MY 2010-based market 

forecasts yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.tt

The agencies also investigated the relationship between HP/WT and footprint in the 

agencies’ MY 2008-based market forecast developed for the NPRM (Figure 2-6). On a sales 

weighted basis, cars tend to become proportionally more powerful as they get larger. In 

contrast, there is a minimally positive relationship between HP/WT and footprint for light 

trucks, indicating that light trucks become only slightly more powerful as they get larger, but 

that the trend is not especially pronounced. 

 

tt Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-22 

Figure 2-6 HP/WT v. FP 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.uu

One factor influencing results of this analysis is the non-homogenous nature of the 

truck fleet; some vehicles at the smaller end of the footprint curve are different in design and 

utility from others at the larger end (leading to the observed bend in the LOESS fit, Figure 

2-6). There are many high volume four-wheel drive vehicles with smaller footprint in the 

truck fleet (such as the Chevrolet Equinox, Dodge Nitro, Ford Escape, Honda CR-V, Hyundai 

Santa Fe, Jeep Liberty, Nissan Rogue, Toyota RAV4, and others) exhibit only select truck 

characteristics.vv

 By contrast, the largest pickup trucks in the light truck fleet have unique 

aerodynamic and power characteristics that tend to increase CO2 emissions and fuel 

consumption. These disparities contribute to the slopes of lines fitted to the light truck fleet. 

 

uu Docket No. NHTSA-2010-0131.

vv In most cases, these vehicles have four-wheel drive, but no significant towing capability, and no open-bed. 

Many of these vehicles are also offered without four-wheel drive, and these two-wheel drive versions are 

classified as passenger cars, not light trucks. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-23 

Several comments, such as those by CBD and ACEEE, were submitted with regard to 

the non-homogenous nature of the truck fleet, and the “unique” attributes of pickup trucks. 

Ford Motor Company described the attributes of these vehicles, noting that “towing capability 

generally requires increased aerodynamic drag caused by a modified frontal area, increased 

rolling resistance, and a heavier frame and suspension to support this additional capability.”ww

 

Ford further noted that these vehicles further require auxiliary transmission oil coolers, 

upgraded radiators, trailer hitch connectors and wiring harness equipment, different steering 

ratios, upgraded rear bumpers and different springs for heavier tongue load (for upgraded 

towing packages), body-on-frame (vs. unibody) construction (also known as ladder frame 

construction) to support this capability and an aggressive duty cycle, and lower axle ratios for 

better pulling power/capability. In the agencies’ judgment, the curves and cutpoints defining 

the light truck standards appropriately account for engineering differences between different 

types of vehicles. For example, the agencies’ estimates of the applicability, cost, and efficacy 

of different fuel-saving technologies differentiate between small, medium, and large light 

trucks. Further discussion on this topic is contained in Section II.C. 

The agencies’ technical analyses of regulatory alternatives developed using curves 

fitted as described below supported OEM comments that there would be significant 

compliance challenges for the manufacturers of large pickup trucks, and led toward the 

agencies’ policy goal of a steeper slope for the light truck curve relative to MY 2016. Three 

primary drivers were as follows: (a) the largest trucks have unique equipment and design, as 

described in the Ford comment referenced above; (b) the agencies agree with those large truck 

manufacturers who indicated in discussions prior to the proposal that they believed that the 

light truck standard should be somewhat steeper after MY 2016, primarily because, after more 

than ten recent years of progressive increases in the stringency of applicable CAFE standards 

(after nearly ten years during which Congress did not allow NHTSA to increase light truck 

CAFE standards), manufacturers of large pickups would have limited options to comply with 

more stringent standards without resorting to compromising large truck load carrying and 

towing capacity; and (c) given the relatively few platforms which comprise the majority of the 

sales at the largest truck footprints, the agencies were concerned about requiring levels of 

average light truck performance that might lead to overly aggressive advanced technology 

penetration rates in this important segment of the work fleet. Specifically, the agencies were 

concerned at proposal, and remain concerned, about issues of lead time and cost with regard 

to manufacturers of these work vehicles. As noted later in this chapter, while the largest 

trucks are a small segment of the overall truck fleet, and an even smaller segment of the 

overall fleet, xx these changes to the truck slope have been made in order to provide a clearer 

path toward compliance for manufacturers of these vehicles, and reduce the potential that new 

 

ww Ford comments, Docket No. [fill in], at [page number]. 

xx The agencies’ market forecast used at proposal includes about 24 vehicle configurations above 74 square feet 

with a total volume of about 50,000 vehicles or less during any MY in the 2017-2025 time frame, In the 

MY2010 based market forecast, there are 14 vehicle configurations with a total volume of 130,000 vehicles or 

less during any MY in the 2017-2025 time frame. This is a similarly small portion of the overall number of 

vehicle models or vehicle sales. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-24 

standards would lead these manufacturers to choose to downpower, modify the structure, or 

otherwise reduce the utility of these work vehicles. 

Some commenters disagreed with these policy goals concerning the largest light trucks 

and argued that higher fuel economy for the largest light trucks is fully compatible with 

maintaining towing and hauling capacity. These comments, which largely deal with 

stringency, are addressed in each agency’s respective preamble section (III.D and IV.F), as 

well as in Section II.C, which addresses the shapes of the target curves. Consequently, the 

agencies considered options including fitting curves developed using results of the analysis 

described above. Specifically, the agencies note that the WT/FP ratio of the light duty fleet 

potentially has a large impact on a sales-weighted regression.yy The increasing trend in 

WT/FP versus footprint for cars in the 2008 MY baseline would steepen the slope of the car 

curve, while the decreasing trend in WT/FP would flatten the truck slope, as compared to a 

WT/FP adjusted fleet. This result was reflected in the MYs 2012-2016 final rulemaking,10

where the agencies noted the steep car curves resulting from a weighted least-squares 

analysis. 

Based on the above analysis, the agencies also considered adjustments for other 

differences between vehicle models. Therefore, utilizing the coefficients derived in Equation 

2-1, the agencies also evaluated curve fitting approaches through which fuel consumption and 

CO2 levels were adjusted with respect to weight-to-footprint alone, and in combination with 

power-to-weight. This adjustment procedure inflates or deflates the fuel economy or CO2

emissions of each vehicle model based on the extent to which one of the vehicle’s attributes, 

such as power, is higher or lower than average. As mentioned above, while the agencies 

considered this technique for purposes of fitting curves, the agencies did not propose a multiattribute standard, as the proposed fuel economy and CO2 targets for each vehicle were still 

functions of footprint alone. The agencies are not promulgating a multi-attribute standard, 

and no adjustment would be used in the compliance process. 

The basis for the gallon-per-mile (GPM) adjustments is the sales-weighted linear 

regression discussed in 2.4 (Equation 2-1, Table 2-1). The coefficients to this equation give 

the impact of the various car attributes on CO2 emissions and fuel consumption in the 

agencies’ MY 2008-based market forecast used in the NPRM. For example, gives the 

impact of weight while holding the ratio horsepower to weight constant. Importantly, this 

means that as weight changes, horsepower must change as well to keep the power/weight ratio 

constant. Similarly, gives the CO2 impact of changing the performance of the vehicle 

while keeping the weight constant. These coefficients were used to perform an adjustment of 

the gallons per mile measure for each vehicle to the respective car or truck—i.e., in the case 

of a HP/WT adjustment, to deflate or inflate the fuel consumption of each vehicle model 

based on the extent to which the vehicle’s power-to-weight ratio is above or below the 

regression-based value at that footprint. 

 

yy As mentioned above, the agencies also performed the same analysis without sales-weighting, and found that 

the WT/FP ratio also had a directionally similar effect on the fitted car and truck curves. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-25 

 

The agencies performed this normalization to adjust for differences in vehicle weight 

per square foot observations in the data discussed in Section 2.4. This adjustment process 

requires two pieces of information: the weight coefficient from Equation 2-1 and the average 

weight per footprint (i.e., pounds per square foot) for that vehicle’s group. Two groups, 

passenger cars and light trucks, were used. For each group, the average weight per footprint 

was calculated as a weighted average with the weight being the same as in the above 

regression (projected sales by vehicle in 2021). The equation below indicates how this 

adjustment was carried out. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-26 

Equation 2-2 WT/FP adjustment 

The term in parentheses represents the vehicle’s deviation from an “expected weight.” 

That is, multiplying the average weight per footprint for a group of vehicles (cars or trucks) 

by a specific vehicle’s footprint gives an estimate of the weight of that specific vehicle if its 

density were “average,” based on the analyzed fleet. Put another way, this factor represents 

what the weight is “expected” to be, given the vehicle’s footprint, and based on the analyzed 

fleet. This “expected weight” is then subtracted from the vehicle’s actual weight. Vehicles 

that are heavier than their “expected weight” will receive a positive value (i.e., a deflated fuel 

economy value) here, while vehicles that are lighter than their “expected weight” will receive 

a negative number (i.e., an inflated fuel economy value). 

This deviation from “expected weight” is then converted to a gallon value by the 

regression coefficient. The units on this coefficient are gallons per mile per pound, as can be 

deduced from equation 1. This value is then subtracted from the vehicle’s actual gallons per 

mile measure. Note that the adjusted truck data no longer exhibits the bend seen in Figure 2-1

and Figure 2-2. 

Figure 2-7 WT/FP Adjusted Fuel Consumption vs. Footprint 

.01 .02 .03 .04 .05

40 50 60 70 80 40 50 60 70 80

Car Truck

GPM

Footprint
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-27 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.zz

This adjustment serves to reduce the variation in gallons per mile measures caused by 

variation in weight in the agencies’ MY 2008-based market forecast used in the NPRM. 

Importantly, this adjustment serves to reduce the fuel consumption (i.e., inflate fuel economy) 

for those vehicles which are heavier than their footprint would suggest while increasing the 

gallons per mile measure (i.e., deflating fuel economy) for those vehicles which are lighter. 

For trucks, a linear trend is more evident in the data cloud.aaa

 The following table shows the 

degree of adjustment for several vehicle models: 

 

zz Docket No. NHTSA-2010-0131.

aaa Using EPA’s dataset, R2

 for the sales weighted ordinary least squared linear fit between footprint and CO2

improved from 0.38 (technology adjusted CO2) to 0.64 (technology and weight / footprint adjusted CO2) 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-28 

Table 2-2 - Sample Adjustments for Weight to Footprint, Cars 

Manufacturer Model Name Plate

Weight / 

Footprint Footprint GPM MPG

Adjusted 

GPM 

Adjusted 

MPG

GPM % 

Adjustment

HONDA HONDA FIT FIT 64.4 39.5 0.01 69.40 0.0157 63.73 8.9%

TOYOTA

TOYOTA 

COROLLA COROLLA 61.3 42.5 0.01 69.94 0.0164 60.80 15.0%

FORD FORD FOCUS FOCUS FWD 62.9 41.7 0.02 61.94 0.0177 56.34 9.9%

GENERAL 

MOTORS

CHEVROLET 

MALIBU MALIBU 73.5 46.9 0.02 53.70 0.0185 54.08 -0.7%

HONDA

HONDA 

ACCORD

ACCORD 4DR 

SEDAN 69.6 46.6 0.02 57.57 0.0179 55.73 3.3%

NISSAN INFINITI G37 G37 COUPE 76.7 47.6 0.02 47.83 0.0200 50.08 -4.5%

GENERAL 

MOTORS

CHEVROLET 

CORVETTE CORVETTE 69.3 46.3 0.02 40.84 0.0251 39.83 2.5%

FORD

FORD 

MUSTANG MUSTANG 74.7 46.7 0.03 31.32 0.0316 31.67 -1.1%

TOYOTA

TOYOTA 

CAMRY

CAMRY 

SOLARA 

CONVERTIBLE 75.6 46.9 0.02 50.87 0.0191 52.27 -2.7%

VOLKSWAGEN

VOLKSWAGEN 

JETTA JETTA 78.0 42.4 0.02 46.77 0.0211 47.47 -1.5%

FORD FORD FUSION FUSION FWD 72.2 46.1 0.02 59.96 0.0168 59.61 0.6%

HONDA

HONDA 

ACCORD

ACCORD 2DR 

COUPE 71.6 46.6 0.02 56.92 0.0178 56.26 1.2%

HYUNDAI

HYUNDAI 

SONATA SONATA 70.7 46.0 0.02 61.72 0.0166 60.34 2.3%

HONDA HONDA CIVIC CIVIC 59.9 43.2 0.02 64.25 0.0177 56.38 14.0%
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-29 

Table 2-3 – Sample Adjustments for Weight to Footprint, Trucks 

Manufacturer Model Name Plate

Weight / 

Footprint Footprint GPM MPG

Adjusted 

GPM 

Adjusted 

MPG

GPM % 

Adjustment

FORD FORD ESCAPE ESCAPE FWD 80.1 65.2 0.02 51.00 0.0181 55.11 -7.5%

GENERAL 

MOTORS

CHEVROLET 

C15

C15 

SILVERADO 

2WD 119WB 85.9 55.9 0.03 39.76 0.0248 40.29 -1.3%

FIAT

JEEP GRAND 

CHEROKEE

GRAND 

CHEROKEE 

4WD 103.7 47.1 0.02 41.45 0.0222 44.98 -7.9%

HONDA HONDA PILOT PILOT 4WD 85.2 51.3 0.02 40.95 0.0243 41.22 -0.6%

TOYOTA

TOYOTA 

HIGHLANDER

HIGHLANDER 

4WD 79.6 49.0 0.02 45.90 0.0227 44.05 4.2%

FORD FORD F150

F150 FFV 

4WD 145 WB 73.8 67.4 0.03 32.70 0.0334 29.97 9.1%

FIAT DODGE RAM

RAM 1500 

PICKUP 4WD 

140 WB 78.1 66.3 0.03 33.75 0.0316 31.65 6.6%

TOYOTA TUNDRA

TOYOTA 

TUNDRA 

4WD 145 WB 79.3 68.7 0.03 32.07 0.0325 30.73 4.3%

TATA

LAND ROVER 

RANGE 

ROVER SPORT

RANGE 

ROVER 

SPORT 118.6 47.5 0.03 33.17 0.0239 41.92 -20.9%

GENERAL 

MOTORS

CHEVROLET 

UPLANDER

UPLANDER 

FWD 114.4 49.2 0.02 45.46 0.0163 61.34 -25.9%

GENERAL 

MOTORS HUMMER H3 H3 4WD 99.9 50.7 0.03 36.71 0.0242 41.30 -11.1%

GENERAL 

MOTORS

PONTIAC 

TORRENT

TORRENT 

FWD 84.2 48.2 0.02 46.64 0.0215 46.56 0.2%

TOYOTA TACOMA

TOYOTA 

TACOMA 

4WD 74.8 53.4 0.02 43.01 0.0252 39.63 8.5%

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.bbb

Based on Equation 2-1, the agencies also evaluated an adjustment of GPM and CO2

based on HP/WT. 

Equation 2-3 –Adjustment based on HP/WT 

 

bbb Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-30 

Figure 2-8 shows the adjusted data and the estimated relationship between the adjusted 

GPM values and footprint. 

Figure 2-8 HP/WT Adjusted Fuel Consumption v. Footprint 

Table 2-4 shows the degree of adjustment for several vehicle models. Those vehicles 

which have more power than average for their actual curb weight are adjusted downward (i.e., 

fuel economy ratings are inflated), while those that have less power than average are adjusted 

upward (i.e., fuel economy ratings are deflated). 

Table 2-4 - Sample Adjustments for Horsepower to Weight, Cars 

.01 .02 .03 .04

40 50 60 70 80 40 50 60 70 80

Car Truck

GPM

Footprint

Manufacturer Model Name Plate Horsepower Footprint GPM MPG

Adjusted 

GPM 

Adjusted 

MPG

GPM % 

Adjustment

HONDA HONDA FIT FIT 109 39.5 0.01 69.40 0.0157 63.73 8.9%

TOYOTA

TOYOTA 

COROLLA COROLLA 126 42.5 0.01 69.94 0.0164 60.80 15.0%

FORD FORD FOCUS FOCUS FWD 140 41.7 0.02 61.94 0.0177 56.34 9.9%

GENERAL 

MOTORS

CHEVROLET 

MALIBU MALIBU 169 46.9 0.02 53.70 0.0185 54.08 -0.7%
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-31 

Table 2-5 - Sample Adjustments for Horsepower to Weight, Trucks 

Manufacturer Model Name Plate Horsepower Footprint GPM MPG

Adjusted 

GPM 

Adjusted 

MPG

GPM % 

Adjustment

FORD FORD ESCAPE ESCAPE FWD 153 65.2 0.02 51.00 0.0181 55.11 -7.5%

GENERAL 

MOTORS

CHEVROLET 

C15

C15 SILVERADO 

2WD 119WB 195 55.9 0.03 39.76 0.0248 40.29 -1.3%

FIAT

JEEP GRAND 

CHEROKEE

GRAND CHEROKEE 

4WD 210 47.1 0.02 41.45 0.0222 44.98 -7.9%

HONDA HONDA PILOT PILOT 4WD 244 51.3 0.02 40.95 0.0243 41.22 -0.6%

TOYOTA

TOYOTA 

HIGHLANDER

HIGHLANDER 

4WD 270 49.0 0.02 45.90 0.0227 44.05 4.2%

FORD FORD F150

F150 FFV 4WD 

145 WB 300 67.4 0.03 32.70 0.0334 29.97 9.1%

FIAT DODGE RAM

RAM 1500 PICKUP 

4WD 140 WB 345 66.3 0.03 33.75 0.0316 31.65 6.6%

TOYOTA TUNDRA

TOYOTA TUNDRA 

4WD 145 WB 381 68.7 0.03 32.07 0.0325 30.73 4.3%

TATA

LAND ROVER 

RANGE ROVER 

SPORT

RANGE ROVER 

SPORT 300 47.5 0.03 33.17 0.0239 41.92 -20.9%

GENERAL 

MOTORS

CHEVROLET 

UPLANDER UPLANDER FWD 240 49.2 0.02 45.46 0.0163 61.34 -25.9%

GENERAL 

MOTORS HUMMER H3 H3 4WD 242 50.7 0.03 36.71 0.0242 41.30 -11.1%

GENERAL 

MOTORS

PONTIAC 

TORRENT TORRENT FWD 185 48.2 0.02 46.64 0.0215 46.56 0.2%

TOYOTA TACOMA

TOYOTA TACOMA 

4WD 236 53.4 0.02 43.01 0.0252 39.63 8.5%

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

HONDA

HONDA 

ACCORD

ACCORD 4DR 

SEDAN 190 46.6 0.02 57.57 0.0179 55.73 3.3%

NISSAN INFINITI G37 G37 COUPE 330 47.6 0.02 47.83 0.0200 50.08 -4.5%

GENERAL 

MOTORS

CHEVROLET 

CORVETTE CORVETTE 400 46.3 0.02 40.84 0.0251 39.83 2.5%

FORD

FORD 

MUSTANG MUSTANG 500 46.7 0.03 31.32 0.0316 31.67 -1.1%

TOYOTA

TOYOTA 

CAMRY

CAMRY SOLARA 

CONVERTIBLE 225 46.9 0.02 50.87 0.0191 52.27 -2.7%

VOLKSWAGEN

VOLKSWAGEN 

JETTA JETTA 170 42.4 0.02 46.77 0.0211 47.47 -1.5%

FORD FORD FUSION FUSION FWD 160 46.1 0.02 59.96 0.0168 59.61 0.6%

HONDA

HONDA 

ACCORD

ACCORD 2DR 

COUPE 190 46.6 0.02 56.92 0.0178 56.26 1.2%

HYUNDAI

HYUNDAI 

SONATA SONATA 162 46.0 0.02 61.72 0.0166 60.34 2.3%

HONDA HONDA CIVIC CIVIC 140 43.2 0.02 64.25 0.0177 56.38 14.0%
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-32 

analyses are with the final rulemaking fleet projections presented in a memorandum available 

in NHTSA’s docket.ccc

The above approaches resulted in three data sets each for (a) vehicles without added 

technology and (b) vehicles with technology added to reduce technology differences, any of 

which may provide a reasonable basis for fitting mathematical functions upon which to base 

the slope of the standard curves: (1) vehicles without any further adjustments; (2) vehicles 

with adjustments reflecting differences in “density” (weight/footprint); and (3) vehicles with 

adjustments reflecting differences in “density,” and adjustments reflecting differences in 

performance (power/weight). Further, these sets were developed for both the revised MY 

2008-based fleet projection and the post-proposal MY 2010-based fleet projection. Detailed 

results using these market forecasts are presented in a memorandum available in NHTSA’s 

docket.ddd

2.4.2.5 What statistical methods did the agencies evaluate? 

Using these data sets, the agencies tested a range of regression methodologies, each 

judged to be possibly reasonable for application to at least some of these data sets. 

2.4.2.6 Regression Approach 

In the MYs 2012-2016 final rules, the agencies employed a robust regression approach 

(minimum absolute deviation, or MAD), rather than an ordinary least squares (OLS) 

regression.11 MAD is generally applied to mitigate the effect of outliers in a dataset, and thus 

was employed in that rulemaking as part of our interest in attempting to best represent the 

underlying technology. NHTSA had used OLS in early development of attribute-based 

CAFE standards, but NHTSA (and then NHTSA and EPA) subsequently chose MAD instead 

of OLS for both the MY 2011 and the MYs 2012-2016 rulemakings. These decisions on 

regression technique were made both because OLS gives additional emphasis to outliers12 and 

because the MAD approach helped achieve the agencies’ policy goals with regard to curve 

slope in those rulemakings.13 In the interest of taking a fresh look at appropriate regression 

methodologies as promised in the 2012-2016 light duty rulemaking, in developing this 

proposal, the agencies gave full consideration to both OLS and MAD. The OLS 

representation, as described, uses squared errors, while MAD employs absolute errors and 

thus weights outliers less. 

As noted, one of the reasons stated for choosing MAD over least square regression in 

the MYs 2012-2016 rulemaking was that MAD reduced the weight placed on outliers in the 

data. As seen in Figure 2-1, there clearly are some outliers in the data, mostly to the high CO2

and fuel consumption side. However, the agencies have further considered whether it is 

appropriate to classify these vehicles as outliers. Unlike in traditional datasets, these vehicles’ 

performance is not mischaracterized due to errors in their measurement, a common reason for 

 

ccc Docket No. NHTSA-2010-0131.

ddd Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-33 

outlier classification. Being certification data, the chances of large measurement errors 

should be near zero, particularly towards high CO2 or fuel consumption. Thus, they can only 

be outliers in the sense that the vehicle designs are unlike those of other vehicles. These 

outlier vehicles may include performance vehicles, vehicles with high ground clearance, 

4WD, or boxy designs. Given that these are equally legitimate on-road vehicle designs, the 

agencies concluded that it would appropriate to reconsider the treatment of these vehicles in 

the regression techniques. 

Based on these considerations as well as on the adjustments discussed above, the 

agencies concluded it was not meaningful to run MAD regressions on gpm data that had 

already been adjusted in the manner described above. Normalizing already reduced the 

variation in the data, and brought outliers towards average values. This was the intended 

effect, so the agencies deemed it unnecessary to apply an additional remedy to resolve an 

issue that had already been addressed, but we sought comment on the use of robust regression 

techniques under such circumstances. One commenter, ACEEE, addressed this question in 

this rulemaking, indicating (consistent with the agencies’ views) that MAD and OLS are both 

technically sound methods for fitting functions. 

2.4.2.7 Sales Weighting 

Likewise, in the proposal, the agencies reconsidered the application of sales-weighting 

to represent the data. As explained below, the decision to sales weight or not is ultimately 

based upon a choice about how to represent the data, and not by an underlying statistical 

concern. Sales weighting is used if the decision is made to treat each (mass produced) unit 

sold as a unique physical observation. Doing so thereby changes the extent to which different 

vehicle model types are emphasized as compared to a non-sales weighted regression. For 

example, while total General Motors Silverado (332,000) and Ford F-150 (322,000) sales 

differed by less than 10,000 in MY 2021 market forecast used for the NPRM, 62 F-150s 

models and 38 Silverado models were reported in the agencies baselines. Without salesweighting, the F-150 models, because there were more of them, were given 63 percent more 

weight in the regression despite comprising a similar portion of the marketplace and a 

relatively homogenous set of vehicle technologies. 

The agencies did not use sales weighting in the MYs 2012-2016 rulemaking analysis 

of the curve shapes. A decision to not perform sales weighting reflects judgment that each 

vehicle model provides an equal amount of information concerning the underlying 

relationship between footprint and fuel economy. Sales-weighted regression gives the highest 

sales vehicle model types vastly more emphasis than the lowest-sales vehicle model types 

thus driving the regression toward the sales-weighted fleet norm. For unweighted regression, 

vehicle sales do not matter. The agencies note that the light truck market forecast shows MY 

2025 sales of 218,000 units for Toyota’s 2WD Sienna, and shows 66 model configurations 

with MY 2025 sales of fewer than 100 units. Similarly, the agencies’ market forecast shows 

MY 2025 sales of 267,000 for the Toyota Prius, and shows 40 model configurations with 

MY2025 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota 

Sienna and Prius more than a thousand times the consideration of many vehicle model 

configurations. Sales-weighted analysis would, therefore, cause a large number of vehicle 

model configurations to be virtually ignored in the regressions.14

 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-34 

However, the agencies did note in the MYs 2012-2016 final rules that, “sales weighted 

regression would allow the difference between other vehicle attributes to be reflected in the 

analysis, and also would reflect consumer demand.” 15 In reexamining the sales-weighting for 

this analysis, the agencies note that there are low-volume model types that account for many 

of the passenger car model types (50 percent of passenger car model types account for 3.3 

percent of sales), and it is unclear whether the engineering characteristics of these model types 

should equally determine the standard for the remainder of the market. 

In the interest of taking a fresh look at appropriate methodologies as promised in the 

last final rule, in developing proposed and final standards for MYs 2017-2025, the agencies 

gave full consideration to both sales-weighted and unweighted regressions. 

2.4.2.8 Analyses Performed 

We performed regressions describing the relationship between a vehicle’s CO2/fuel 

consumption and its footprint, in terms of various combinations of factors: initial (raw) fleets 

with no technology, versus after technology is applied; sales-weighted versus non-sales 

weighted; and with and without two sets of normalizing factors applied to the observations. 

The agencies excluded diesels and dedicated AFVs because the agencies anticipate that 

advanced gasoline-fueled vehicles are likely to be dominant through MY2025. 

Results supporting development of the proposed and finalized standards are depicted 

graphically in Figures 2-9 through 2-16, below. 

Thus, the basic OLS regression on the initial data (with no technology applied) and no 

sales-weighting represents one perspective on the relation between footprint and fuel 

economy. Adding sales weighting changes the interpretation to include the influence of sales 

volumes, and thus steps away from representing vehicle technology alone. Likewise, MAD is 

an attempt to reduce the impact of outliers, but reducing the impact of outliers might perhaps 

be less representative of technical relationships between the variables, although that 

relationship may change over time in reality. Each combination of methods and data reflects 

a perspective, and the regression results reflect that perspective in a simple quantifiable 

manner, expressed as the coefficients determining the line through the average (for OLS) or 

the median (for MAD) of the data. It is left to policy makers to determine an appropriate 

perspective and to interpret the consequences of the various alternatives.

The agencies sought comment on the application of the weights as described above, 

and the implications for interpreting the relationship between fuel efficiency and footprint. 

ACEEE questioned adjustment of the light truck data based on differences in weight/footprint, 

indicating that, in their view, the adjustment produces too steep a slope and potentially 

implies overstatement of the efficacy of some technologies as applied to pickup trucks. 

ACEEE also suggested that adjustment based on differences in power/weight would yield 

flatter curves and be more consistent with how the EU constructed related CO2 targets. The 

Alliance, in contrast, supported the weightings applied by the agencies, and the resultant 

relationships between fuel efficiency and footprint. Both ACEEE and the Alliance 

commented that the agencies should revisit the application of weights—and broader aspects 

of analysis to develop mathematical functions—in the future. Moreover, although ACEEE 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-35 

expressed concern regarding the outcomes of the application of the weight/footprint 

adjustment, the agencies maintain that the adjustments (including no adjustments) considered 

in the NPRM are all potentially reasonable to apply for purposes of developing fuel economy 

and GHG target curves. This issue is discussed in greater detail in Section II.C of the 

preamble, and related issues–the slope and stringency of the light truck standards—are 

addressed further in Sections III and IV of the preamble. 

2.4.2.9 What results did the agencies obtain? 

Both agencies employed the same statistical approaches. For regressions against data 

including technology normalization, NHTSA used the CAFE modeling system, and EPA used 

EPA’s OMEGA model. The agencies obtained similar regression results, and based the joint 

proposal on those obtained by NHTSA. 

For illustrative purposes, the set of figures below show the range of curves determined 

by the possible combinations of regression techniques, with and without sales weighting, with 

and without the application of technology, and with various adjustments to the gpm variable 

prior to running a regression. Again, from a statistical perspective, each of these regressions 

simply represents the assumptions employed. Since they are all univariate linear regressions, 

they describe the line that will result from minimizing the sum of the residuals (for MAD) or 

sum of squared residuals (for OLS). Figures show the results for passenger cars, then light 

trucks, for ordinary least squares (OLS) then similar results for MAD regressions for cars and 

light trucks, respectively. The various equations are represented by the string of attributes 

used to define the regression. See the table, Regression Descriptors, below, for the legend. 

Thus, for example, the line representing “ols_LT_wt_ft_adj_init_w” should be read as 

follows: an OLS regression, for light trucks, using data adjusted according to weight to 

footprint, no technology added, and weighted by sales. 

Table 2-6 Regression Descriptors 

Notation Description

ols or mad Ordinary least squares or mean absolute deviation

PC or LT Passenger car or light truck

hp_wt_adj Adjustment for horsepower to weight

wt_ft_adj Adjustment for weight to footprint

wt_ft_hp_wt_adj Adjustment for both horsepower to weight and weight to footprint

init or final Vehicles with no technology (initial) or with technology added (final)

u or w Unweighted or weighted by sales
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-36 

Thus, the next figures, for example, represent a family of curves (lines) fit using ordinary least 

squares on data for passenger cars, not modified for technology, and which therefore permits 

comparisons of results in terms of the factors that change in each regression. These factors 

are whether the data are sales-weighted (denoted “w”) or unweighted (denoted “u”), as well as 

the adjustments described above. Each of these adjustments has an influence on the 

regressions results, depicted in the figures below. 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. See section 2.6 below. 

Detailed results of the analysis with the final rulemaking fleet projections are presented in a 

memorandum available in NHTSA’s docket.eee

 

eee Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-37 

Figure 2-9 Best Fit Results for Various Regressions: Cars, No Added Technology, OLS 

Figure 2-10, below, shows comparable results, this time with data representing the 

additional technology that has been added to reduce technological heterogeneity. Note that 

the data now pass through the relevant data “cloud” for the fleet with the technology 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-38 

adjustment applied. The slopes of the lines are somewhat more clustered (less divergent) in 

the chart depicting added technology (as discussed in footnote ii) 

Figure 2-10 Best Fit Results for Various Regressions: Cars, with Added Technology, OLS 

Similar to the figures displaying the results for passenger cars, the figures below 

display regression lines for trucks, first with no technology added, then subsequently, for the 

case where technology has been added. Slopes appear more similar to each other here than of 

passenger cars. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-39 

Figure 2-11 Best Fit Results for Various Regressions: Trucks, No Added Technology, OLS 

.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-40 

Figure 2-12 Best Fit Results for Various Regressions: Trucks, With Added Technology, OLS 

Figure 2-13, below, displays regression results for the passenger car MAD fitted 

curves. The technology adjustment does not have, however, the same degree of impact in 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-41 

reducing the difference in the attained slopes (between those with and without the addition of 

technology) evidenced in the OLS regressions. 

Figure 2-13 Best Fit Results for Various Regressions: Cars, No Added Technology, MAD 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-42 

Figure 2-14 Best Fit Results for Various Regressions: Cars, Added Technology, MAD 

The MAD regression results below in Figure 2-15 show a grouping of the fitted lines 

similar to that displayed in the OLS fits for trucks. As expected, an additional reduction in 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-43 

divergence is seen in the case where technology has been added, in Figure 2-15, which can be 

ascribed to the reduction in heterogeneity of the fleet brought about by the addition of the 

technology. 

Figure 2-15 Best Fit Results for Various Regressions: Trucks, No Added Technology, MAD 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-44 

Figure 2-16 Best Fit Results for Various Regressions: Trucks, with Added Technology, MAD 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projections yielded results generally similar to those shown above. Detailed results of the 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-45 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.fff

2.4.2.10 Which methodology did the agencies choose for the proposal, and why was it 

reasonable?

For the proposal, the choice among the alternatives presented above was to use the 

OLS formulation, on sales-weighted data, using a fleet that has had technology applied, and 

after adjusting the data for the effect of weight-to-footprint, as described above. The agencies 

believe that this represented a technically reasonable approach for purposes of developing 

target curves to define the proposed standards, and that it represents a reasonable trade-off 

among various considerations balancing statistical, technical, and policy matters, which 

include the statistical representativeness of the curves considered and the steepness of the 

curve chosen. The agencies judged the application of technology prior to curve fitting to 

provide a reasonable means—one consistent with the rule’s objective of encouraging 

manufacturers to add technology in order to increase fuel economy and reduce GHG 

emissions—of reducing variation in the data and thereby helping to estimate a relationship 

between fuel consumption/CO2 and footprint. 

Similarly, for the agencies’ NPRM MY 2008-based market-forecast and the agencies’ 

estimates of future technology effectiveness, the inclusion of the weight-to-footprint data 

adjustment prior to running the regression also helped to improve the fit of the curves by 

reducing the variation in the data, and the agencies believed that the benefits of this 

adjustment for the proposed rule likely outweighed the potential that resultant curves might 

somehow encourage reduced load carrying capability or vehicle performance (note that we 

were not suggesting that we believed these adjustments would reduce load carrying capability 

or vehicle performance). In addition to reducing the variability, the truck curve was also 

steepened, and the car curve flattened compared to curves fitted to sales weighted data that do 

not include these normalizations. The agencies agreed with manufacturers of full-size pick-up 

trucks that in order to maintain towing and hauling utility, the engines on pick-up trucks must 

be more powerful, than their low “density” nature statistically suggested based on the 

agencies’ NPRM MY 2008-based market forecast and the agencies’ estimates of the 

effectiveness of different fuel-saving technologies. Therefore, the agencies judged that it may 

be more appropriate (i.e., in terms of relative compliance challenges faced by different light 

truck manufacturers) to adjust the slope of the curves defining fuel economy and CO2 targets. 

The results of the normalized regressions are displayed in Table, below.ggg

Table 2-7 Regression Results 

 

fff Docket No. NHTSA-2010-0131.

ggg As presented in the draft TSD supporting the NPRM, this table erroneously reported coefficients from the 

regression using normalization based on differences in horsepower to weight rather than differences in weight 

per footprint. The differences in this Table as presented in this final TSD reflect this correction. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-46 

Vehicle Slope

(gallons/mile)

Constant

(gallons/mile)

Passenger cars 0.00037782 0.00181033

Light trucks 0.00038891 0.00401336

Updating this analysis using the corrected MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.hhh

As described above, however, other approaches are also technically reasonable, and 

also represent a way of expressing the underlying relationships. The agencies revisited the 

analysis for the final rule, after correcting the underlying MY 2008 based market forecast, 

developing a MY 2010 based market forecast, updating estimates of technology effectiveness 

and cost, and after considering relevant public comments. As presented below in section 2.6, 

results of these updated analyses were generally similar to those supporting the NPRM 

analysis results, and the agencies’ balancing of considerations led the agencies to select final 

curves unchanged from the NPRM curves. 

As shown in the figures below, the line represents the sales-weighted OLS regression 

fit of gallons per mile regressed on footprint, with the proposal data first adjusted by weight to 

footprint, as described above. This introduces weight as an additional consideration into the 

slope of the footprint curve, although in a manner that adjusts the data as described above, and 

thus maintains a simple graphical interpretation of the curve in a two dimensional space 

(gallons per mile and footprint). 

 

hhh Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-47 

Figure 2-17 Gallons per Mile versus Footprint, Cars 

(Data adjusted by weight to footprint). 

0

0.01

0.02

0.03

0.04

0.05

0.06

30 35 40 45 50 55 60 65 70 75 80 Weight per Footprint Adjusted GPM

Footprint

GPM vs. Footprint - Cars
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-48 

Figure 2-18 Gallons per Mile versus Footprint, Trucks 

(data adjusted by weight to footprint). 

Updating this analysis using the revised MY 2008- and the MY 2010-based fleet 

projection yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.iii

In the preceding two figures, passenger car and light truck data is represented for the 

specification chosen, with the size of the observation scaled to sales. The agencies note with 

regard to light trucks that for the MYs 2012-2016 analysis NPRM and final rule analyses, 

some models of pickups are aggregated , when, for example, the same pickup had been 

available in different cab configurations with different wheelbases.16

 For the analysis 

presented above, these models have been disaggregated and are represented individually, 

which leads to a slightly different outcome in the regression results than had they remained 

aggregated. 

 

iii Docket No. NHTSA-2010-0131.

0

0.01

0.02

0.03

0.04

0.05

0.06

30 35 40 45 50 55 60 65 70 75 80 Weight per Footprint Adjusted GPM

Footprint

GPM vs. Footprint - Trucks
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-49 

2.4.2.11 Implications of the adopted slopes compared to the slopes in MYs 2012-2016 

Rules 

The slope first proposed, and now adopted by the agencies has several implications 

relative to the MY 2016 curves, with the majority of changes affecting the truck curve. The 

selected car curve has a slope similar to that finalized in the MYs 2012-2016 rulemaking (4.7 

g/mile in MY 2016, vs. 4.5 g/mile proposed in MY 2017). By contrast, the truck curve is 

steeper in MY 2017 than in MY 2016 (4.0 g/mile in MY 2016 vs. 4.9 g/mile in MY 2017). 

As discussed previously, a steeper slope relaxes the stringency of targets for larger vehicles 

relative to those for smaller vehicles, thereby shifting relative compliance burdens among 

manufacturers based on their respective product mix. Comments regarding the slope of the 

agencies’ proposed curves are discussed in Section II.C of the preamble to today’s final rule. 

2.5 Once the agencies determined the appropriate slope for the sloped part, how did the 

agencies determine the rest of the mathematical function? 

The agencies continue to believe that without a limit at the smallest footprints, the 

function—whether logistic or linear—can reach values that would be unfairly burdensome for 

a manufacturer that elects to focus on the market for small vehicles; depending on the 

underlying data, an unconstrained form could result in stringency levels that are 

technologically infeasible and/or economically impracticable for those manufacturers that 

may elect to focus on the smallest vehicles. On the other side of the function, without a limit 

at the largest footprints, the function may provide no floor on required fuel economy. Also, 

the safety considerations that support the provision of a disincentive for downsizing as a 

compliance strategy apply weakly, if at all, to the very largest vehicles. Limiting the 

function’s value for the largest vehicles thus leads to a function with an inherent absolute 

minimum level of performance, while remaining consistent with safety considerations. 

Just as for slope, in determining the appropriate footprint and fuel economy values for 

the “cutpoints,” the places along the curve where the sloped portion becomes flat, the 

agencies took a fresh look for purposes of this rulemaking, taking into account the updated 

market forecasts and new assumptions about the availability of technologies. The next two 

sections discuss the agencies’ approach to cutpoints for the passenger car and light truck 

curves separately, as the policy considerations for each vary somewhat. 

2.5.1 Cutpoints for Passenger Car curve 

The passenger car fleet upon which the agencies based the proposed target curves for 

MYs 2017-2025 was derived from MY 2008 data, as discussed above. In MY 2008, 

passenger car footprints ranged from 36.7 square feet, the Lotus Exige 5, to 69.3 square feet, 

the Daimler Maybach 62. In that fleet, several manufacturers offer small, sporty coupes 

below 41 square feet, such as the BMW Z4 and Mini, Honda S2000, Mazda MX-5 Miata, 

Porsche Carrera and 911, and Volkswagen New Beetle. Because such vehicles represent a 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-50 

small portion (less than 10 percent) of the passenger car market, yet often have performance, 

utility, and/or structural characteristics that could make it technologically infeasible and/or 

economically impracticable for manufacturers focusing on such vehicles to achieve the very 

challenging average requirements that could apply in the absence of a constraint, EPA and 

NHTSA again proposed to cut off the sloped portion of the passenger car function at 41 

square feet, consistent with the MYs 2012-2016 rulemaking. The agencies recognized that for 

manufacturers who make small vehicles in this size range, putting the cutpoint at 41 square 

feet creates some incentive to downsize (i.e., further reduce the size, and/or increase the 

production of models currently smaller than 41 square feet) to make it easier to meet the 

target. Putting the cutpoint here may also create the incentive for manufacturers who do not 

currently offer such models to do so in the future. However, at the same time, the agencies 

believe that there is a limit to the market for cars smaller than 41 square feet -- most 

consumers likely have some minimum expectation about interior volume, among other things. 

The agencies thus believe that the number of consumers who will want vehicles smaller than 

41 square feet (regardless of how they are priced) is small, and that the incentive to downsize 

to less than 41 square feet in response to this proposal, if present, will be at best minimal. On 

the other hand, the agencies note that some manufacturers are introducing mini cars not 

reflected in the agencies MY 2008-based market forecast, such as the Fiat 500, to the U.S. 

market, and that the footprint at which the curve is limited may affect the incentive for 

manufacturers to do so. 

Above 56 square feet, the only passenger car models present in the MY 2008 fleet 

were four luxury vehicles with extremely low sales volumes—the Bentley Arnage and three 

versions of the Rolls Royce Phantom. As in the MYs 2012-2016 rulemaking, NHTSA and 

EPA therefore proposed again to cut off the sloped portion of the passenger car function at 56 

square feet.jjj

While meeting with manufacturers prior to issuing the proposal, the agencies received 

comments from some manufacturers that, combined with slope and overall stringency, using 

41 square feet as the footprint at which to cap the target for small cars would result in unduly 

challenging targets for small cars. The agencies do not agree. No specific vehicle need meet 

its target (because standards apply to fleet average performance), and maintaining a sloped 

function toward the smaller end of the passenger car market is important to discourage unsafe 

downsizing, the agencies thus proposed to again “cut off” the passenger car curve at 41 square 

feet, notwithstanding these comments. 

. The agencies discuss the comments that were received for the cutpoints on both 

passenger car and light truck curves in the next section. 

 

jjj The MY 2010 based market forecast has a similarly small number of cars above a footprint of 56 sq ft. These 

nine vehicle models include 5 Rolls Royce models, a Maybach 57-S and three BMW vehicles, with fewer than 

20,000 total projected sales in any model year during this timeframe. 
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2.5.2 Cutpoints for Light Truck curve 

The light truck fleet upon which the agencies based the proposed target curves for 

MYs 2017-2025, like the passenger car fleet, was derived from MY 2008 data, as discussed in 

Section 2.4 above. In MY 2008, light truck footprints ranged from 41.0 square feet, the Jeep 

Wrangler, to 77.5 square feet, the Toyota Tundra. For consistency with the curve for 

passenger cars, the agencies proposed to cut off the sloped portion of the light truck function 

at the same footprint, 41 square feet, although we recognized that no light trucks are currently 

offered below 41 square feet. With regard to the upper cutpoint, the agencies heard from a 

number of manufacturers during the discussions leading up to the proposal of the MYs 2017-

2025 standards that the location of the cutpoint in the MYs 2012-2016 rules, 66 square feet, 

resulted in very challenging targets for the largest light trucks in the later years of that 

rulemaking (although, because CAFE and GHG standards are based on average performance, 

manufacturers to not need to ensure that every vehicle model meets its fuel economy and 

GHG targets). See 76 FR at 74864-65. Those manufacturers requested that the agencies 

extend the cutpoint to a larger footprint, to reduce targets for the largest light trucks which 

represent a significant percentage of those manufacturers’ light truck sales. At the same time, 

in re-examining the light truck fleet data, the agencies concluded that aggregating pickup 

truck models in the MYs 2012-2016 rule had led the agencies to underestimate the impact of 

the different pickup truck model configurations above 66 square feet on manufacturers’ fleet 

average fuel economy and CO2 levels (as discussed immediately below). In disaggregating 

the pickup truck model data, the impact of setting the cutpoint at 66 square feet after model 

year 2016 became clearer to the agencies. 

In the agencies’ view, these comments have a legitimate basis. The agencies’ market 

forecast used at proposal includes about 24 vehicle configurations above 74 square feet with a 

total volume of about 50,000 vehicles or less during any MY in the 2017-2025 time frame.kkk 

While a relatively small portion of the overall truck fleet, for some manufacturers, these 

vehicles are a non-trivial portion of their sales. As noted above, the very largest light trucks 

have significant load-carrying and towing capabilities that make it particularly challenging for 

manufacturers to add fuel economy-improving/CO2-reducing technologies in a way that 

maintains the full functionality of those capabilities.lll

 Considering manufacturer CBI and our 

estimates of the impact of the 66 square foot cutpoint for future model years, the agencies 

determined to adopt curves that transition to a different cut point. While noting that no 

specific vehicle need meet its target (because standards apply to fleet average performance), 

we believe that the information provided to us by manufacturers (i.e., information provided 

regarding the accumulated impacts, especially on manufacturers’ credit balances, of CAFE 

standards since MY2005 and GHG standards since MY2012) and our own analysis supported 

the gradual extension of the cutpoint for large light trucks in the proposal from 66 square feet 

 

kkk In the MY2010 based market forecast, there are 14 vehicle configurations with a total volume of 130,000 

vehicles or less during any MY in the 2017-2025 time frame. This is a similarly small portion of the overall 

number of vehicle models or vehicle sales. 

lll Comments on this issue are discussed in section 0.
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in MY 2016 out to a larger footprint square feet before MY 2025. The agencies’ analyses 

with regard to this topic, and how it relates to the stringency of the standards, are presented in 

preamble sections III.D and IV.F and summarized in preamble section II.C. 

Figure 2-19 Footprint Distribution by Car and Truck* 

*Proposed truck cutpoints for MY 2025 shown in red, car cutpoints shown in green 

Updating this analysis using the revised MY 2008- and the MY 2010-based market 

forecasts yielded results generally similar to those shown above. Detailed results of the 

analyses with the final rulemaking fleet projections are presented in a memorandum available 

in NHTSA’s docket.mmm

 

The agencies proposed to phase in the higher cutpoint for the truck curve in order to 

avoid any backsliding from the MY 2016 standard. A target that is feasible in one model year 

should never become less feasible in a subsequent model year since manufacturers should 

have no reason to remove fuel economy-improving/CO2-reducing technology from a vehicle 

 

mmm Docket No. NHTSA-2010-0131.
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-53 

once it has been applied. Put another way, the agencies proposed to not allow “curve 

crossing” from one model year to the next. In proposing MYs 2011-2015 CAFE standards 

and promulgating MY 2011 standards, NHTSA proposed and requested comment on avoiding 

curve crossing, as an “anti-backsliding measure.”17 The MY 2016 2-cycle test curves are 

therefore a floor for the MYs 2017-2025 curves. For passenger cars, which have minimal 

change in slope from the MY 2012-2016 rulemakings and no change in cut points, there were 

no curve crossing issues in the proposed (or final) standards. 

The minimum stringency determination was done using the two-cycle curves. 

Stringency adjustments for air conditioning and other credits were calculated after curves that 

did not cross were determined in two-cycle space. The year over year increase in these 

adjustments cause neither the GHG nor CAFE curves (with A/C) to contact the 2016 curves 

when charted. 

The agencies received some comments on the selection of these cutpoints. ACEEE 

commented that the extension of the light truck cutpoint upward from 66 s.f. to 74 s.f. would 

reduce stringency for large trucks even though there is no safety-related reason to discourage 

downsizing of these trucks. Sierra Club and Volkswagen commented that moving this 

cutpoint could encourage trucks to get larger and may be detrimental to societal fatalities. 

Global Automakers commented that the cutpoint for the smallest light trucks should be set at 

approximately ten percent of sales (as for passenger cars) rather than at 41 square feet. 

Conversely, IIHS commented that, for both passenger cars and light trucks, the 41 s.f. 

cutpoint should be moved further to the left (i.e., to even smaller footprints), to reduce the 

incentive for manufacturers to downsize the lightest vehicles. 

The agencies have considered these comments regarding the cutpoint applied to the 

high footprint end of the target function for light trucks, and we judge there to be minimal risk 

that manufacturers would respond to this upward extension of the cutpoint by deliberately 

increasing the size of light trucks that are already at the upper end of marketable vehicle sizes, 

particularly as gasoline prices may continue to increase in the future. Such vehicles have 

distinct size, maneuverability, fuel consumption, storage, and other characteristics which 

differ from vehicles between 43 and 48 square feet, and are likely not be suited for all 

consumers in all usage scenarios. Further, larger vehicles typically also have additional 

production costs that make it unlikely that the sales of these vehicles will increase in response 

to changes in the cutpoint. Therefore, we remain concerned that not to extend this cutpoint to 

74 s.f. would fail to take into adequate consideration the challenges to improving fuel 

economy and CO2 emissions to the levels required by this final rule for vehicles with 

footprints larger than 66 s.f., given their increased utility, As noted above, while 

manufacturers are not required to ensure that every vehicle model meets its target, the 

agencies are concerned that standards with more stringent targets for large trucks would 

unduly burden full-line manufacturers active in the market for full-size pickups and other 

large light trucks, as discussed earlier, and evidenced by the agencies’ estimates of differences 

between compliance burdens faced by OEMs active and not active in the market for full-size 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-54 

pickups. While some manufacturers have recently indicatednnn that buyers are currently 

willing to pay a premium for fuel economy improvements, the agencies are concerned that 

disparities in long-term regulatory requirements could lead to future market distortions 

undermining the economic practicability of the standards. Absent an upward extension of the 

cutpoint, such disparities would be even greater. For these reasons, the agencies do not 

expect that gradually extending the cutpoint to 74 s.f will incentivize the upsizing of large 

trucks and, thus, believe there will be no adverse effects on societal safety. Therefore, we are 

promulgating standards that, as proposed, gradually extend the truck curve cutpoint to 74 s.f. 

We have also considered the above comments by Global Automakers and IIHS on the 

cutpoints for the smallest passenger cars and light trucks. In our judgment, placing these 

cutpoints at 41 square feet continues to strike an appropriate balance between (a) not 

discouraging manufacturers from introducing new small vehicle models in the U.S. and (b) 

not encouraging manufacturers to downsize small vehicles. 

2.5.3 Once the agencies determined the complete mathematical function shape, how 

did the agencies adjust the curves to develop the proposed standards and 

regulatory alternatives? 

The curves discussed above all reflect the addition of technology to individual vehicle 

models to reduce technology differences between vehicle models before fitting curves. This 

application of technology was conducted not to directly determine the proposed standards, but 

rather for purposes of technology adjustments, and set aside considerations regarding 

potential rates of application (i.e., phase-in caps), and considerations regarding economic 

implications of applying specific technologies to specific vehicle models. The following 

sections describe further adjustments to the curves discussed above, that affect both the shape 

of the curve (section 2.5.3.1), and the location of the curve (2.5.3.2), that helped the agencies 

determine curves that defined the proposed standards. 

2.5.3.1 Adjusting for Year over Year Stringency 

As in the MYs 2012-2016 rules, the agencies developed curves defining regulatory 

alternatives for consideration by “shifting” these curves. For the MYs 2012-2016 rules, the 

agencies did so on an absolute basis, offsetting the fitted curve by the same value (in gpm or 

g/mi) at all footprints. In developing the proposal for MYs 2017-2025, the agencies 

reconsidered the use of this approach, and concluded that after MY 2016, curves should be 

offset on a relative basis—that is, by adjusting the entire gpm-based curve (and, equivalently, 

the CO2 curve) by the same percentage rather than the same absolute value. The agencies’ 

estimates of the effectiveness of these technologies are all expressed in relative terms—that is, 

each technology (with the exception of A/C) is estimated to reduce fuel consumption (the 

inverse of fuel economy) and CO2 emissions by a specific percentage of fuel consumption 

without the technology. It is, therefore, more consistent with the agencies’ estimates of 

 

nnn For example, in its June 11, 2012 edition, Automotive News quoted a Ford sales official saying that "fuel 

efficiency continues to be a top purchaser driver.” (“More MPG – ASAP”, Automotive News, Jun 11, 2012.) 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-55 

technology effectiveness to develop standards and regulatory alternatives by applying a 

proportional offset to curves expressing fuel consumption or emissions as a function of 

footprint. In addition, extended indefinitely (and without other compensating adjustments), 

an absolute offset would eventually (i.e., at very high average stringencies) produce negative 

(gpm or g/mi) targets. Relative offsets avoid this potential outcome. Relative offsets do 

cause curves to become, on a fuel consumption and CO2 basis, flatter at greater average 

stringencies; however, as discussed above, this outcome remains consistent with the agencies’ 

estimates of technology effectiveness. In other words, given a relative decrease in average 

required fuel consumption or CO2 emissions, a curve that is flatter by the same relative 

amount should be equally challenging in terms of the potential to achieve compliance through 

the addition of fuel-saving technology. 

On this basis, and considering that the “flattening” occurs gradually for the regulatory 

alternatives the agencies have evaluated, the agencies conclude that this approach to offsetting 

the curves to develop year-by-year regulatory alternatives neither re-creates a situation in 

which manufacturers are likely to respond to standards in ways that compromise highway 

safety, nor undoes the attribute-based standard’s more equitable balancing of compliance 

burdens among disparate manufacturers. The agencies sought comment on these conclusions, 

and on any other means that might avoid the potential negative outcomes discussed above. 

As indicated earlier, ACEEE and the Alliance both expressed support for the application of 

relative adjustments in order to develop year-over-year increases in the stringency of fuel 

consumption and CO2 targets, although the Alliance also commented that this approach 

should be revisited as part of the mid-term evaluation. 

2.5.3.2 Adjusting for anticipated improvements to mobile air conditioning systems 

The fuel economy values in the agencies’ market forecasts are based on the 2-cycle 

(i.e., city and highway) fuel economy test and calculation procedures that do not reflect 

potential improvements in air conditioning system efficiency, refrigerant leakage, or 

refrigerant Global Warming Potential (GWP). Recognizing that there are significant and cost 

effective potential air conditioning system improvements available in the rulemaking 

timeframe (discussed in detail below in Chapter 5), the agencies are increasing the stringency 

of the target curves based on the agencies’ assessment of the capability of manufacturers to 

implement these changes. For the proposed CAFE standards and alternatives, an offset was 

included based on air conditioning system efficiency improvements, as these improvements 

are the only improvements that effect vehicle fuel economy. For the proposed GHG standards 

and alternatives, a stringency increase was included based on air conditioning system 

efficiency, leakage and refrigerant improvements. As discussed in Chapter 5 of the joint 

TSD, the air conditioning system improvements affect a vehicle’s fuel efficiency or CO2

emissions performance as an additive stringency increase, as compared to other fuel 

efficiency improving technologies which are multiplicative. Therefore, in adjusting target 

curves for improvements in the air conditioning system performance, the agencies adjusted 

the target curves by additive stringency increases (or vertical shifts) in the curves. 

For the GHG target curves, the offset for air conditioning system performance is being 

handled in the same manner as for the MYs 2012-2016 rules. For the CAFE target curves, 

NHTSA for the first time is accounting for potential improvements in air conditioning system 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-56 

performance. Using this methodology, the agencies first use a multiplicative stringency 

adjustment for the sloped portion of the curves to reflect the effectiveness on technologies 

other that air conditioning system technologies, creating a series of curve shapes that are 

“fanned” based on two-cycle performance. Then the curves are offset vertically by the air 

conditioning improvement by an equal amount at every point. 

2.6 What does the agencies’ updated analysis indicate? 

As discussed above in Chapter 1, the agencies have used two different market 

forecasts to conduct analyses supporting today’s final rule. The first, referred to here as the 

“MY 2008-Based Fleet Projection,” is largely identical to that used for analysis supporting the 

NPRM, but includes some corrections (in particular, to the footprint of some vehicle models) 

discussed in Chapter 1 of this TSD. The second, referred to here as the “MY 2010-Based 

Fleet Projection,” is a post-proposal market forecast based on the MY 2010 fleet of vehicles; 

the development of this 2010 based fleet projection is discussed in Chapter 1. 

Having made these changes, the agencies repeated the normalization and statistical 

analyses describe above, following the same approaches as used in the analysis supporting the 

NPRM. The tables and charts that follow compare the results of NHTSA’s updated analysis 

to those of NHTSA’s prior analysis, and compare the resultant fitted lines to the lines (one 

each for passenger cars and light trucks) selected for purposes of developing the proposed 

attribute-based standards. The charts below present details of the results in graphical form. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-57 

Table 2-8 Fitted Coefficients (Slope in gpm/sf, Intercept in gpm), Passenger Cars 

Note 1: Coefficients selected for NPRM shown underlined. 

Note 2: “MY2008-Based Fleet Projection” refers to market forecast developed using (a) MY2008 vehicle models and 

characteristics, (b) AEO2011-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2009 by CSM (now owned by Global Insight). 

Note 3: “MY2010-Based Fleet Projection” refers to market forecast developed using (a) MY2010 vehicle models and 

characteristics, (b) AEO2012-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2011 by J.D. Power (automotive forecasting service now owned by LMC). 

Normalized for Technology DifferencesNormalized for Differences in Power/WeightNormalized for Differences in Weight/FootprintSales-WeightedRegression TechniqueSlope - NPRM AnalysisSlope - MY2008-Based Market ForecastSlope - MY2010-Based Market ForecastIntercept - NPRM AnalysisIntercept - MY2008-Based Market ForecastIntercept - MY2010-Based Market Forecast

Yes No No Yes OLS 0.000648 0.000510 0.000472 -0.01027 -0.00450 -0.00376

Yes No No No OLS 0.000513 0.000464 0.000502 0.00009 0.00184 -0.00076

Yes No No Yes MAD 0.000725 0.000560 0.000427 -0.01408 -0.00699 -0.00210

Yes No No No MAD 0.000359 0.000334 0.000445 0.00610 0.00650 0.00076

Yes Yes No Yes OLS 0.000431 0.000293 0.000248 -0.00052 0.00520 0.00643

Yes Yes No No OLS 0.000399 0.000351 0.000398 0.00336 0.00508 0.00221

Yes Yes Yes Yes OLS 0.000161 0.000131 0.000093 0.01155 0.01238 0.01349

Yes Yes Yes No OLS 0.000264 0.000250 0.000268 0.00844 0.00873 0.00736

No No No Yes MAD 0.001486 0.001220 0.001058 -0.03401 -0.02131 -0.01670

No No No No MAD 0.000942 0.000959 0.000995 -0.00507 -0.00572 -0.00944

No No No Yes OLS 0.001345 0.001175 0.001096 -0.02766 -0.01974 -0.01806

No No No No OLS 0.001109 0.001085 0.001099 -0.01122 -0.00983 -0.01259

No Yes No Yes OLS 0.000984 0.000800 0.000737 -0.01144 -0.00299 -0.00176

No Yes No No OLS 0.000920 0.000890 0.000933 -0.00579 -0.00425 -0.00785

No Yes Yes Yes OLS 0.000481 0.000452 0.000403 0.01103 0.01242 0.01336

No Yes Yes No OLS 0.000669 0.000673 0.000654 0.00367 0.00358 0.00319

Yes No Yes Yes OLS 0.000378 0.000348 0.000316 0.00181 0.00268 0.00330

Yes No Yes No OLS 0.000378 0.000362 0.000371 0.00517 0.00550 0.00440
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-58 

 Table 2-9 Fitted Coefficients (Slope in gpm/sf, Intercept in gpm), Light Trucks 

Note 1: Coefficients selected for NPRM shown underlined. 

Note 2: “MY2008-Based Market Forecast” refers to market forecast developed using (a) MY2008 vehicle models and 

characteristics, (b) AEO2011-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2009 by CSM (now owned by Global Insight). 

Note 3: “MY2010-Based Fleet Projection” refers to market forecast developed using (a) MY2010 vehicle models and 

characteristics, (b) AEO2012-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2011 by J.D. Power (automotive forecasting service now owned by LMC). 

Normalized for Technology DifferencesNormalized for Differences in Power/WeightNormalized for Differences in Weight/FootprintSales-WeightedRegression TechniqueSlope - NPRM AnalysisSlope - MY2008-Based Market ForecastSlope - MY2010-Based Market ForecastIntercept - NPRM AnalysisIntercept - MY2008-Based Market ForecastIntercept - MY2010-Based Market Forecast

Yes No No Yes OLS 0.000269 0.000251 0.000256 0.01036 0.01012 0.00976

Yes No No No OLS 0.000233 0.000229 0.000198 0.01457 0.01376 0.01477

Yes No No Yes MAD 0.000250 0.000245 0.000278 0.01104 0.01060 0.00832

Yes No No No MAD 0.000204 0.000210 0.000231 0.01567 0.01438 0.01248

Yes Yes No Yes OLS 0.000253 0.000239 0.000237 0.01122 0.01078 0.01078

Yes Yes No No OLS 0.000221 0.000220 0.000201 0.01509 0.01414 0.01448

Yes Yes Yes Yes OLS 0.000373 0.000347 0.000340 0.00487 0.00507 0.00526

Yes Yes Yes No OLS 0.000395 0.000374 0.000303 0.00541 0.00558 0.00864

No No No Yes MAD 0.000448 0.000452 0.000481 0.01995 0.01984 0.01654

No No No No MAD 0.000356 0.000349 0.000440 0.02872 0.02914 0.02139

No No No Yes OLS 0.000491 0.000483 0.000470 0.01784 0.01825 0.01756

No No No No OLS 0.000433 0.000432 0.000423 0.02480 0.02486 0.02283

No Yes No Yes OLS 0.000462 0.000453 0.000446 0.01941 0.01988 0.01890

No Yes No No OLS 0.000410 0.000409 0.000426 0.02575 0.02579 0.02245

No Yes Yes Yes OLS 0.000669 0.000662 0.000629 0.00849 0.00881 0.00903

No Yes Yes No OLS 0.000710 0.000708 0.000609 0.00909 0.00919 0.01199

Yes No Yes Yes OLS 0.000389 0.000359 0.000358 0.00401 0.00441 0.00425

Yes No Yes No OLS 0.000407 0.000383 0.000301 0.00489 0.00520 0.00892
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-59 

Figure 2-20 Fitted Lines, Passenger Cars, NPRM Analysis 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-60 

Figure 2-21 Fitted Lines, Passenger Cars, Corrected MY2008-Based Market Forecast 

Note 1: Line based on coefficients selected for NPRM shown for comparison. 

Note 2: “MY2008-Based Fleet Projection” refers to market forecast developed using (a) MY2008 vehicle models and 

characteristics, (b) AEO2011-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2009 by CSM (now owned by Global Insight). 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-61 

Figure 2-22 Fitted Lines, Passenger Cars, MY2010-Based Market Forecast 

Note 1: Line based on coefficients selected for NPRM shown for comparison. 

Note 2: “MY2010-Based Fleet Projection” refers to market forecast developed using (a) MY2010 vehicle models and 

characteristics, (b) AEO2012-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2011 by J.D. Power (automotive forecasting service now owned by LMC). 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-62 

Figure 2-23 Fitted Lines, Light Trucks, NPRM Analysis 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-63 

Figure 2-24 Fitted Lines, Light Trucks, Corrected MY2008-Based Market Forecast 

Note 1: Line based on coefficients selected for NPRM shown for comparison. 

Note 2: “MY2008-Based Fleet Projection” refers to market forecast developed using (a) MY2008 vehicle models and 

characteristics, (b) AEO2011-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2009 by CSM (now owned by Global Insight). 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-64 

Figure 2-25 Fitted Lines, Light Trucks, MY2010-Based Market Forecast 

Note 1: Line based on coefficients selected for NPRM shown for comparison. 

Note 2: “MY2010-Based Fleet Projection” refers to market forecast developed using (a) MY2010 vehicle models and 

characteristics, (b) AEO2012-based overall passenger car and light truck volumes, and (c) manufacturer- and segment-level 

shares from forecast provided late 2011 by J.D. Power (automotive forecasting service now owned by LMC). 

 As discussed above, the selection of a calibrated functional form—in this case, a 

specific line expressing a relationship between fuel consumption and footprint—upon which 

to base attribute-based fuel economy and related GHG standards involves considering not just 

the apparent range of the relevant technical relationship, but also the potential implications for 

affected policy issues. The approaches described above provide a range of reasonable means 

of estimating relationships between observed or adjusted fuel consumption and footprint. 

Having made corrections to the MY 2008-based fleet projection, and having 

developed a new MY 2010-based fleet projection, the agencies have obtained results 

generally similar, albeit not identical, to those obtained for the NPRM analysis. For any given 

method of estimating these lines, it is unlikely that the agencies could have obtained identical 

results after changing inputs. Also, there is no reason to expect that the MY 2008- and MY 

2010-based fleet projections should produce identical results. Still, these differences were 

mostly small. Using both the corrected MY 2008-based passenger car market forecast and the 

new MY 2010-based forecast, three techniques produced fitted passenger car lines very 

close—in terms of average squared differences within the range of footprints between the 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-65 

selected cutpoints discussed above—to those selected for the NPRM: sales-weighted OLS 

without normalizations for differences in power/weight or weight/footprint, sales-weighted 

OLS with normalization for differences weight/footprint, and unweighted OLS with 

normalizations for differences in both power/weight and weight/footprint. For light trucks, 

two techniques did so for both the corrected MY 2008-based passenger car market forecast 

and the post-proposal MY 2010-based forecast: unweighted OLS with normalizations for 

differences in both power/weight and weight/footprint, and unweighted OLS with 

normalization for differences weight/footprint. Without any normalizations applied to the set 

of footprint and fuel economy values, unweighted OLS produced fitted slopes within 2% of 

the values obtained through the corresponding unweighted OLS analysis conducted in support 

of the NPRM. Also, as the above charts show, the resultant ranges (i.e., areas in fuel 

consumption – footprint space) spanned by these methods are similar across the NPRM 

analysis and the updated analyses using the MY 2008- and MY 2010-based fleet projections. 

Considering that the agencies have adopted an approach whereby regulatory 

alternatives are developed by shifting fitted curves on a multiplicative basis, results of several 

of the techniques evaluated here thus would produce regulatory alternatives virtually identical 

to those developed for the NPRM. For the method that produced results selected for 

development of the NPRM, relative adjustment of lines fitted to the corrected MY 2008-based 

market forecast and the MY 2010-based market forecast produces lines that are, between the 

footprint cutpoints discussed above (41-56 ft2

and 41-74 ft2for passenger cars and light 

trucks, respectively), very close to the lines fitted for the NPRM (FIGURE Label): 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-66 

Figure 2-26 Sales-Weighted OLS with Normalization for Differences in Weight/Footprint, Passenger Cars, 

MY2008- and MY2010-Based Fleets Multiplicatively Adjusted 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-67 

Figure 2-27 Sales-Weighted OLS with Normalization for Differences in Weight/Footprint, Light Trucks, 

MY2008- and MY2010-Based Fleets Multiplicatively Adjusted 

 The above figures show, for both for passenger cars and light trucks, that applying the 

techniques selected for the NPRM to either the corrected MY 2008-based fleet projection or 

the MY 2010-based fleet projection would produce regulatory alternatives with highly 

similar, but slightly flatter slopes than those in the NPRM. At any given average stringency, 

these slightly flatter slopes would produce slightly greater incentives for manufacturers to 

respond to new standards by reducing vehicle size. In addition, the slightly flatter slopes 

would slightly increase the stringency of targets for the largest vehicles relative to stringency 

of targets for the smallest vehicles. As discussed in preamble sections III.D and II.C.4.a, 

considering the accumulated effects of light truck CAFE standards having increased steadily 

since MY2004, and GHG standards from MY 2012, the agencies are concerned that flatter 

slopes could induce manufacturers of large light trucks toward overly aggressive penetration 

rates of advanced technologies into the sector, raising significant issues of cost, lead time and 

consumer acceptance which the agencies regard as inappropriate. As discussed above, the 

agencies remain concerned that about manufacturer incentives to reduce the capability to 

carry and/or tow heavy loads using full-size light trucks. 

The agencies have thus looked at a range of analytical techniques for establishing a 

fitted line including using two market forecasts and using different approaches for the 

normalization for differences in technology content, normalization for differences in other 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-68 

vehicle attributes (e.g., power/weight or weight/footprint or, plausibly, seating capacity, 

interior volume, towing capacity, etc.), and statistical techniques (e.g., unweighted, salesweighted, MAD, OLS). Considering (a) that the reasonable analytical techniques examined 

by the agencies produce a range of fitted lines, (b) that the future composition of the light 

vehicle market is subject to some uncertainty, and (c) that other aspects of the agencies’ 

analysis are informed by policy implications, in the agencies’ judgment, there is no single 

analytical method that is the sole “correct” way to establish the two fitted lines (one for 

passenger cars, one for light trucks) the agencies use to specify final standards. The agencies’ 

updated analysis shows newly-fitted lines producing regulatory alternatives very close to the 

corresponding regulatory alternatives considered in the NPRM. This confirms that the 

standards are within the range of technically supportable possibilities. 

While the agencies’ analysis indicates that slopes spanning relatively wide ranges 

could be technically supportable, the agencies note that the final car standard is very similar to 

the slope of the MY 2016 standard, despite being based on a different analytical approach 

than the previous rule. As explained above, the agencies have selected a truck curve differing 

from that adopted for the previous rule (both slope and upper cut-point); the agencies expect 

that doing so will account for the future characteristics of the larger (work) trucks, and the 

manufacturers serving the future market for such trucks. The upper size cut-points for cars, 

and the lower size cut-point for both cars and trucks, are the same as in the previous rule. 

Without these adjustments, the agencies’ believe that there would either be incentives for 

manufacturers to reduce the utility of these trucks, or that the manufacturer’s compliance 

costs for reaching the targets would be disproportionately high (Preamble Sections III.C.5 and 

III.D). 

Thus, in the agencies’ judgment, the curves strike a reasonable and appropriate 

balance between the affected policy considerations—better reflecting the reasonable 

penetration rates of the technologies needed to achieve the standards and the lead time needed 

for implementation of those technologies, minimizing the incentive for manufacturers to 

respond to standards in ways that may either result in decreased utility or compromise safety 

(by downsizing vehicles with footprints on the sloped portion of mathematical functions 

defining fuel economy and GHG targets), and encouraging widespread penetration of 

technologies throughout both the car and light truck fleets at reasonable cost while achieving 

very significant energy and environmental benefits. Having repeated the analysis documented 

in the NPRM, and having done so based on two fleets (the corrected MY 2008-based market 

forecast, and the MY 2010-based market forecast), the agencies have demonstrated that, as 

proposed, the passenger car and light truck curves are well within technically supportable 

ranges. Slightly flatter standards would directionally have a potentially compromising effect 

on the safety-related incentives reflected by the promulgated curves, and potentially force 

more aggressive penetration of advanced technologies into work trucks in a way that raises 

issues of both increased cost and consumer acceptance. Conversely, slightly steeper 

standards would tend to increase the potential that manufacturers would respond to the 

standards by increasing vehicle size beyond levels the market would otherwise demand, in 

lieu of applying some fuel-saving technologies. For these reasons, the agencies are today 

promulgating standards using lines matching those used to develop proposed standards for the 

NPRM. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-69 

Additional discussion of the feasibility of the final standards is available in Preamble 

section III.D and IV.F. 
Chapter 2: What are the Attribute-Based Curves the Agencies are Adopting

2-70 

References:

1

49 U.S.C. 32902(a)(3)(A). 

2

69 FR 38958 (June 29, 2004). 

3

76 FR 57106, 57162-64, (Sept. 15, 2011). 

4

See 74 FR at 14359 (Mar. 30, 2009). 

5

75 FR at 25362. 

6

See generally 74 FR at 49491-96; 75 FR at 25357-62. 

7

68 FR 74920-74926. 

8

74 FR 14359. 

9

See 75 FR at 25458 

10 75 FR at 25363 

11 See 75 FR at 25359. 

12 Id. at 25362-63. 

13 Id. at 25363. 

14 75 FR at 25362 and n. 64 

15 75 FR at 25632/3. 

16 See 75 FR at 25354 

17 74 Fed. Reg. at 14370 (Mar. 30, 2009). 
 Technologies Considered in the Agencies’ Analysis

3-2 

Chapter 3: Technologies Considered in the Agencies’ Analysis 

This Chapter of the joint TSD describes the technologies NHTSA and EPA evaluated 

as potential inputs in their respective models and provides estimates of the technologies’ 

costs, effectiveness and availability. This Chapter also describes, in general terms, how the 

agencies use these inputs in their respective models. 

The agencies assume, in this analysis, that manufacturers will add a variety of 

technologies to each of their vehicle model platforms in order to improve their fuel economy 

and GHG performance. In order to evaluate CAFE and GHG standards and regulatory 

alternatives, it is essential to understand what is feasible within the timeframe of the final rule. 

Determining the technological feasibility of the MYs 2017-2025 standards requires a 

thorough study of the technologies available to the manufacturers during that timeframe. This 

chapter includes an assessment of the cost, effectiveness, and the availability, development 

time, and manufacturability of the technologies within either the normal redesign periods of a 

vehicle line or in the design of a new vehicle. As we describe below, when a technology can 

be applied can affect the costs as well as the technology penetration rates (or phase-in caps) 

that are assumed in the analysis. 

The agencies considered technologies in many categories that manufacturers could use 

to improve the fuel economy and reduce CO2 emissions of their vehicles during the MYs 

2017-2025 timeframe. Many of the technologies described in this chapter are available today, 

are well known, and could be incorporated into vehicles once product development decisions 

are made. These are “nearer-term” technologies and are identical or very similar to those 

considered in the MYs 2012-2016 final rule analysis (of course, many of these technologies 

will likely be applied to the light-duty fleet in order to achieve the 2012-2016 CAFE and 

GHG standards; such technologies would be part of the 2016 reference case for this 

analysisa). Other technologies considered may not currently be in production, but are under 

development and are expected to be in production in the next five to ten years. Examples of 

these technologies are downsized and turbocharged engines operating at combustion pressures 

even higher than today’s turbocharged engines, and an emerging hybrid architecture mated 

with an 8 speed dual clutch transmission (DCT)—a combination that is not available today. 

These are technologies which the agencies believe can, for the most part, be applied both to 

cars and trucks, and which are expected to achieve significant improvements in fuel economy 

and reductions in CO2 emissions at reasonable costs in the MYs 2017 to 2025 timeframe. The 

agencies note that we did not consider in our analysis technologies that are currently in an 

initial stage of research because of the uncertainties involved in estimating their costs and 

effectiveness and in assessing whether the technologies will be ready to implement at 

significant penetration rates during the timeframe of the MY 2017-2025 standards. Examples 

 

a

 The technologies in the 2016 reference fleet are projections made by EPA’s OMEGA model and NHTSA’s 

CAFE model respectively. Some technologies may be significantly represented in this reference fleet and these 

details can be found in each agency’s respective RIAs. 
 Technologies Considered in the Agencies’ Analysis

3-3 

of such technologies would be camless valve actuation and fuel cell vehicles.b The agencies 

acknowledge that due to the relatively long period between the date of this final rule and the 

timeframe of the MY 2017-2025 standards, the possibility exists that new and innovative 

technologies not considered in this analysis will make their way into the fleet (perhaps even in 

significant numbers). The agencies plan to assess these technologies afresh, along with all of 

the technologies considered in this final rule, as part of our mid-term evaluation. 

3.1 What Technologies did the agencies consider for the final 2017-2025 standards? 

The technologies considered for this final rulemaking (FRM) analysis by NHTSA and 

EPA are briefly described below. They fit generally into five broad categories: engine, 

transmission, vehicle, electrification/accessory, and hybrid technologies. A more detailed 

description of each technology, and the technology’s costs and effectiveness, is described in 

greater detail in section 3.4 of this TSD. 

Types of engine technologies applied in this FRM analysis, consistent with the 

proposal, analysis to improve fuel economy and reduce CO2 emissions include the following:

Low-friction lubricants – low viscosity and advanced low friction lubricants oils 

are now available with improved performance and better lubrication. 

Reduction of engine friction losses – can be achieved through low-tension piston 

rings, roller cam followers, improved material coatings, more optimal thermal 

management, piston surface treatments, and other improvements in the design of 

engine components and subsystems that improve engine operation. 

Second level of low-friction lubricants and engine friction reduction – As 

technologies advance between now and the rulemaking timeframe, there will be 

further developments enabling lower viscosity and lower friction lubricants and 

more engine friction reduction technologies available. 

Cylinder deactivation – deactivates the intake and exhaust valves and prevents fuel 

injection into some cylinders during light-load operation. The engine runs 

temporarily as though it were a smaller engine which substantially reduces 

pumping losses. 

Variable valve timing – alters the timing or phase of the intake valve, exhaust 

valve, or both, primarily to reduce pumping losses, increase specific power, and 

control residual gases.

 

b

 Fuel cell vehicles may be especially useful in lieu of full battery electric technology for the larger trucks. 

However, the agencies are not including this technology in the final rule due to the maturity level of the 

technology. 
 Technologies Considered in the Agencies’ Analysis

3-4 

Discrete variable valve lift – increases efficiency by optimizing air flow over a 

broader range of engine operation which reduces pumping losses. Accomplished 

by controlled switching between two or more cam profile lobe heights. 

Continuous variable valve lift – is an electromechanically controlled system in 

which cam period and phasing is changed as lift height is controlled. This yields a 

wide range of performance optimization and volumetric efficiency, including 

enabling the engine to be valve throttled. 

Stoichiometric gasoline direct-injection technology – injects fuel at high pressure 

directly into the combustion chamber to improve cooling of the air/fuel charge 

within the cylinder, which allows for higher compression ratios and increased 

thermodynamic efficiency. 

Turbocharging and downsizing – increases the available airflow and specific 

power level, allowing a reduced engine size while maintaining performance. This 

reduces pumping losses at lighter loads in comparison to a larger engine. In this 

FRM, the agencies considered three levels of boosting, 18 bar brake mean 

effective pressure (BMEP), 24 bar BMEP and 27 bar BMEP, as well as four levels 

of downsizing, from I4 to smaller I4 or I3, from V6 to I4 and from V8 to V6 and 

I4. 18 bar BMEP is applied with 33 percent downsizing, 24 bar BMEP is applied 

with 50 percent downsizing and 27 bar BMEP is applied with 56 percent 

downsizing. To achieve the same level of torque when downsizing the 

displacement of an engine by 50 percent, approximately double the manifold 

absolute pressure (2 bar) is required. Accordingly, with 56 percent downsizing, 

the manifold absolute pressure range increases up to 2.3 bar. Ricardo states in 

their 2011 vehicle simulation project report that advanced engines in the 2020–

2025 timeframe can be expected to have advanced boosting systems that increase 

the pressure of the intake charge up to 3 bar1

. Refer to Section 3.3.1.2.24.2 for 

examples of Ricardo-modeled displacements used for turbocharged and downsized 

engines in each vehicle class.

Exhaust-gas recirculation boost – increases the exhaust-gas recirculation used in 

the combustion process to increase thermal efficiency and reduce pumping losses. 

Levels of exhaust gas recirculation approach 25% by volume in the highly boosted 

engines modeled by Ricardo (this, in turn raises the boost requirement by 

approximately 25%). This technology is only applied to 24 bar and 27 bar BMEP 

engines in this FRM. 

Diesel engines – have several characteristics that give superior fuel efficiency, 

including reduced pumping losses due to lack of (or greatly reduced) throttling, 

and a combustion cycle that operates at a higher compression ratio, with a very 

lean air/fuel mixture, than an equivalent-performance gasoline engine. This 

technology requires additional enablers, such as NOx trap catalyst after-treatment 

or selective catalytic reduction NOx after-treatment.
 Technologies Considered in the Agencies’ Analysis

3-5 

Types of transmission technologies applied in this FRM, consistent with the proposal, 

include: 

Improved automatic transmission controls – optimizes shift schedule to maximize 

fuel efficiency under wide ranging conditions, and minimizes losses associated 

with torque converter slip through lock-up or modulation. 

Six- and seven-speed automatic transmissions – the gear ratio spacing and 

transmission ratio are optimized to enable the engine to operate in a more efficient 

operating range over a broader range of vehicle operating conditions. 

Dual clutch transmission (DCT) - are similar to a manual transmission, but the 

vehicle controls shifting and launch functions. A dual-clutch automated shift 

manual transmission uses separate clutches for even-numbered and odd-numbered 

gears, so the next expected gear is pre-selected, which allows for faster, smoother 

shifting. 

Eight-speed automatic transmissions – the transmission gear ratios are optimized 

to enable the engine to operate in a more efficient operating range over a broader 

range of vehicle operating conditions. This technology is applied after 2016. 

Shift Optimization – tries to keep the engine operating near its most efficient point 

for a given power demand. The shift controller emulates a traditional Continuously 

Variable Transmission by selecting the best gear ratio for fuel economy at a given 

required vehicle power level to take full advantage of high BMEP engines.

Manual 6-speed transmission – offers an additional gear ratio, often with a higher 

overdrive gear ratio, than a 5-speed manual transmission. 

High Efficiency Gearbox (automatic, DCT or manual) – continuous improvement 

in seals, bearings and clutches, super finishing of gearbox parts, and development 

in the area of lubrication, all aimed at reducing frictional and other parasitic load in 

the system for an automatic, DCT or manual type transmission.

Types of vehicle technologies applied in this FRM analysis, consistent with the 

proposal, analysis include: 

Low-rolling-resistance tires – have characteristics that reduce frictional losses 

associated with the energy dissipated in the deformation of the tires under load, 

thereby reducing the energy needed to move the vehicle. There are two levels of 

rolling resistance reduction considered in this FRM analysis targeting at 10 percent 

and 20 percent rolling resistance reduction respectively. 

Low-drag brakes – reduce the sliding friction of disc brake pads on rotors when 

the brakes are not engaged because the brake pads are pulled away from the rotors. 

Front or secondary axle disconnect for four-wheel drive systems – provides a 

torque distribution disconnect between front and rear axles when torque is not 

required for the non-driving axle. This results in the reduction of associated 

parasitic energy losses. 
 Technologies Considered in the Agencies’ Analysis

3-6 

Aerodynamic drag reduction – is achieved by changing vehicle shape or reducing 

frontal area, including skirts, air dams, underbody covers, and more aerodynamic 

side view mirrors. There are two levels of aerodynamic drag reduction considered 

in this FRM analysis targeting 10 percent and 20 percent aerodynamic drag 

reduction respectively. 

Mass reduction– Mass reduction encompasses a variety of techniques ranging 

from improved design and better component integration to application of lighter 

and higher-strength materials. Mass reduction can lead to collateral fuel economy 

and GHG benefits due to downsized engines and/or ancillary systems 

(transmission, steering, brakes, suspension, etc.). The maximum mass reduction 

level considered in this FRM is 20 percent.

Types of electrification/accessory and hybrid technologies applied in this FRM 

include: 

Electric power steering (EPS) and electro-hydraulic power steering (EHPS) – is 

an electrically-assisted steering system that has advantages over traditional 

hydraulic power steering because it replaces a continuously operated hydraulic 

pump, thereby reducing parasitic losses from the accessory drive.

Improved accessories (IACC) – There are two levels of IACC applied in this FRM 

analysis, consistent with the proposal. The first level may include high efficiency 

alternators, electrically driven (i.e., on-demand) water pumps and cooling systems. 

This excludes other electrical accessories such as electric oil pumps and 

electrically driven air conditioner compressors. The second level of IACC 

includes alternator regenerative braking on top of what are included in the first 

level of IACC.

Air Conditioner Systems – These technologies include improved hoses, connectors 

and seals for leakage control. They also include improved compressors, expansion 

valves, heat exchangers and the control of these components for the purposes of 

improving tailpipe CO2 emissions and fuel economy when the A/C is operating. 

These technologies are covered separately in Chapter 5 of this joint TSD. 

12-volt Stop-start – also known as idle-stop or 12V micro hybrid and commonly 

implemented as a 12-volt belt-driven integrated starter-generator, this is the most 

basic hybrid system that facilitates idle-stop capability. Along with other enablers, 

this system replaces a common alternator with an enhanced power starteralternator, both belt driven, and a revised accessory drive system.

Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG) – sometimes 

referred to as a mild hybrid, BISG provides idle-stop capability and launch 

assistance and uses a high voltage battery with increased energy capacity over 

typical automotive batteries. The higher system voltage allows the use of a 

smaller, more powerful electric motor and reduces the weight of the motor, 

inverter, and battery wiring harnesses. This system replaces a standard alternator 

with an enhanced power, higher voltage, higher efficiency belt-driven starter-
 Technologies Considered in the Agencies’ Analysis

3-7 

alternator which can recover braking energy while the vehicle slows down 

(regenerative braking). An example of a BISG system is the GM eAssist 

introduced in MY 2012. This technology was not included in the analysis for the 

proposal because we had incomplete information on the technology at that time. 

Since the proposal, the agencies have obtained better data on the costs and 

effectiveness of this technology (see 3.4.3.5 of this joint TSD). Therefore, the 

agencies have revised their technical analysis on both and found that the 

technology is now competitive with the others in the CAFE model technology 

decision trees and EPA’s technology packages. Further, this technology has been 

used for “game changing” credit for pick-up trucks and can act as a bridge 

technology for strong hybrid. For these reasons, the technology is now included in 

the analysis.

P2 Hybrid – P2 hybrid is a hybrid technology that uses a transmission integrated 

electric motor placed between the engine and a gearbox or CVT, with a wet or dry 

separation clutch which is used to decouple the motor/transmission from the 

engine. In addition, a P2 Hybrid would typically be equipped with a larger electric 

machine than a mild hybrid system but smaller than a power-split or 2-mode 

hybrid architecture. Disengaging the clutch allows all-electric operation and more 

efficient brake-energy recovery. Engaging the clutch allows efficient coupling of 

the engine and electric motor and based on simulation, when combined with a 

DCT transmission, provides similar or improved fuel efficiency to other strong 

hybrid systems with reduced cost. 

Plug-in hybrid electric vehicles (PHEV) – are hybrid electric vehicles with the 

means to charge their battery packs from an outside source of electricity (usually 

the electric grid). These vehicles have larger battery packs than non-plug-in 

hybrid electric vehicles with more energy storage and a greater capability to be 

discharged. They also use a control system that allows the battery pack to be 

substantially depleted under electric-only or blended mechanical/electric 

operation, allowing for reduced fuel use during “charge depleting” operation.

Electric vehicles (EV) – are vehicles with all-electric drive and with vehicle 

systems powered by energy-optimized batteries charged primarily from grid 

electricity. EVs with 75 mile, 100 mile and 150 mile ranges have been included as 

potential technologies. 

Types of accessory/hybridization/electrification technologies discussed but not applied 

in this FRM analysis, consistent with the proposal, include: 

Integrated Motor Assist (IMA)/Crank integrated starter generator (CISG) – 

provides idle-stop capability and uses a high voltage battery with increased energy 

capacity over typical automotive batteries. The higher system voltage allows the 

use of a smaller, more powerful electric motor and reduces the weight of the 

wiring harness. This system replaces a standard alternator with an enhanced 

power, higher voltage and higher efficiency starter-alternator that is crankshaft 

mounted and can recover braking energy while the vehicle slows down 
 Technologies Considered in the Agencies’ Analysis

3-8 

(regenerative braking). The IMA technology is not included as an enabling 

technology in this analysis as the industry trends toward more cost effective hybrid 

configurations, although it is included as a baseline technology because it exists in 

the baseline fleet. 

Power-split Hybrid (PSHEV) – is a hybrid electric drive system that replaces the 

traditional transmission with a single planetary gearset and two motor/generators. 

The smaller motor/generator uses the engine to either charge the battery or supply 

additional power to the drive motor. The second, more powerful motor/generator 

is permanently connected to the vehicle’s final drive and always turns with the 

wheels, as well as providing regenerative braking capability. The planetary 

gearset splits engine power between the first motor/generator and the output shaft 

to either charge the battery or supply power to the wheels. The power-split hybrid 

technology is not included as an enabling technology in this analysis as the 

industry is expected to trend toward more cost-effective hybrid configurations, 

although it is included as a baseline technology because it exists in the baseline 

fleet. 

2-Mode Hybrid (2MHEV) – is a hybrid electric drive system that uses an 

adaptation of a conventional stepped-ratio automatic transmission by replacing 

some of the transmission clutches with two electric motors that control the ratio of 

engine speed to vehicle speed, while clutches allow the motors to be bypassed. 

This improves both the transmission torque capacity for heavy-duty applications 

and reduces fuel consumption and CO2 emissions at highway speeds relative to 

other types of hybrid electric drive systems. The 2-mode hybrid technology is not 

included as an enabling technology in this analysis as the industry is expected to 

trend toward more cost effective hybrid configurations, although it is included as a 

baseline technology because it exists in the baseline fleet. 

3.2 How did the agencies determine the costs of each of these technologies? 

3.2.1 Direct Costsc

3.2.1.1 Costs from Tear-down Studies 

There are a number of technologies in this analysis that have been cost using the 

rigorous tear-down method described in this section. As a general matter, the agencies 

believe that the best method to derive technology cost estimates is to conduct studies 

involving tear-down and analysis of actual vehicle components. A “tear-down” involves 

breaking down a technology into its fundamental parts and manufacturing processes by 

completely disassembling actual vehicles and vehicle subsystems and precisely determining 

 

c

 Note that only battery pack and non-battery costs for HEVs, EVs and PHEVs have changed since proposal. 

All other direct costs are unchanged except for adjustments from 2009 to 2010 dollars. Battery pack and nonbattery cost changes are detailed in Section 3.4.3.6. 
 Technologies Considered in the Agencies’ Analysis

3-9 

what is required for its production. The result of the tear-down is a “bill of materials” for 

each and every part of the vehicle or vehicle subsystem. This tear-down method of costing 

technologies is often used by manufacturers to benchmark their products against competitive 

products. Historically, vehicle and vehicle component tear-down has not been done on a large 

scale by researchers and regulators due to the expense required for such studies. While teardown studies are highly accurate at costing technologies for the year in which the study is 

intended, their accuracy, like that of all cost projections, may diminish over time as costs are 

extrapolated further into the future because of uncertainties in predicting commodities (and 

raw material) prices, labor rates, and manufacturing practices. The projected costs may be 

higher or lower than predicted. 

Over the past several years, EPA has contracted with FEV, Inc. and its subcontractor 

Munro & Associates to conduct tear-down cost studies for a number of key technologies 

evaluated by the agencies in assessing the feasibility of future GHG and CAFE standards. 

The analysis methodology included procedures to scale the tear-down results to smaller and 

larger vehicles, and also to different technology configurations. FEV’s methodology was 

documented in a report published as part of the MY 2012-2016 rulemaking process, detailing 

the costing of the first tear-down conducted in this work (#1 in the below list).2

 This report 

was peer reviewed by experts in the industry and revised by FEV in response to the peer 

review comments.3

 Subsequent tear-down studies (#2-5 in the below list) were documented 

in follow-up FEV reports made available in the public docket for the MY 2012-2016 

rulemaking.4

Since then, FEV’s work under this contract has continued. Additional cost studies 

have been completed for mild hybrid technology and are available for public review.5 The 

most extensive study, performed after the MY 2012-2016 Final Rule, involved whole-vehicle 

tear-downs of a 2010 Ford Fusion power-split hybrid and a conventional 2010 Ford Fusion. 

(The latter served as a baseline vehicle for comparison.) In addition to providing power-split 

HEV costs, the results for individual components in these vehicles were subsequently used to 

cost another hybrid technology, the P2 hybrid, which employs similar hardware. This 

approach to costing P2 hybrids was undertaken because P2 HEVs were not yet in volume 

production at the time of hardware procurement for tear-down. Finally, an automotive 

lithium-polymer battery was torn down and costed to provide supplemental battery costing 

information to that associated with the NiMH battery in the Fusion, because we think 

automakers are moving to Li-ion battery technologies due to the higher energy and power 

density of these batteries. This HEV cost work, including the extension of results to P2 

HEVs, has been extensively documented in a new report prepared by FEV.6

 Because of the 

complexity and comprehensive scope of this HEV analysis, EPA commissioned a separate 

peer review focused exclusively on the new tear down costs developed for the HEV analysis. 

Reviewer comments generally supported FEV’s methodology and results, while including a 

number of suggestions for improvement, many of which were subsequently incorporated into 

FEV’s analysis and final report. The peer review comments and responses are available in the 
 Technologies Considered in the Agencies’ Analysis

3-10 

rulemaking docket.d,eOver the course of this contract between EPA and FEV, FEV performed 

teardown-based studies on the technologies listed below. These completed studies provide a 

thorough evaluation of the new technologies’ costs relative to their baseline (or replaced) 

technologies. 

1. Stoichiometric gasoline direct injection (SGDI) and turbocharging with engine 

downsizing (T-DS) on a DOHC (dual overhead cam) I4 engine, replacing a 

conventional DOHC I4 engine. 

2. SGDI and T-DS on a SOHC (single overhead cam) on a V6 engine, replacing a 

conventional 3-valve/cylinder SOHC V8 engine. 

3. SGDI and T-DS on a DOHC I4 engine, replacing a DOHC V6 engine. 

4. 6-speed automatic transmission (AT), replacing a 5-speed AT. 

5. 6-speed wet dual clutch transmission (DCT) replacing a 6-speed AT. 

6. 8-speed AT replacing a 6-speed AT. 

7. 8-speed DCT replacing a 6-speed DCT. 

8. Power-split hybrid (Ford Fusion with I4 engine) compared to a conventional 

vehicle (Ford Fusion with V6). The results from this tear-down were extended to 

address P2 hybrids. In addition, costs from individual components in this teardown study were used by the agencies in developing cost estimates for PHEVs and 

EVs. 

9. Mild hybrid with stop-start technology (Saturn Vue with I4 engine), replacing a 

conventional I4 engine. 

10. Fiat Multi-Air engine technology. (Although results from this cost study are 

included in the rulemaking docket, they were not used by the agencies in this 

rulemaking’s technical analyses because the technology is under a very recently 

awarded patent and we have chosen not to base our analyses on its widespread use 

across the industry in the 2017-2025 timeframe.) 

In addition, FEV and EPA extrapolated the engine downsizing costs for the following 

scenarios that were based on the above study cases: 

Downsizing a SOHC 2 valve/cylinder V8 engine to a DOHC V6. 

Downsizing a DOHC V8 to a DOHC V6. 

Downsizing a SOHC V6 engine to a DOHC 4 cylinder engine. 

Downsizing a DOHC 4 cylinder engine to a DOHC 3 cylinder engine. 

The agencies have relied on the findings of FEV for estimating the cost of the 

technologies covered by the tear-down studies. However, we note that FEV based their costs 

on the assumption that these technologies would be mature when produced in large volumes 

 

d

 ICF, “Peer Review of FEV Inc. Report Light Duty Technology Cost Analysis, Power-Split and P2 Hybrid 

Electric Vehicle Case Studies”, EPA-420-R-11-016, November 2011. 

e

 FEV and EPA, “FEV Inc. Report ‘Light Duty Technology Cost Analysis, Power-Split and P2 Hybrid Electric 

Vehicle Case Studies’, Peer Review Report – Response to Comments Document”, EPA-420-R-11-017, 

November 2011.
 Technologies Considered in the Agencies’ Analysis

3-11 

(450,000 units or more for each component or subsystem). If manufacturers are not able to 

employ the technology at the volumes assumed in the FEV analysis with fully learned costs, 

then the costs for each of these technologies would be expected to be higher. There is also the 

potential for stranded capitalfif technologies are introduced too rapidly for some indirect costs 

to be fully recovered. While the agencies consider the FEV tear-down analysis results to be 

generally valid for the 2017-2025 timeframe for fully mature, high sales volumes, we have 

had FEV perform supplemental analysis to consider potential stranded capital costs, and have 

included these in our primary analyses of program costs. The issue of stranded capital is 

discussed in detail in Section 3.2.2.3 of this TSD. 

3.2.1.2 Costs of HEV, PHEV, EV, and FCEVs 

The agencies have also reconsidered the costs for HEVs, PHEVs, EVs, and FCEVs 

since the MY 2012-2016 rulemaking and the Technical Assessment Report (TAR) as the 

result of two issues. The first issue is that electrified vehicle technologies are developing 

rapidly and we sought to capture the results from the most recent analyses. The second issue 

is that the analysis for the MYs 2012-2016 final rule employed a single $/kWh ($ per 

kilowatt-hour) estimate, and did not consider the specific vehicle and technology application 

for the battery when we estimated the cost of the battery.g Specifically, batteries used in 

HEVs (high power density applications) versus EVs (high energy density applications) need 

to be considered appropriately to reflect the design differences, the chemical material usage 

differences, and the differences in cost per kWh as the power to energy ratio of the battery 

changes for different applications. To address these issues for this final rule, consistent with 

the proposal, the agencies have used a battery cost model developed by Argonne National 

Laboratory (ANL) for the Vehicle Technologies Program of the U.S. Department of Energy 

(DoE) Office of Energy Efficiency and Renewable Energy.7 The model developed by ANL 

allows users to estimate unique battery pack costs using user customized input sets for 

different types of electrified powertrains, such as strong hybrid, PHEV and EV. Since the 

publication of the TAR, ANL’s battery cost model has been peer-reviewed and ANL has 

updated the model to incorporate suggestions from peer-reviewers.8

Further updates have 

been made to the model since the NPRM and this newly updated model is used in this FRM 

analysis.9 We discuss our updated battery costs in section in Section 3.4.3.9. As done in the 

proposal, the agencies developed costs and effectiveness values for the mild and P2 HEV 

configuration, two different all-electric mileage ranges for PHEVs (20 and 40 in-use miles) 

and three different mileage ranges for EVs (75, 100 and 150 in-use miles). Details regarding 

these vehicle technologies are discussed in sections 3.4.3.6.4 and 3.4.3.6.5.

 

f

 The potential for stranded capital occurs when manufacturing equipment and facilities cannot be used in the 

production of a new technology.

g

 However, we believe that this had little impact on the results of the cost analyses in support of the MYs 2012-

2016 final rule, as the agencies projected that the standards could be met with an increase of less than 2 percent 

penetration of hybrid technology and no increase in plug-in or full electric vehicle technology. 
 Technologies Considered in the Agencies’ Analysis

3-12 

3.2.1.3 Direct Manufacturing Costs Used in the Rulemaking Analysis 

Building on the MYs 2012-2016 final rule, for the NPRM analysis, the agencies took a 

fresh look at technology cost and effectiveness values. For this final rule analysis, the direct 

manufacturing costs employed in the NPRM have been largely retained, although they were 

updated to 2010$, and revisions were made to the costs of Li-ion batteries. The battery costs 

have been updated for the final rule using the latest ANL BatPaC model as discussed above. 

For costs, the agencies considered both the direct or “piece” costs and indirect costs of 

individual components of technologies. For the direct costs that were not developed through 

the FEV tear-down studies, the agencies generally followed a bill of materials (BOM) 

approach. A bill of materials, in a general sense, is a list of components that make up a 

system—in this case, an item of fuel economy-improving technology. In order to determine 

what a system costs, one of the first steps is to determine its components and what they cost. 

NHTSA and EPA estimated these components and their costs based on a number of 

sources for cost-related information. The objective was to use those sources of information 

considered to be most credible for projecting the costs of individual vehicle technologies. For 

those cost estimates that are fundamentally unchanged since the 2012-2016 final rule and/or 

the 2010 TAR (we make note of these in Section 3.4, below), we have a full description of the 

sources used in Chapter 3 of the final joint TSD supporting that rule.10,11

 For those costs that 

have been updated since those analyses (e.g., battery pack cost, costs based on more recent 

tear down analyses, etc.), we note their sources in Section 3.4, below. We have also 

considered input from manufacturers and suppliers gathered either through meetings 

following the 2010 TAR or in comment submitted in response to the 2010 TAR, some of 

which cannot be shared publicly in detailed form but, where used, we make note of it while 

protecting its confidentiality. In this final rule analysis, the agencies have not updated the 

costs based on any confidential information. Note that a summary of comments on the 2010 

TAR, with the agencies’ responses, was published as a “Supplemental Notice of Intent” in 

December of 2010.12

 As discussed throughout this chapter, the agencies have reviewed, 

revalidated or updated cost estimates for individual components based on the latest 

information available. 

Once costs were determined, they were adjusted to ensure that they were all expressed 

in 2010 dollars (the NPRM was in 2009 dollars) using the GDP price deflator as described in 

section 3.2.4.

h

 Indirect costs were accounted for using the ICM approach developed by EPA 

and explained below. NHTSA and EPA also considered how costs should be adjusted to 

reflect manufacturer learning as discussed below. Additionally, costs were adjusted by 

modifying or scaling content assumptions to account for differences across the range of 

vehicle sizes and functional requirements, and the associated material cost impacts were 

adjusted to account for the revised content, although these adjustments were different for each 

agency due to the different vehicle subclasses used in their respective models. 

 

h

 The conversion to 2010 dollars has very little impact on costs (the conversion factor to convert from 2009 to 

2010 dollars is 1.01). 
 Technologies Considered in the Agencies’ Analysis

3-13 

3.2.2 Indirect Costsi

3.2.2.1 Indirect Cost Multiplier Changes since the 2012-2016 FRM and 2010 TAR 

As discussed in greater detail below, the agencies have revised the markups used to 

estimate indirect costs. The first change was to normalize the ICM values to be consistent 

with the historical average retail price equivalent (RPE) of 1.5, rather than the single year that 

the RTI study examined. This was done by applying a factor of .5/.46 to all indirect cost 

elements. The second change was to re-consider the markup factors and the data used to 

generate them. The result on this new thinking is to increase the markup in all cases. The 

final change is the way in which the ICM factors are applied. In previous analyses ICMs 

were applied to the learned value of direct costs. However, since learning influences direct 

costs only, the agencies were concerned that this could overstate the impact of learning on 

total costs. Indirect costs are thus now established based on the initial value of direct costs 

and held constant until the long-term ICM is applied. This is done for all ICM factors except 

warranties, which are influenced by the learned value of direct costs. 

3.2.2.2 Cost markups to account for indirect costs 

To produce a unit of output, auto manufacturers incur direct and indirect costs. Direct 

costs include the cost of materials and labor costs. Indirect costs may be related to production 

(such as research and development [R&D]), corporate operations (such as salaries, pensions, 

and health care costs for corporate staff), or selling (such as transportation, dealer support, and 

marketing). Indirect costs are generally recovered by allocating a share of the costs to each 

unit of goods sold. Although it is possible to account for direct costs allocated to each unit of 

goods sold, it is more challenging to account for indirect costs allocated to a unit of goods 

sold. To make a cost analysis process more feasible, markup factors, which relate total 

indirect costs to total direct costs, have been developed. These factors are often referred to as 

retail price equivalent (RPE) multipliers. 

Cost analysts and regulatory agencies including EPA and NHTSA have frequently 

used these multipliers to estimate the resultant impact on costs associated with manufacturers’ 

responses to regulatory requirements. The best approach to determining the impact of 

changes in direct manufacturing costs on a manufacturer’s indirect costs would be to actually 

estimate the cost impact on each indirect cost element. However, doing this within the 

constraints of an agency’s time or budget is not always feasible, and the technical, financial, 

and accounting information to carry out such an analysis may simply be unavailable. 

RPE multipliers provide, at an aggregate level, the relative shares of revenues 

(Revenue = Direct Costs + Indirect Costs + Net Income) to direct manufacturing costs. Using 

RPE multipliers implicitly assumes that incremental changes in direct manufacturing costs 

produce common incremental changes in all indirect cost contributors as well as net income. 

 

i

 Note that our approach to estimating indirect costs remains unchanged since the proposal. 
 Technologies Considered in the Agencies’ Analysis

3-14 

A concern in using the RPE multiplier in cost analysis for new technologies added in response 

to regulatory requirements is that the indirect costs of vehicle modifications are not likely to 

be the same for different technologies. For example, less complex technologies could require 

fewer R&D efforts or less warranty coverage than more complex technologies. In addition, 

some simple technological adjustments may, for example, have no effect on the number of 

corporate personnel and the indirect costs attributable to those personnel. The use of RPEs, 

with their assumption that all technologies have the same proportion of indirect costs, is likely 

to overestimate the costs of less complex technologies and underestimate the costs of more 

complex technologies. 

To address this concern, EPA has developed modified multipliers. These multipliers 

are referred to as indirect cost multipliers (ICMs). In contrast to RPE multipliers, ICMs 

assign unique incremental changes to each indirect cost contributor 

ICM = (direct cost + adjusted indirect cost + profit)/(direct cost) 

Developing the ICMs from the RPE multipliers requires developing adjustment factors 

based on the complexity of the technology and the time frame under consideration. This 

methodology was used in the cost estimation for the MYs 2012-2016 final rule. The ICMs 

were developed in a peer-reviewed report from RTI International and were subsequently 

discussed in a peer-reviewed journal article.13

 Note that the cost of capital (reflected in profit) 

is included because of the assumption implicit in ICMs (and RPEs) that capital costs are 

proportional to direct costs, and businesses need to be able to earn returns on their 

investments. The capital costs are those associated with the incremental costs of the new 

technologies. 

As noted above, for the analysis supporting this final rulemaking, consistent with the 

proposal, the agencies are again using the ICM approach but have made some changes to both 

the ICM factors and to the method of applying those factors to arrive at a final cost estimate. 

The first of these changes was done in response to continued thinking among the EPANHTSA team about how past ICMs have been developed and what are the most appropriate 

data sources to rely upon in determining the appropriate ICMs. The second change has been 

done both due to staff concerns and public feedback suggesting that the agencies were 

inappropriately applying learning effects to indirect costs via the multiplicative approach to 

applying the ICMs. 

Regarding the first change – to the ICM factors themselves – a little background must 

first be provided. In the original work done under contract to EPA by RTI International,14

EPA staff with extensive experience in the auto industry had undertaken a consensus 

approach to determining the impact of specific technology changes on the indirect costs of a 

company. Subsequent to that effort, EPA staff, again with extensive experience in the auto 

industry, conducted a blind survey to make this determination on a different set of technology 

changes. This subsequent effort, referred to by EPA as a modified-Delphi approach, resulted 

in slightly different ICM determinations. This effort is detailed in a memorandum contained 

in the docket for this rule.15

 Upon completing this effort, the EPA team determined that the 

original RTI values should be averaged with the modified-Delphi values to arrive at the final 
 Technologies Considered in the Agencies’ Analysis

3-15 

ICMs for low and medium complexity technologies and that the original RTI values would be 

used for high complexity level 1 while the modified-Delphi values would be used for high 

complexity level 2. These final ICMs as described were used in the MYs 2012-2016 lightduty GHG/CAFE rulemaking. 

More recently, EPA and NHTSA decided that the original light-duty RTI values, 

because of the technologies considered for low and medium complexity, should no longer be 

used and that we should rely solely on the modified-Delphi values for these complexity levels. 

The original light-duty RTI study used low rolling resistance tires as a low complexity 

technology example and a dual clutch transmission as a medium complexity technology. 

Upon further thought, the technologies considered for the modified Delphi values (passive 

aerodynamic improvements for low complexity and turbocharging with downsizing for 

medium complexity) were considered to better represent the example technologies. As a 

result, the modified-Delphi values became the working ICMs for low and medium complexity 

rather than averaging those values with the original RTI report values. NHTSA and EPA staff 

also re-examined the technology complexity categories that were assigned to each light-duty 

technology and modified these assignments to better reflect the technologies that are now 

used as proxies to determine each category’s ICM value. 

A secondary-level change was also made as part of this ICM recalculation to the lightduty ICMs. That change was to revise upward the RPE level reported in the original RTI 

report from an original value of 1.46 to 1.5 to reflect the long term average RPE. The original 

RTI study was based on 2007 data. However, an analysis of historical RPE data indicates 

that, although there is year to year variation, the average RPE has remained roughly 1.5. 

ICMs will be applied to future year’s data and therefore NHTSA and EPA staff believe that it 

would be appropriate to base ICMs on the historical average rather than a single year’s result. 

Therefore, ICMs in this final rulemaking, consistent with the proposal, were adjusted to 

reflect this average level. As a result, the High 1 and High 2 ICMs have also changed. 

Table 3-1 shows both the ICM values used in the MYs 2012-2016 final rule and the 

new ICM values used for the analysis supporting these final rules. Near term values account 

for differences in the levels of R&D, tooling, and other indirect costs that will be incurred. 

Once the program has been fully implemented, some of the indirect costs will no longer be 

attributable to the standards and, as such, a lower ICM factor is applied to direct costs. 

Table 3-1 Indirect Cost Multipliers Used in this Analysisa

2012-2016 Rule This Final rule

Complexity Near term Long term Near term Long term

Low 1.17 1.13 1.24 1.19

Medium 1.31 1.19 1.39 1.29

High1 1.51 1.32 1.56 1.35

High2 1.70 1.45 1.77 1.50

a Rogozhin, A., et. al., “Using indirect cost multipliers to estimate the total cost of 

adding new technology in the automobile industry,” International Journal of Production 

Economics (2009); “Documentation of the Development of Indirect Cost Multipliers 

for Three Automotive Technologies,” Helfand, G., and Sherwood, T., Memorandum 

dated August 2009; “Heavy Duty Truck Retail Price Equivalent and Indirect Cost 
 Technologies Considered in the Agencies’ Analysis

3-16 

Multipliers,” Draft Report prepared by RTI International and Transportation Research 

Institute, University of Michigan, July 2010

The second change made to the ICMs has to do with the way in which they are 

applied. To date, we have applied the ICMs, as done in any analysis that relied on RPEs, as a 

pure multiplicative factor. This way, a direct manufacturing cost of, say, $100 would be 

multiplied by an ICM of 1.24 to arrive at a marked up technology cost of $124. However, as 

learning effects (discussed below) are applied to the direct manufacturing cost, the indirect 

costs are also reduced accordingly. Therefore, in year two the $100 direct manufacturing cost 

might reduce to $97, and the marked up cost would become $120 ($97 x 1.24). As a result, 

indirect costs would be reduced from $24 to $20. Given that indirect costs cover many things 

such as facility-related costs, electricity, etc., it is perhaps not appropriate to apply the ICM to 

the learned direct costs, at least not for those indirect cost elements unlikely to change with 

learning. The EPA-NHTSA team believes that it is appropriate to allow only warranty costs 

to decrease with learning, since warranty costs are tied to direct manufacturing costs (since 

warranty typically involves replacement of actual parts which should be less costly with 

learning). The remaining elements of the indirect costs should remain constant year-overyear, at least until some of those indirect costs are no longer attributable to the rulemaking 

effort that imposed them (such as R&D). 

As a result, the ICM calculation has become more complex with the analysis 

supporting this final rule, consistent with the proposal. We must first establish the year in 

which the direct manufacturing costs are considered “valid.” For example, a cost estimate 

might be considered valid today, or perhaps not until high volume production is reached—

which will not occur until MY 2015 or later. That year is known as the base year for the 

estimated cost. That cost is the cost used to determine the “non-warranty” portion of the 

indirect costs. For example, the non-warranty portion of the medium complexity ICM in the 

short-term is 0.343 (the warranty versus non-warranty portions of the ICMs are shown in 

Table 3-2).

 For the dual cam phasing (DCP) technology on an I4 engine we have estimated a 

direct manufacturing cost of $70 in MY 2015. So the non-warranty portion of the indirect 

costs would be $24.01 ($70 x 0.343). This value would be added to the learned direct 

manufacturing cost for each year through 2018, the last year of short term indirect costs. 

Beginning in 2019, when long-term indirect costs begin, the additive factor would become 

$18.13 ($70 x 0.259). Additionally, the $70 cost in 2015 would become $67.90 in MY 2016 

due to learning ($70 x (1-3%)). So, while the warranty portion of the indirect costs would be 

$3.15 ($70 x 0.045) in 2015, indirect costs would decrease to $3.06 ($67.90 x 0.045) in 2016 

as warranty costs decrease with learning. The resultant indirect costs for the DCP-I4 

technology would be $27.16 ($24.01+$3.15) in MY 2015 and $27.07 ($24.01+$3.06) in 

MY2016, and so on for subsequent years. 

Table 3-2 Warranty and Non-Warranty Portions of ICMs 

Near term Long term

Complexity Warranty Non-warranty Warranty Non-warranty

Low 0.012 0.230 0.005 0.187
 Technologies Considered in the Agencies’ Analysis

3-17 

Medium 0.045 0.343 0.031 0.259

High1 0.065 0.499 0.032 0.314

High2 0.074 0.696 0.049 0.448

There is some level of uncertainty surrounding both the ICM and RPE markup factors. 

The ICM estimates used in this final rule, consistent with the proposal, group all technologies 

into three broad categories and treat them as if individual technologies within each of the 

three categories (low, medium, and high complexity) will have exactly the same ratio of 

indirect costs to direct costs. This simplification means it is likely that the direct cost for 

some technologies within a category will be higher and some lower than the estimate for the 

category in general. Additionally, the ICM estimates were developed using adjustment factors 

developed in two separate occasions: the first, a consensus process, was reported in the RTI 

report; the second, a modified Delphi method, was conducted separately and reported in an 

EPA memorandum. Both these panels were composed of EPA staff members with previous 

background in the automobile industry; the memberships of the two panels overlapped but 

were not the same. The panels evaluated each element of the industry’s RPE estimates and 

estimated the degree to which those elements would be expected to change in proportion to 

changes in direct manufacturing costs. The method and the estimates in the RTI report were 

peer reviewed by three industry experts and subsequently by reviewers for the International 

Journal of Production Economics.16

 However, the ICM estimates have not yet been validated 

through a direct accounting of actual indirect costs for individual technologies. RPEs 

themselves are also inherently difficult to estimate because the accounting statements of 

manufacturers do not neatly categorize all cost elements as either direct or indirect costs. 

Hence, each researcher developing an RPE estimate must apply a certain amount of judgment 

to the allocation of the costs. Since empirical estimates of ICMs are ultimately derived from 

the same data used to measure RPEs, this affects both measures. However, the value of RPE 

has not been measured for specific technologies, or for groups of specific technologies. Thus 

applying a single average RPE to any given technology by definition overstates costs for very 

simple technologies, or understates them for advanced technologies. 

The International Council on Clean Transportation (ICCT) and the National 

Automobile Dealers Association (NADA) commented on our use of ICMs. ICCT supported 

the ICM approach as presented in the proposal, but argued for removal of sensitivity analyses 

examining RPEs in NHTSA’s FRIA. NADA argued that the ICM approach is not valid and 

should be replaced with an RPE approach. Further, it argued that the RPE factor should be 2x 

rather than the 1.5x approach that is supported by filings to the Securities and Exchange 

Commission. We have conducted a thorough analysis of the NADA comments on the RPE 

vs. ICM approach. We disagree with NADA’s arguments for both using the RPE approach 

and a 2x RPE factor, for the following reasons. 

NADA’s objections to the ICM approach include: 

1. There is no evidence that the RPE method is flawed. 

2. The ICMs do not include the total costs of complying with the standards, 

because it does not include all the costs included in the RPE. 
 Technologies Considered in the Agencies’ Analysis

3-18 

3. The ICMs use a subjective judgment to adjust indirect costs for different 

technologies, while the RPE uses one value for all components and does not 

rely on “nearly perfect foreknowledge.” 

4. The ICMs do not incorporate dealer and OEM profits. 

NADA’s arguments for the RPE of 2x include: 

5. Several scholarly papers support the use of RPEs in the 2.0 range. 

6. A case study comparison of the added content of a 1971 Chevrolet Vega and 

2011 Cruze shows that an RPE of 2.0 accounts for the change in retail price. 

The discussion above provides background on the issue of RPEs and ICMs, and on the 

agencies’ decision to use ICMs to estimate indirect costs for this rulemaking. Our responses 

here address the specific points raised by NADA. 

First, the RPE approach applies the same average indirect cost markup across all 

technologies in the redesigned vehicle fleet, regardless of the source of the direct cost (i.e. 

whether a technology is simple or complex; whether the source of the additional cost is a new 

or a mature technology). The RPE methodology also assumes that an indirect cost is 

associated with the rule, even if no relation is apparent. For instance, the RPEs (until recent 

union contract changes) would have included the costs to the domestic auto companies of the 

health insurance for retired auto workers. Because the rulemaking would not affect the 

current retiree health care costs, (which account for about 1.5% of the RPE), they are 

irrelevant to the rulemaking. The ICM approach differs in that it allows indirect costs to vary 

with the complexity of the technology and the time frame.17 It is a reasonable assumption that 

simple technologies are expected to have fewer indirect costs per dollar than complex 

technologies. For instance, the use of low-rolling-resistance tires, considered by the 

EPA/NHTSA team to be a low-complexity technology, adds costs, but, because they require 

significantly less vehicle integration effort than for example, adding a hybrid powertrain 

would, the additional indirect costs per dollar of direct manufacturing costs may be very low. 

In contrast, converting a conventional vehicle to a hybrid-electric is a far more complex 

activity, involving increases in indirect costs such as research and development 

disproportionate to its direct costs. Shortly after product introduction, indirect costs for 

components such as warranty and research may be relatively high, but auto makers are 

expected to be able to reduce the costs of any specific technology over time, as they gain 

experience with them and, thus, redirect those expenditures to other areas of their choosing. 

Second, the ICM approach excludes some costs included in the RPE when those costs 

are expected not to be affected by the standards. The ICM approach, as discussed above, 

begins with the RPE and includes all the relevant cost categories. ICMs reflect the indirect 

costs judged by the EPA panel (see above for further explanation) to be incurred for each 

technology in response to regulatory imposed changes. Any “omissions”, or instances where 

the ICM carries no costs for a given technology, are cases where the indirect costs are 

considered by the EPA panel not to be impacted by regulatory imposed changes for that 

technology. For instance, the costs of switching from a standard tire to a low-rollingresistance tire (the example of a low-complexity technology in Rogozhin et al. (2009)) are not 

expected to lead to an increase in transportation costs (i.e., costs for transporting finished 
 Technologies Considered in the Agencies’ Analysis

3-19 

vehicles from production site to retail site) because it is not expected to be any more 

expensive to ship a new vehicle with the new tires than with the old tires.18

Third, the RPE approach relies on the assumption that applying the average RPE for 

the vehicle fleet as a whole will produce a reasonable average indirect cost for all 

technologies in the redesigned vehicle fleet resulting from these standards. The agencies 

believe that using the professional judgment and expertise of EPA staff with extensive 

experience in the auto industry provides useful insight into how a given regulation will impact 

indirect costs and is an improvement over ignoring differences among technologies. The 

agencies have therefore based their central analyses on the ICM method. 

Fourth, it is incorrect that the ICMs do not include profit. Although the initial ICM 

report reviewed by NRC did not include OEM profit, the ICM approach applied in this 

rulemaking does incorporate an allowance for profit, at the average corporate profit rate of 6% 

of sales. The inclusion of profit for the Joint NPRM is discussed in the draft Technical 

Support Document, and the agencies have included profit as an element of the indirect costs 

for the final rulemaking as well.19

Fifth, the papers cited to support the use of an RPE of 2x are only a subset of the literature. 

The National Research Council (NRC)20 discusses the four studies that NADA’s Exhibit A 

cites in its support of an RPE of 2.0. The NRC also notes that NHTSA used an RPE of 1.5 for 

its MY 2011 fuel economy rule; the NRC in 2002 used an RPE of 1.4, as did the California 

Air Resources Board; and EPA has used a markup factor of 1.3. The NRC report then 

discusses work done for the committee itself, doing a detailed analysis of a Honda Accord and 

a Ford F-150 truck; the former had an RPE of “1.39 to market transaction price and 1.49 to 

MSRP,” and the latter had an RPE of “1.52 for market price and 1.54 for MSRP.” Most 

significantly, the NRC does not recommend an RPE of 2.0. Rather, the NRC recommends, 

for technologies where the primary manufacturer of the technology is the automotive supply 

base, an RPE of 1.5, except for hybrid powertrain components from the automotive supply 

base, where it recommends an RPE of 1.3 due to the inclusion of several indirect costs in their 

base estimate.21

 Only in the case of technologies where an automotive OEM is the primary 

manufacturer does the NRC recommend an RPE of 2.0.j

 We note, without specifically 

commenting on the quality of the studies, that none of the papers NADA cites in support of an 

RPE of 2x was published in a peer-reviewed journal, and none of the studies claim to have 

been peer-reviewed. In contrast, the research in Rogozhin et al. (2009) was peer-reviewed 

twice: as documented in the Peer Review Report, and when it was submitted (and accepted) 

for publication in the International Journal of Production Economics. A full reading of the 

literature on RPEs thus shows little support for a value of 2x. Further support for an average 

 

j

 Importantly, application of the 2.0s RPE in the “OEM as primary manufacturer” case would be done to a 

smaller direct cost since the OEM has produced the part in-house and, thus, is not paying the full supplier-level 

indirect costs that would be included in a part purchased from a supplier. The end result should be a total cost 

roughly equivalent or less than a 1.5x RPE applied to the supplier-produced part. If not, the manufacturer should 

probably not produce in-house and should, instead, purchase parts since they would be less costly (all other 

considerations being equal). 
 Technologies Considered in the Agencies’ Analysis

3-20 

RPE lower than 2.0 comes from an examination of industry financial statements. NHTSA 

examined industry 10-K submissions to the Securities and Exchange Commission from the 

period 1972-1997.k The cost information in these submissions represents all industry 

operations, including both OEM and supplier-sourced technologies. During this period, the 

RPE averaged 1.5 while varying slightly, but never dropped below 1.4 or exceeded 1.6. At no 

time did the average RPE approach the 2.0 value advocated by NADA. The results are 

shown, together with the 2007 results from Rogozhin et al in the following figure: 

Sixth, the comparison of the Vega and the Cruze uses circular logic; it assumes its 

conclusion. The direct costs of the vehicles are calculated using an RPE of 2, and the NADA 

analysis then calculates a quality difference based on the change in direct costs. The 

magnitude of the quality difference is then discovered to correspond to an RPE of 2, although 

it is also an inevitable result of the initial assumption of an RPE of 2. The analysis provided 

can be replicated with any value of RPE. This argument thus provides no evidence on the 

value of the RPE. 

For these reasons, we do not accept NADA’s request to use an RPE of 2x., and instead 

continue with our use of ICMs as the basis for our central analysis. However, the agencies 

recognize that there is uncertainty regarding the impact on indirect costs of regulatorily 

imposed changes. For this reason, both agencies have conducted sensitivity analyses using 

different indirect cost estimates. EPA presents its sensitivities in Chapter 3 of its final RIA. 

For its part, NHTSA rejects the ICCT proposal to eliminate sensitivity analyses examining the 

RPE and presents the impact of using the RPE as a basis for indirect costs in its analysis in 

Chapters 7 and 10 of NHTSA’s FRIA. In addition, RPEs are incorporated into the 

Probabilistic Uncertainty analysis in Chapter 12 of NHTSA’s FRIA. 

 

k

 Spinney, B.C., Faigin, B.M, Bowie, N.N, Kratzke, S.R., Advanced Air Bag Systems Cost, Weight, and Lead 

Time Analysis Summary Report, Contract No. DTNH22-96-0-12003, Task Orders – 001, 003, and 005. 
 Technologies Considered in the Agencies’ Analysis

3-21 

3.2.2.3 Stranded capital 

Because the production of automotive components is capital-intensive, it is possible 

for substantial capital investments in manufacturing equipment and facilities to become 

“stranded” (where their value is lost, or diminished). This would occur when the capital is 

rendered useless (or less useful) by some factor that forces a major change in vehicle design, 

plant operations, or manufacturer’s product mix, such as a shift in consumer demand for 

certain vehicle types. It can also be caused by new standards that phase-in at a rate too rapid 

to accommodate planned replacement or redisposition of existing capital to other activities. 

The lost value of capital equipment is then amortized in some way over production of the new 

technology components. 

It is difficult to quantify accurately any capital stranding associated with new 

technology phase-ins under the final standards because of the iterative dynamic involved – 

that is, the new technology phase-in rate strongly affects the potential for additional cost due 

to stranded capital, but that additional cost in turn affects the degree and rate of phase-in for 

the same or other individual competing technologies. In addition, such an analysis is very 

company-, factory-, and manufacturing process-specific, particularly in regard to finding 

alternative uses for equipment and facilities. Nevertheless, in order to account for the 

possibility of stranded capital costs, the agencies asked FEV to perform an analysis, using 

conservative assumptions, of the potential stranded capital costs associated with rapid phasein of technologies due to new standards, using data from FEV’s primary teardown-based cost 

analyses.22 Since the direct manufacturing costs developed by FEV assumed a 10 year 

production life (i.e., capital costs amortized over 10 years) the agencies applied the FEV 

derived stranded capital costs whenever technologies were replaced prior to being utilized for 

the full 10 years. The other option would have been to assume a 5 year product life (i.e., 

capital costs amortized over 5 years), which would have increased the direct manufacturing 

costs. It seems only reasonable to account for stranded capital costs in the instances where the 

fleet modeling performed by the agencies replaced technologies before the capital costs were 

fully amortized. The agencies did not derive or apply stranded capital costs to all 

technologies only the ones analyzed by FEV. While there is uncertainty about the possible 

stranded capital costs (i.e., understated or overstated), their impact would not call into 

question the overall results of our cost analysis or otherwise affect the stringency of the 

standards, since costs of stranded capital are a relatively minor component of the total 

estimated costs of the rules. 

The assumptions made in FEV’s stranded capital analysis with potential for major 

impacts on results are: 

• All manufacturing equipment was bought brand new when the old technology 

started production (no carryover of equipment used to make the previous 

components that the old technology itself replaced). 

• 10-year normal production runs: Manufacturing equipment used to make old 

technology components is straight-line depreciated over a 10-year life. 

• Factory managers do not optimize capital equipment phase-outs (that is, they are 

assumed to routinely repair and replace equipment without regard to whether or 

not it will soon be scrapped due to adoption of new vehicle technology). 
 Technologies Considered in the Agencies’ Analysis

3-22 

• Estimated stranded capital is amortized over 5 years of annual production at 

450,000 units (of the new technology components). This annual production is 

identical to that assumed in FEV’s primary teardown-based cost analyses. The 5-

year recovery period is chosen to help ensure a conservative analysis; the actual 

recovery would of course vary greatly with market conditions. 

FEV assembled a team of manufacturing experts to perform the analysis, using a 

methodology with the following key steps for each vehicle technology scenario: 

1) Identify all of the old technology components that are no longer used or that are 

modified in the new technology vehicles (from the comparison bills of materials 

developed in the primary teardown-based analyses). 

2) For each of these components identify the manufacturing equipment and tooling 

needed to make it. 

3) Estimate the new-purchase $ value of each item identified in step 2. 

4) Assign an “Investment Category” to each equipment item identified in step 2, 

based on an assessment by FEV’s experts of recoverable value: 

• Flexible: Equipment can be used to manufacture new technology or other parts 

(0% stranded) 

• Re-Useable: Equipment can be used in alternative industries, sold at 50% of its 

remaining value (50% stranded) 

• Semi-Dedicated: Estimate that 50% of equipment is flexible (50% stranded) 

• Dedicated: Custom manufacturing equipment (100% stranded) 

5) Assign an “Investment Category” to each tooling item identified in step 2, based 

on an assessment by FEV’s experts of recoverable value: 

• Flexible: Can be used for manufacturing new technology parts (0% stranded) 

• Perishable: Frequent replacement of tooling (0% stranded) 

• Semi-Dedicated Tooling: Estimate that 50% of tooling is dedicated (50% 

stranded) 

• Dedicated: Commodity-specific (100% stranded) 

6) Multiply the % stranding values from steps 4 and 5 by the $ values from step 3. 

7) Multiply the results in step 6 by 70%, 50%, and 20% for 3-, 5-, and 8-year 

stranding scenarios, respectively. That is, an old technology, for which production 

is truncated prematurely after only 8 years, will experience the stranding of 20% 

(the last 2 years of its 10-year normal production run) of its associated remaining 

capital value. 

8) Sum the results in step 7 to obtain overall stranded capital costs. 
 Technologies Considered in the Agencies’ Analysis

3-23 

9) Divide the results in step 8 by 2,250,000 (5 years x 450,000 units/year) to obtain 

$/vehicle values, applicable to new technology vehicles for the 1st 5 years of their 

production due to the assumed 5-year recovery period. 

The stranded capital analysis was performed for three transmission technology 

scenarios, two engine technology scenarios, and one hybrid technology scenario, as shown in 

Table 3-3. The methodology used by EPA in applying these results to the technology costs is 

described in Chapter 3 of EPA’s RIA. The methodology used by NHTSA in applying these 

results to the technology costs is described in NHTSA’s RIA section V. 

Table 3-3 Stranded Capital Analysis Results (2010 dollars /vehicle) 

Replaced

technology

New

technology

Stranded capital cost per vehicle 

when replaced technology’s production is ended 

after:

3 years 5 years 8 years

6-speed AT 6-speed DCT $56 $39 $16

6-speed AT 8-speed AT $48 $34 $14

6-speed DCT 8-speed DCT $28 $20 $8

Conventional V6 DSTGDI I4 $57 $40 $16

Conventional V8 DSTGDI V6 $61 $43 $17

Conventional V6 Power-split HEV $112 $80 $32

DSTGDI=Downsized, turbocharged engine with stoichiometric gasoline direct injection.

3.2.3 Cost reduction through manufacturer learningl

For this final rule, consistent with the proposal, we have not changed our estimates of 

learning and how learning will impact costs going forward from what was employed in the 

analysis for the MYs 2012-2016 light-duty vehicle rule. However, we have updated our 

terminology in an effort to clarify that we consider there to be one learning effect—learning 

by doing—which results in cost reductions occurring with every doubling of production.m

 In 

the past, we have referred to volume-based and time-based learning. Our terms were meant 

only to denote where on the volume learning curve a certain technology was—“volume-based 

learning” meant the steep portion of the curve where learning effects are greatest, while 

“time-based learning” meant the flatter portion of the curve where learning effects are less 

pronounced. Unfortunately, our terminology led some to believe that we were implementing 

two completely different types of learning—one based on volume of production and the other 

based on time in production. Our new terminology—steep portion of the curve and flat 

portion of curve—is simply meant to make more clear that there is one learning curve and 

some technologies can be considered to be on the steep portion while others are well into the 

 

l

 Note that our approach to accounting for cost reduction through manufacturer learning is unchanged since the 

proposal. 

m

 Note that this new terminology was described in the recent heavy-duty GHG final rule (see 76 FR 57320). The 

learning approach used in this analysis is entirely consistent with that used and described for the heavy-duty 

analysis. 
 Technologies Considered in the Agencies’ Analysis

3-24 

flatter portion of the curve. These two portions of the volume learning curve are shown in 

Figure 3-1. 

Figure 3-1 Steep & Flat Portions of the Volume Learning Curve 

For some of the technologies considered in this analysis, manufacturer learning effects 

would be expected to play a role in the actual end costs. The “learning curve” or “experience 

curve” describes the reduction in unit production costs as a function of accumulated 

production volume. In theory, the cost behavior it describes applies to cumulative production 

volume measured at the level of an individual manufacturer, although it is often assumed—as 

both agencies have done in past regulatory analyses—to apply at the industry-wide level, 

particularly in industries like the light duty vehicle production industry that utilize many 

common technologies and component supply sources. Both agencies believe there are indeed 

many factors that cause costs to decrease over time. Research in the costs of manufacturing 

has consistently shown that, as manufacturers gain experience in production, they are able to 

apply innovations to simplify machining and assembly operations, use lower cost materials, 

and reduce the number or complexity of component parts. All of these factors allow 

manufacturers to lower the per-unit cost of production. We refer to this phenomenon as the 

manufacturing learning curve. 

NHTSA and EPA included a detailed description of the learning effect in the MYs 

2012-2016 light-duty rule and the more recent heavy-duty rule.23 Most studies of the effect of 

experience or learning on production costs appear to assume that cost reductions begin only 

after some initial volume threshold has been reached, but not all of these studies specify this 

0%

20%

40%

60%

80%

100%

120% Cost Relative to Orliginal

Cumulative Production

Volume Learning Curve - Steep & Flat Portions

Steep portion of volume learning curve

Flat portion of volume learning curve

Effectively no more learning
 Technologies Considered in the Agencies’ Analysis

3-25 

threshold volume. The rate at which costs decline beyond the initial threshold is usually 

expressed as the percent reduction in average unit cost that results from each successive 

doubling of cumulative production volume, sometimes referred to as the learning rate. Many 

estimates of experience curves do not specify a cumulative production volume beyond which 

cost reductions would no longer occur, instead depending on the asymptotic behavior of the 

effect for learning rates below 100 percent to establish a floor on costs. 

In past rulemaking analyses, as noted above, both agencies have used a learning curve 

algorithm that applied a learning factor of 20 percent for each doubling of production volume. 

NHTSA has used this approach in analyses supporting recent CAFE rules. In its analyses, 

EPA has simplified the approach by using an “every two years” based learning progression 

rather than a pure production volume progression (i.e., after two years of production it was 

assumed that production volumes would have doubled and, therefore, costs would be reduced 

by 20 percent).n

 

In the MYs 2012-2016 light-duty rule and the heavy-duty GHG final rule, the agencies 

employed an additional learning algorithm to reflect the volume-based learning cost 

reductions that occur further along on the learning curve. This additional learning algorithm 

was termed “time-based” learning in the MYs 2012-2016 rule simply as a means of 

distinguishing this algorithm from the volume-based algorithm mentioned above, although 

both of the algorithms reflect the volume-based learning curve supported in the literature. As 

described above, we are now referring to this learning algorithm as the “flat portion” of the 

learning curve. This way, we maintain the clarity that all learning is, in fact, volume-based 

learning, and that the level of cost reductions depend only on where on the learning curve a 

technology’s learning progression is. We distinguish the flat portion of the curve from the 

steep portion of the curve to indicate the level of learning taking place in the years following 

implementation of the technology (see Figure 3-1). The agencies have applied learning 

effects on the steep portion of the learning curve for those technologies considered to be 

newer technologies likely to experience rapid cost reductions through manufacturer learning, 

and learning effects on the flat portion learning curve for those technologies considered to be 

more mature technologies likely to experience only minor cost reductions through 

manufacturer learning. As noted above, the steep portion learning algorithm results in 20 

 

n

 To clarify, EPA has simplified the steep portion of the volume learning curve by assuming that production 

volumes of a given technology will have doubled within two-years time. This has been done largely to allow for 

a presentation of estimated costs during the years of implementation, without the need to conduct a feedback 

loop that ensures that production volumes have indeed doubled. If we were to attempt such a feedback loop, we 

would need to estimate first year costs, feed those into OMEGA, review the resultant technology penetration rate 

and volume increase, calculate the learned costs, feed those into OMEGA (since lower costs would result in 

higher penetration rates, review the resultant technology penetration rate and volume increase, etc., until an 

equilibrium was reached. To do this for all of the technologies considered in our analysis is simply not feasible. 

Instead, we have estimated the effects of learning on costs, fed those costs into OMEGA, and reviewed the 

resultant penetration rates. The assumption that volumes have doubled after two years is based solely on the 

assumption that year two sales are of equal or greater number than year one sales and, therefore, have resulted in 

a doubling of production. This could be done on a daily basis, a monthly basis, or, as we have done, a yearly 

basis. 
 Technologies Considered in the Agencies’ Analysis

3-26 

percent lower costs after two full years of implementation (i.e., the MY 2016 costs would be 

20 percent lower than the MYs 2014 and 2015 costs). Once two steep portion learning steps 

have occurred, flat portion learning at 3 percent per year becomes effective for 5 years. 

Beyond 5 years of learning at 3 percent per year, 5 years of learning at 2 percent per year, 

then 5 at 1 percent per year become effective. 

Learning effects are applied to most but not all technologies because some of the 

expected technologies are already used rather widely in the industry and we therefore assume 

that learning impacts have already occurred. The steep portion learning algorithm was 

applied for only a handful of technologies that are considered to be new or emerging 

technologies. Most technologies have been considered to be more established given their 

current use in the fleet and, hence, the lower flat portion learning algorithm has been applied. 

The learning algorithms applied to each technology and the applicable timeframes are 

summarized in Table 3-4.

Table 3-4 Learning Effect Algorithms Applied to Technologies Used in this Analysis 

Technology Steep learning Flat learning No learning

Engine modifications to accommodate low

friction lubes 2012-2025

Engine friction reduction – level 1 & 2 2012-2025

Lower rolling resistance tires – level 1 2012-2025

Low drag brakes 2012-2025

Secondary axle disconnect 2012-2025

Electric/Plug-in vehicle battery charger 

installation labor 2012-2025

Variable valve timing 2012-2025

Variable valve lift 2012-2025

Cylinder deactivation 2012-2025

Stoichiometric gasoline direct injection 2012-2025

Aggressive shift logic – level 1 & 2 2012-2025

Early torque converter lockup 2012-2025

5/6/7/8 speed auto transmission 2012-2025

6/8 speed dual clutch transmission 2012-2025

High efficiency gearbox 2012-2025

Improved accessories – level 1 & 2 2012-2025

Electronic/electro-hydraulic power steering 2012-2025

Aero improvements – level 1 & 2 2012-2025

Conversion to DOHC without reducing # of 

cylinders 2012-2025

Air conditioner related hardware 2012-20205

Air conditioner alternative refrigerant 2016-2020 2021-2025

Cooled EGR 2012-2025

Conversion to Atkinson cycle 2012-2025

Turbocharging & downsizing 2012-2025

Mass reduction 2012-2025
 Technologies Considered in the Agencies’ Analysis

3-27 

Advanced diesel 2012-2025

Hybrid/Electric/Plug-in vehicle non-battery 

components 2012-2025

P2 Hybrid vehicle battery-pack components 2012-2016 2017-2025

Electric/Plug-in vehicle battery-pack 

components 2012-2025a

Electric/Plug-in vehicle battery charger 

components 2012-2025a

Stop-start 2012-2015 2016-2025

Lower rolling resistance tires – level 2 2017-2021 2022-2025

a

Note that the steep learning effects have for EV and PHEV battery packs and charger components have been 

carried through 5 learning cycles but at a decelerated pace as described in the text.

The learning effects discussed here impact the technology costs in that those 

technology costs for which learning effects are considered applicable are changing throughout 

the period of implementation and the period following implementation. For example, some of 

the technology costs considered in this analysis are taken from the MYs 2012-2016 light-duty 

rule. Many of the costs in the MYs 2012-2016 light-duty rule were considered “applicable” 

for the 2012 model year. If flat-portion learning were applied to those technologies, the 2013 

cost would be 3 percent lower than the 2012 cost, and the 2014 model year cost 3 percent 

lower than the 2013 cost, etc. As a result, the MYs 2017-2025 costs for a given technology 

used in this analysis reflect those years of flat learning and would not be identical to the 2012 

model year cost for that same technology presented in the MYs 2012-2016 light-duty rule. 

Because of the nature of battery pack development (i.e., we are arguably still in the 

research phase for the types of batteries considered in this final rule, and cost reduction 

through manufacturer-based learning has only just begun), the agencies have carried the 

learning curve through five steep learning steps although at a somewhat slower pace than 

every two years. This has been done in an effort to maintain the shape of a traditional 

learning curve. This curve was developed by using the ANL BatPaC model costs as direct 

manufacturing costs applicable in the 2025 MY. We have then unlearned those costs back to 

2012 using the curve shown in Figure 3-2. This is the same curve used in the 2010 TAR (see 

2010 TAR at page B-22). This allows the agencies to estimate costs in MYs 2017 through 

2025, as well as those costs in each year back to MY 2012, if desired. As noted, this learning 

curve consists of 5 full learning steps on the steep portion of the learning curve, each of which 

results in costs being reduced 20 percent relative to the prior step. These learning steps are 

shown occurring every two years beginning in 2012 until 2020, at which time a 5 year gap is 

imposed until 2025 when the fifth steep learning step occurs. Beyond 2025, learning on the 

flat portion of the curve begins at 3 percent per year cost reductions. The smooth line shows a 

logarithmic curve fit applied to the learning curve as the agencies’ cost model would apply 

learning. 
 Technologies Considered in the Agencies’ Analysis

3-28 

Figure 3-2 Learning Curve used for EV & PHEV Battery-Packs and In-Home Charger Costs 

Note that the effects of learning on individual technology costs can be seen in the cost 

tables presented in section 3.3, below. For each technology, we show direct manufacturing 

costs for the years 2017 through 2025. The changes shown in the direct manufacturing costs 

from year-to-year reflect the cost changes due to learning effects. 

3.2.4 Costs Updated to 2010 Dollarso

This change is simply to update any costs presented in earlier analyses to 2010 dollars 

using the GDP price deflator as reported by the Bureau of Economic Analysis on February 9, 

2012. The factors used to update costs from 2007, 2008 and 2009 dollars to 2010 dollars are 

shown below. 

2007 2008 2009 2010

Price Index for Gross Domestic Product 106.2 108.6 109.7 111.0

Factor applied to convert to 2010 dollars 1.04 1.02 1.01 1.00

Source: Bureau of Economic Analysis, Table 1.1.4. Price Indexes for Gross Domestic Product, 

downloaded 2/9/2012, last revised 1/27/2012.

 

o

 Note that costs in the proposal were in terms of 2009 dollars. 

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

2012 2016 2020 2024 2028

Cost Model Log. (Cost Model)
 Technologies Considered in the Agencies’ Analysis

3-29 

3.3 How did the agencies determine effectiveness of each of these technologies? 

The agencies determined the effectiveness of each individual technology with a 

process similar to the one used for the 2012-2016 light duty vehicle GHG and CAFE 

standards. The individual effectiveness of several technologies discussed in this rule that 

were present in the earlier rule were left largely unchanged while others were updated. EPA 

and NHTSA reviewed recent confidential manufacturer estimates of technology effectiveness 

and found them to be generally consistent with our estimates. Additionally, EPA used vehicle 

simulation modeling to gain further insight on existing and new technologies for this 

rulemaking. EPA conducted a vehicle simulation project (described in 3.3.1) that included a 

majority of the technologies, the results of which: 

informed existing individual technology effectiveness values, 

provided data for newly introduced technologies, and 

most importantly, provided an interactive data source with which to update and 

calibrate the new LP model 

The lumped parameter model then served as the primary tool in evaluating the 

individual technology effectiveness estimates the combined effectiveness of groups of 

technologies (or packages) and synergy factors, as described in 3.3.2. The effectiveness 

values, in conjunction with costs, were then applied to vehicles across the fleet for use in the 

Agencies’ respective compliance models. For the final rule, NHTSA conducted a vehicle 

simulation project with Argonne National Laboratory (ANL), as described in NHTSA’s FRIA 

that performed additional analyses on mild hybrid technologies and advanced transmissions to 

help NHTSA develop effectiveness values better tailored for the CAFE model’s incremental 

structure. The effectiveness values that were developed by ANL for the mild hybrid vehicles 

were applied by both agencies for the final rule. Additionally, NHTSA updated the 

effectiveness values of advanced transmissions coupled with naturally-aspirated engines 

based on ANL’s simulation work for the final rule. 

3.3.1 Vehicle simulation modeling 

3.3.1.1 Background 

For regulatory purposes, the fuel economy of any given vehicle is determined by 

placing the vehicle on a chassis dynamometer (akin to a large treadmill that puts the vehicle’s 

wheels in contact with one or more rollers, rather than with a belt stretched between rollers) in 

a controlled environment, driving the vehicle over a specific driving cycle (in which driving 

speed is specified for each second of operation), measuring the amount of carbon dioxide 

emitted from the vehicle’s tailpipe, and calculating fuel consumption based on the density and 

carbon content of the fuel. 

One means of determining the effectiveness of a given technology as applied to a 

given vehicle model would be to measure the vehicle’s fuel economy on a chassis 

dynamometer, install the new technology, and then re-measure the vehicle’s fuel economy. 

However, most technologies cannot simply be “swapped out,” and even for those that can, 
 Technologies Considered in the Agencies’ Analysis

3-30 

simply doing so without additional engineering work may change other vehicle characteristics 

(e.g., ride, handling, performance, etc.), producing an “apples to oranges” comparison. 

Some technologies can also be more narrowly characterized through bench or engine 

dynamometer (i.e., in which the engine drives a generator that is, in turn, used to apply a 

controlled load to the engine) testing. For example, engine dynamometer testing could be 

used to evaluate the brake-specific fuel consumption (e.g., grams per kilowatt-hour) of a 

given engine before and after replacing the engine oil with a less viscous oil. However, such 

testing does not provide a direct measure of overall vehicle fuel economy or changes in 

overall vehicle fuel economy. 

For a vehicle that does not yet exist, as in the agencies’ analyses of CAFE and GHG 

standards applicable to future model years, even physical testing can provide only an estimate 

of the vehicle’s eventual fuel economy. Among the alternatives to physical testing, 

automotive engineers involved in vehicle design make use of computer-based analysis tools, 

including a powerful class of tools commonly referred to as “full vehicle simulation.” Given 

highly detailed inputs regarding vehicle engineering characteristics, full vehicle simulation 

provides a means of estimating vehicle fuel consumption over a given drive cycle, based on 

the explicit representation of the physical laws governing vehicle propulsion and dynamics. 

Some vehicle simulation tools also incorporate combustion simulation tools that represent the 

combustion cycle in terms of governing physical and chemical processes. Although these 

tools are computationally intensive and required a great deal of input data, they provide 

engineers involved in vehicle development and design with an alternative that can be 

considerably faster and less expensive than physical experimentation and testing. 

Properly executed, methods such as physical testing and full vehicle simulation can 

provide reasonably (though not absolutely) certain estimates of the vehicle fuel economy of 

specific vehicles to be produced in the future. However, when analyzing potential CAFE and 

GHG standards, the agencies are not actually designing specific vehicles. In this rulemaking 

analysis, the agencies have considered the implications of new standards that will apply to the 

average performance of manufacturers’ entire production lines. For this type of analysis, 

precision in the estimation of the fuel economy of individual vehicle models is not essential; 

although it is important that the agency avoid systematic upward or downward bias, 

uncertainty at the level of individual models is mitigated by the fact that compliance with 

CAFE and GHG standards is based on average fleet performance. 

DOT’s CAFE model and EPA’s OMEGA are not full vehicle simulation models. 

Both models use higher-level estimates of the efficacy of different technologies or technology 

packages. Both models apply methods to avoid potential double-counting of efficacy 

addressing specific energy loss mechanisms (e.g., pumping losses), and for this FRM, 

consistent with the proposal, both agencies applied estimates using EPA’s lumped parameter 

model, which was updated using results of full vehicle simulation performed by Ricardo, 

PLC. Although full vehicle simulation could, in principle, be fully integrated into the 

agencies’ model-by-model analyses of the entire fleet to be projected to be produced in future 

model years, this level of integration would be infeasible considering the size and complexity 

of the fleet. Also, considering the forward-looking nature of the agencies’ analyses, and the 

amount of information required to perform full vehicle simulation, this level of integration 
 Technologies Considered in the Agencies’ Analysis

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would involve misleadingly precise estimates of fuel consumption and CO2 emissions. Still, 

while the agencies have used results of full vehicle simulation to inform the development of 

model inputs for performing fleet-level analysis, information from other sources (e.g., vehicle 

testing) could be considered when developing such model inputs. 

3.3.1.2 2011 Ricardo Simulation Study 

For this rule EPA built upon its 2008 vehicle simulation project24 used to support the 

2012-2016 light duty vehicle GHG and CAFE standards. As in the initial project, the 

technical work was conducted by the global engineering consulting firm, Ricardo, Inc. (under 

subcontract to SRA Corporation), using its MSC.EASY5 dynamic vehicle simulation model. 

This section is intended to supplement the main report which has been recently published and 

peer-reviewed1. While this project represents a new round of full-scale vehicle simulation of 

advanced technologies, the scope has also been expanded in several ways to broaden the 

range of vehicle classes and technologies considered, consistent with a longer-term outlook 

through model years 2017-2025. The expanded scope also includes a new analytical tool 

(complex systems analysis tool) to assist in interpolating the response surface modeling 

(RSM) data and visualizing technology effectiveness. This tool was especially useful in 

isolating effectiveness trends during development of the updated Lumped Parameter model. 

The agencies try to use publicly available information as the basis for technical 

assessments whenever possible. Because these standards extend to MY 2025, and include 

some technologies that are not currently in production and for which there is limited 

information available in the literature, some of the technology inputs used to estimate 

effectiveness are based on confidential business information. This includes the inputs related 

to the technologies listed below which were based on confidential business information 

belonging to Ricardo, Inc, and their expert judgment that contributed to projecting how these 

technologies might improve in the future. The agencies have also considered information 

which is in the public domain, in particular for turbo-charged, downsized GDI engines as 

discussed in Section 3.4.1.8, as well as confidential information on engine and transmission 

technologies from automotive suppliers which directionally was in line with the information 

considered by Ricardo. In the draft TSD, the agencies encouraged commenters to submit 

technical information, preferably that may be released publicly, related to these technologies, 

particularly on their effectiveness and ability to be implemented in a way that maintains 

utility. The agencies sought comment and data on the following technologies individually or 

in combination: advanced turbocharged and downsized, atkinson, and advanced diesel (e.g.

projected BSFC maps) engines, hybrid powertrain control strategies, optimized transmission 

shift control strategies, and transmission efficiency improvement. Few comments were 

received specific to these technologies, although the Alliance emphasized that the agencies 

should examine the progress in the development of powertrain improvements as part of the 

mid-term evaluation and determine if researchers are making the kind of breakthroughs 

anticipated by the agencies for technologies like high-efficiency transmissions. Additionally, 

Volkswagen commented that while high BMEP (27-31) bar engines with cooled EGR are 

currently the subject of research, Volkswagen believed that there are significant obstacles, 

such as thermal and mechanical loads and their impacts on costs and durability, low-end 

torque performance and part-load efficiency, which need to be overcome before these engines 

represent a viable option for improving fuel economy while maintaining customer 
 Technologies Considered in the Agencies’ Analysis

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satisfaction. The agencies recognize Volkswagen’s comments, but note that the analysis for 

this final rule considered only high BMEP engines up to 27 bar, and will be monitoring the 

progress of this technology carefully and consider it at the mid-term evaluation. Moreover, 

since this technology does not reach significant levels in our modeling analyses of the final 

standards until after MY 2021, the agencies will evaluate industry experience with this 

technology at the mid-term evaluation and can adjust assumptions as appropriate. 

Below is a summary of the significant content changes from the 2008 simulation 

project to the 2011 simulation project that supports the final rule, consistent with the proposal. 

3.3.1.2.1 More Vehicle Classes 

Two additional vehicle classes were considered, for a total of seven classes: a small 

car (subcompact) and a medium/heavy duty truck class. The inclusion of the small car class 

increased the fidelity of the results by capturing engineering differences unique to the smallest 

vehicles in the market. The inclusion of the medium/heavy duty truck was meant primarily to 

support EPA’s analysis for the Heavy Duty GHG Rule25. It is worth noting that these vehicle 

classes are for simulation purposes only and are not be confused with regulatory classes, 

OMEGA classes, or NHTSA’s technology subclasses for CAFE modeling. 

3.3.1.2.2 More engine and vehicle technologies 

The original 2008 project modeled several engine and transmission technologies that 

were expected to become commercially available within the 2012-2016 time frame. These 

technologies included advanced valvetrain technologies (such as variable valve timing and 

lift, cylinder deactivation), turbocharged and downsized engines, as well as 6 speed automatic 

transmissions, CVTsp

and dual-clutch transmissions. The current project built on top of this 

effort with the inclusion of several new engine and vehicle technologies. Highlighted 

examples included: 

Advanced, highly downsized, high BMEPqturbocharged engines 

High efficiency transmissions with 8 speeds and optimized shift strategies to 

maximize vehicle system efficiency 

Atkinson-cycle engines for hybrids 

Stop-start (or idle-off) technology 

A discussion of these technologies is included Section 3.3.1.2, and also in the 2011 

vehicle simulation report1. 

 

p

 Continuously variable transmissions 

q

 BMEP refers to brake mean effective pressure, a common engineering metric which describes the specific 

torque of an engine, as a way of comparing engines of different sizes. It is usually expressed in units of bar, or 

kPa, Current naturally aspirated production engines typically average 10-12 bar BMEP, while modern 

turbocharged engines are now exceeding 20 bar BMEP with regularity. Simply put, a 20 bar BMEP 

turbocharged engine will provide twice the torque of an equivalently-sized engine that achieves 10 bar BMEP. 
 Technologies Considered in the Agencies’ Analysis

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3.3.1.2.3 Includes hybrid architectures 

For the first time, this new work includes modeling of hybrid architectures for all 

vehicle classes. Two main classes of hybrids were considered: 

Input powersplit hybrids. Examples of input powersplits in the market today 

include the Ford Fusion HEV and the Toyota Prius. 

P2 hybrids. An example of the P2 hybrid is the Hyundai Sonata Hybrid. 

While input powersplit hybrids remain a very likely hybrid architecture choice for 

some manufacturers, the agencies focused solely on P2 hybrids compared to powersplit 

hybrids due to their apparent cost-effectiveness advantage in future years. 

Ricardo proprietary methodology was used to develop control strategies for each 

architecture, the details of which can be found in section 6.8 of the 2011 project report1. 

3.3.1.2.4 Complex systems tool for data analysis 

In the original 2008 project, EPA staff selected unique technology packages, based on 

engineering judgment, to cover a representative subset of possible vehicle options ending in 

MY 2016. The expanded project time horizon (through MY 2025) and increased complexity 

of potential vehicle technology interactions (including hybrids) made package selection much 

more difficult. To account for unforeseen results and trends which might exist, EPA and 

Ricardo adopted a complex systems approach, which is a rigorous computational strategy 

designed to mathematically account for multiple input variables and determine the 

significance of each (the complex systems approach is described in further detail in the 2011 

Ricardo report). As a comparison, in the 2008 study, twenty-six unique technology packages 

spanning five vehicle classes were selected by EPA staff and then modeled. For this project a 

set of core technology packages were chosen for each vehicle class, constituting a total of 107 

unique vehicle packages (“nominal runs”), which are shown as Table 3-5 and Table 3-6 in 

3.3.1.2.8. A neural network Complex Systems approach to design of experiments (DOE) was 

then applied to generate a set of response surface models (RSM), in which several input 

parameters were varied independently over a specified range to identify the complex 

relationship between these inputs and the vehicle performance. Using these methods, the 

vehicle simulation was run for a set of discrete input variables chosen based on a full factorial 

analysis, using a computationally efficient algorithm to select each input variable within the 

design space, allowing for subsequent statistical regression of the output variables. This 

approach resulted in an average of approximately two thousand independent simulation runs 

for each of the 100+ vehicle packages, the outputs of which were interpolated in the data 

analysis tool developed for this modeling activity. For each of these nominal and DOE runs 

Ricardo provided detailed 10-hz output data csv files for reviewr.

 

r

 Stakeholders wishing to obtain this data may contact EPA to arrange for transfer of the data. Due to the 

considerable size of the files (2 terabytes), stakeholders must supply their own storage media. 
 Technologies Considered in the Agencies’ Analysis

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An interactive Complex Systems analysis and visualization tool was developed to 

interpret the vast arrays of RSM data generated as part of the project. It was created to sample 

a selected portion of the design space populated using the DOE approach described above, 

and then interpret the RSM data set in a form that could be used to calibrate the lumped 

parameter model (reference the equivalent-performance results in Section 3.3.1.2.18). For 

more detail on the use of the RSM tool, refer to the 2011 Ricardo report1.

3.3.1.2.5 Process 

The core technical work, completed in February 2011, consisted of the following 

steps: 

Definition of project scope 

Selection of vehicle classes and baseline vehicle characteristics 

Selection of vehicle architectures and individual technologies 

Selection of swept variables for use in the RSM matrix 

Selection of vehicle performance metrics 

Review and revision of the input assumptions and modeling process 

Build and run the baseline EASY5 vehicle models 

Review of baseline runs and checking for errors 

Build and run the nominal technology package EASY5 vehicle models 

Review results and debug 

Run complete DOE matrix for each technology package 

Incorporation of DOE results into RSM tool 

3.3.1.2.6 Definition of project scope 

At project initiation, an advisory committee was formed and led by EPA to help guide 

the analysis. The advisory committee consisted of technical experts from CARB and The 

ICCT, the latter of which co-founded the project. A complete list of advisory committee 

members is found in the vehicle simulation project report1. The committee agreed upon the 

underlying ground rules, reviewed modeling assumptions and identified the desired vehicle 

architectures and selected technologies for review. The boundaries for the project are 

highlighted (quoted) below: 

A total of seven vehicle classes will be included: small car, standard car, large car, 

small and large MPVs (multi-purpose vehicles), truck and HD truck 

LDV technologies must have the potential to be commercially deployed in the 

MYs 2020-2025 timeframe 

Vehicle sizes (footprint and interior space) for each class will be largely 

unchanged from MY 2010 to MYs 2020-2025 
 Technologies Considered in the Agencies’ Analysis

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Hybrid vehicles will use an advanced hybrid control strategy, focusing on battery 

state-of-charge management, but will not compromise vehicle drivability 

Ricardo simulation study uses certification gasoline and 40 cetane pump diesel to 

determine the effectiveness of engine technologies. The certification gasoline 

typically has an RON of approximately 95 versus approximately 91 for regular 

grade 87 anti-knocking index gasoline. 

It is assumed that MYs 2020-2025 vehicles will meet future California LEV III 

requirements for criteria pollutants, approximately equivalent to current SULEV II 

(or EPA Tier 2 Bin 2) emissions levels 

Changes in vehicle road loads including mass, aerodynamic drag, and rolling 

resistance, will not be accounted for in any of the modeled technologies. Instead, 

changes in vehicle road loads may be addressed through user-specified continuous 

input variables in the Complex Systems tool. 

The committee also decided that the following technologies fell outside the scope of 

the project, either due to project resource limitations, lack of sufficient input data, or a low 

potential to be commercially deployed in the timeframe considered: 

Charge-depleting powertrains (e.g. plug-in hybrids and electric range-extended 

vehicles) and electric vehicles 

Fuel cell-powered vehicles 

Non-reciprocating internal combustion engines or external combustion engines 

Manual transmissions and single-clutch automated manual transmissions (AMTs) 

Kinetic energy recovery systems other than battery systems 

Intelligent vehicle-to-vehicle (V2V) and vehicle-to-infrastructure optimization 

technology 

Bottoming cycles (such as organic Rankine cycles) for energy recovery 

Vehicle safety systems or structures will not be explicitly modeled for vehicles, as 

it is beyond the scope of the study 

The committee also selected a set of swept input variables (vehicle parameters) which 

were considered most important to vehicle fuel economy and performance (swept variables 

are continuously variable input values that affect vehicle output efficiency in a smooth 

function for the response surface model). These variables consisted of engine displacement, 

final drive ratio, electric drive motor size (for hybrids), as well as road load factors (vehicle 

mass, aerodynamic drag, and rolling resistance). All of these input variables were 
 Technologies Considered in the Agencies’ Analysis

3-36 

randomized in each vehicle design of experiment matrix and then incorporated into the postprocessing RSM data visualization tool. 

3.3.1.2.7 Selection of vehicle classes and baseline vehicle characteristics 

In order to estimate both technology costs and CO2 reduction estimates, it is necessary 

to describe the baseline vehicle characteristics as the basis from which comparisons may be 

drawn. In the MYs 2012-2016 light-duty vehicle rule the vehicle baseline was defined as 

having a naturally aspirated gasoline engine with a port-fuel injection system, two intake and 

two exhaust valves and fixed valve timing and lift; the baseline transmission was a 

conventional 4-speed automatic, with no hybrid systems. These vehicles are referred to 

throughout this section as the “2008 baselines.” For the present study, EPA and Ricardo 

elected to include a set of “2010 baseline” technology vehicles, which reflect MY 2010 trends 

in engine and vehicle technology as well as some technologies that are expected to be 

widespread within a few years. It is important to note that the 2010 baseline vehicles in the 

Ricardo study do not reflect the technology content of the baseline fleet vehicles used by each 

agency in their respective compliance modeling. The Ricardo 2010 baseline vehicles are only 

used in the analysis required to establish effectiveness and synergies in the lumped parameter 

model. The 2010 baseline vehicles all include an engine with dual overhead camshaft and 

dual-independent intake/exhaust valve timing, a six-speed automatic transmission, 12-volt 

idle off (stop-start) functionality and an alternator with partial energy regeneration capability. 

There is no change in the engine displacement or vehicle road load coefficients between the 

2008 baseline and the 2010 baseline vehicles. For a table showing the 2010 baseline vehicle 

characteristics refer to Appendix 3 of the 2011 Ricardo report1.

In the Ricardo study, seven vehicle classes were selected for the analysis, in order to 

more fully represent the broad groupings of a wide variety of products offered in the US 

passenger car and light-duty truck market. The seven vehicle categories chosen were as 

follows: 

Small car: a subcompact car typically powered by a small 4 cylinder engine. 

Standard car: a midsize car typically powered by a small 6 cylinder engine. 

Large car: a large passenger car typically powered by a large 6 cylinder engine. 

Small MPV: a small multi-purpose vehicle (MPV) or “crossover” vehicle typically 

powered by a 4 cylinder engine 

Large MPV: a minivan or large MPV or “crossover” unibody constructed vehicle 

with a large frontal area, typically powered by a 6 cylinder engine, capable of carrying 

~ 6 or more passengers. 

Large truck (1/2 ton): large sports-utility vehicles and large pickup trucks, typically a 

ladder-on-frame construction, and typically powered by an 8 cylinder engine. 

Class 2b/3 truck (3/4 ton): a large pickup truck (although with a GVW no greater than 

8.500 pounds) with a heavier frame intended to provide additional utility (a.k.a. 

“work” truck), typically powered by a larger 8 cylinder gasoline or diesel engine. 
 Technologies Considered in the Agencies’ Analysis

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3.3.1.2.8 Technology selection 

Ricardo presented the committee with an array of potential technologies that might 

become commercially viable and present in the light-duty market by MY 2025. EPA and the 

Advisory Committee suggested additional other technologies, e.g. Atkinson engines for 

hybrids, fast engine warm-up strategies, etc, to consider in the selection process. The 

complete set of potential technologies can be found in Appendix 2 of the 2011 Ricardo 

report1. After further deliberation within the committee and by Ricardo, a subset of 

technologies considered most promising (from a technical feasibility and cost effectiveness 

standpoint) was selected by the committee and Ricardo for inclusion in the project test matrix. 

The technologies were distributed among four distinct vehicle architectures. These 

architectures represented unique EASY5 model structures, and are listed below: 

2010 Baseline vehicles: intended to represent physical replicas of existing vehicle 

models, although some minor additional content was included (as described in 

Section 3.3.1.2.7) 

Conventional stop-start: vehicles for the MYs 2020-2025 timeframe that included 

advanced engines but did not incorporate an electric drive or braking energy 

recovery. These vehicles all contained a 12 volt stop-start (or idle-off) capability, 

along with the following technologies further detailed in the 2011 Ricardo 

simulation studys: 

o higher efficiency gearbox (2020 timeframe) 

o optimized shift strategy (best BSFC) 

o alternator regeneration (during braking) 

o high-efficiency alternator 

o advanced engine warmup technologies 

o engine friction reduction (+3.5% fuel consumption reduction over 

2008 baseline) 

P2 hybrid: represent a class of hybrids in which the electric drive motor is coupled 

via a clutch directly to the transmission input shaft. An existing vehicle in the 

market which most closely represents this architecture is the 2011 Hyundai Sonata 

Hybrid except that Ricardo recommended a P2 hybrid with a more efficient and 

cost effective dual clutch transmission in lieu of an automatic transmission. 

Additional examples of a P2 hybrid approach are the 2011 Volkswagen Touareg 

Hybrid, the 2011 Porsche S Hybrid, and the 2012 Infiniti M35 Hybrid. Each of 

these are examples of “first generation” P2 systems, as compared to for example 

the powersplit hybrid systems offered by Ford, Toyota and or the IMA systems 

 

s

 The technologies included in all of the conventional stop-start packages were expected to be widespread by 

years 2017-2025. Some “anytime technologies” such as aerodynamic drag and rolling resistance reduction were 

excluded from the nominal runs, but were incorporated in the complex systems portion of this project. 
 Technologies Considered in the Agencies’ Analysis

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from Honda which are in their second, third or even fourth generation. The 

agencies are aware of some articles in trade journals, newspapers and other 

reviews that some first generation P2 hybrid vehicles with automatic transmissions 

have trade-offs in NVH and drivability – though these reviews do not cover all of 

the P2 systems available today, and a number of reviews are very positive with 

respect to NVH and drivability. For this analysis we are projecting that these 

issues with some first generation P2 systems can be addressed with no hardware 

cost increase or reduction in efficiency for future generations of P2 systems 

developed for the 2017-2025 time frame. The agencies sought comment on our 

assumptions in this regard, and we requested comment on the applicability of 

DCTs to P2 hybrid applications, including any challenges associated with NVH or 

drivability. There were no comments submitted. Key technology assumptions 

included: 

o Lithium-ion battery 

o DCT transmission 

o Electric drive motor which provides, when combined with a less 

powerful engine, equivalent 0-60 performance to the baseline 

vehicle. 

o Engine displacement for the P2 hybrids were assumed to be 20% 

less than their conventional stop-start equivalents 

Input powersplit hybrid: represent a class of hybrids with both an electric drive 

motor and a separate generator linked to a planetary gearset which effectively 

controls the overall gear ratio and distribution of tractive and electrical power. 

Example vehicles in the market include the Toyota Prius and the Ford Fusion 

hybrid. Key technology assumptions are consistent with those for the P2 hybrid, 

with the exception of the power split device, which functions as a CVT-type 

transmission (as is the case in real world examples), and replaces the DCT 

transmission in the P2 design. As stated previously while this technology was 

simulated it was not used in this FRM analysis, consistent with the proposal. 

Some architectures that seemed less appropriate for certain vehicle classes were 

omitted. For example, in the Ricardo modeling of the medium/heavy duty truck (a Class 3 

vehicle with a GVWR >10,000 pounds, and thus not subject to the final standards in this 

rulemaking), no P2 or input powersplit hybrids were included. Other technologies that did 

not seem reasonable for some vehicle classes (such as dry-clutch DCTs for Large MPVs and 

Trucks) were also excluded in the Ricardo simulations. 

In summary, 4 distinct vehicle architectures (including the baselines as an 

“architecture”), across 7 vehicle classes, and a number of engine and transmission 

combinations, represented the complete set of vehicle combinations. The test matricest

can be 

 

t

 For each vehicle class, each advanced engine option is combined with each advanced transmission. Baseline 

runs are not combined with other transmissions. 
 Technologies Considered in the Agencies’ Analysis

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found below in Table 3-5 (for 2010 baselines and conventional stop-start vehicles) and Table 

3-6 (for hybrids). 

Table 3-5: Nominal Package Matrix for Non-Hybrids 

Table 3-6: Nominal package matrix for P2 and Input Powersplit hybrids 

Vehicle Class

Stoich DI Turbo with CPSLean DI Turbo with CPSEGR DI Turbowith DVA2020 Diesel6-Speed Automatic6-Speed Dry DCT8-Speed Automatic8-Speed Dry DCT8-Speed Wet DCT

Small Car X X X X X X X

Standard Car X X X X X X

Small MPV X X X X X X

Full Size Car X X X X X X X

Large MPV X X X X X X X

LDT X X X X X X X

LHDT X X X X X X X X

Advanced Engine Advanced Transmission

Baseline Engine & 2010 6-Speed Automatic 2010 Diesel & 2010 6-Speed Automatic Transmission

Vehicle Class

 P2 Hybrid with 2020 DCT Input Powersplit Stoich DI Turbo with CPS Lean DI Turbo with CPS EGR DI Turbo with CPS Atkinson with CPS Atkinson  with DVA

Small Car X X X X X X X

Standard Car X X X X X X X

Small MPV X X X X X X X

Full Size Car X X X X X X X

Large MPV X X X X X X X

LDT X X X X X X

LHDT

Hybrid Architecture Advanced Engine
 Technologies Considered in the Agencies’ Analysis

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3.3.1.2.9 Selection of the swept input variables and their ranges 

The advisory committee agreed upon a set of continuous input variables to be swept in 

each vehicle package response surface. These variables consisted of both powertrain 

characteristics (engine displacement, final drive ratio, and electric machine size for hybrids) 

and road load parameters (rolling resistance coefficient, aerodynamic drag force, and vehicle 

mass). They were included in the DOE matrix for each vehicle architecture and powertrain 

configuration, and also serve as inputs to the complex systems visualization tool. Table 3-7

and Table 3-8 show the swept variables used (and their ranges) for the conventional stop-start 

and hybrid packages, respectively. The ranges represent a percentage of the default value 

used in the nominal runs. 

Table 3-7: Continuous input parameter sweep ranges for conventional stop-start vehicle 

Table 3-8: Continuous input parameter sweep ranges for P2 and Powersplit hybrid vehicles 

The ranges were intended to include both the (unknown) optimal value for each 

technology case, but also wide enough to capture the range of values as they depart from the 

optimal value (in engineering parlance this is often referred to as finding the “knee” in the 

curve). 

From these variables, a user can determine the sensitivity of each input variable to the 

vehicle fuel economy and performance. For example, the effect of engine displacement on 

fuel economy was evaluated for several packages. A more elaborate discussion of engine 

displacement effects is provided in Section 3.3.1.2.24.2.

Parameter

Engine Displacement 50 125

Final Drive Ratio 75 125

Rolling Resistance 70 100

Aerodynamic Drag 70 100

Mass 60 120

DoE Range (%)

Engine Displacement 50 150 50 125

Final Drive Ratio 75 125 75 125

Rolling Resistance 70 100 70 100

Aerodynamic Drag 70 100 70 100

Mass 60 120 60 120

Electric Machine Size 50 300 50 150

P2 Hybrid Powersplit

DoE Range (%)

Parameter
 Technologies Considered in the Agencies’ Analysis

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3.3.1.2.10 Selection of vehicle performance metrics 

For both effectiveness and cost estimates in these rulemakings, the agencies are 

assuming that vehicles will maintain utility (performance) comparable to the models in the 

baseline fleetu. It was therefore important to maintain equivalent performance in the vehicle 

simulation modeling of future vehicle technology. The resulting effectiveness estimates were 

in the context of equivalent performance, which carried over into the lumped parameter model 

and into the OMEGA and CAFE model packages. 

Consistent with the 2008 simulation project, a set of vehicle (acceleration) 

performance metrics were selected by the advisory committee as a way of measuring 

“equivalent” vehicle performance. When quantifying vehicle efficiency, it is important that 

certain other vehicle performance metrics are maintained, such that there are no other 

competing factors contributing or detracting from the vehicle efficiency. Other vehicle 

characteristics that could impact or detract from vehicle efficiency (e.g., noise, vibration and 

harshness (NVH), drivability, durability, etc) were also considered during the generation of 

model inputs. However, they were not analyzed explicitly, with the expectation that 

manufacturers would ultimately be able to meet vehicle refinement levels necessary for 

commercial acceptability of these new technologies. These metrics, shown below in Table 

3-9, include time at full load to reach given speeds (0-10 mph, 0-30 mph, etc), maximum 

grade capability, and distance traveled at a given time (e.g., after 3 seconds). Ultimately, the 

measure of equivalent performance is up to the reader or user of the Complex Systems tool. 

For EPA’s analysis baseline vehicle 0-30 mph and 0-60 mph acceleration times were used as 

a benchmark for equivalent performance for the advanced vehicle packages. These estimated 

acceleration times are included in Table 3-11 through Table 3-18. Detailed results that 

include all performance metrics including those for baseline vehicles are provided in the full 

2011 simulation report1. 

 

u

 The only exception to this is a subset of hybrids explicitly listed as “non-towing” vehicles. For further details 

and background, reference Section 1.3 of EPA’s RIA. 
 Technologies Considered in the Agencies’ Analysis

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Table 3-9: Vehicle performance metrics produced by the EASY5 model 

3.3.1.2.11 Review and revision of inputs 

For any system modeling in which the results extend beyond the bounds of known 

physical examples (and therefore direct data validation is impossible), it is imperative that the 

inputs be carefully constructed and thoroughly examined to minimize the potential for 

uncertainty-related errors. Prior to coding of the models, Ricardo presented the following 

inputs for review and approval to EPA. For each topic, EPA reviewed the material 

considering the rationale of Ricardo’s technical experts, the appropriateness of the inputs in 

relation to the assumed time horizon, the required emissions levels, and the known literature 

in the field today. Listed below are several of the model inputs that were jointly reviewed by 

Ricardo and EPA: 

Engine maps 

o Stoichiometric GDI turbo 

o Lean-burn GDI turbo 

o Cooled EGR turbo 

o Advanced diesel maps 

Transmission efficiency tables (by gear) including torque converter efficiency 

Engine warm-up strategy (cold start modifiers) 

Alternator regeneration strategy 

Transmission shift optimizer 

Engine friction reduction level 

P2 hybrid controls 

Launch (WOT) Passing (WOT)

 Gradeability/ 

torque reserve

0-10 mph 30-50 mph 

Max Speed @ 5% 

grade

0-30 mph 50-70 mph

Max Speed @ 10% 

grade

0-50 mph

Max Grade @ 70 

mph (non-towing)

0-60 mph

Max Grade @ 60 

mph (towing)

0-70 mph

Distance @ 1.3 sec

Distance @ 3 sec

Speed @ 1.3 sec

Speed @ 3 sec
 Technologies Considered in the Agencies’ Analysis

3-43 

Input powersplit hybrid controls

Hybrid battery assumptions 

Hybrid motor/generator efficiency maps 

EPA technical experts recommended several changes and iterated with Ricardo to 

establish a consensus set of inputs that were plausible and met the ground rules of the project. 

Some of these changes resulted in higher efficiencies, while others lowered efficiency. 

Highlighted below are a few key examples, starting with development of the engine maps: 

Engine maps carry perhaps the most significance of any of the sets of inputs needed to 

build vehicle simulation models. They provide the brake specific fuel consumption, or BSFC 

(typically in g/kWh) for a given engine speed and load. Typically these maps show an 

optimum speed and load band (or minimum BSFC “island”) that is the most efficient 

condition in which to operate the engine. Ricardo generated engine maps for both the 

baseline vehicles (through benchmarking data) and proposed future engine maps for the 

various turbocharged and diesel engines. Figure 3-3 shows an example engine map for a 

baseline vehicle. It was constructed from EPA’s analysis of a baseline vehicle model run 

output file. The contours represent lines of equivalent brake-specific fuel consumption.v

3.3.1.2.12 Engine Technologies 

Ricardo developed the engines for the 2012-2025 timeframe in two ways. The first 

was to take current boosted SI research engines and project these would represent the level of 

performance which could be achieved by production engines in the 2020-2025 timeframe. 

The second method took current production Atkinson cycle SI and diesel engines and then 

included 2020-2025 timeframe technology improvements. Both methods extrapolated current 

engine design and development trend to the 2020-2025 timeframe. These current trends 

include engine friction reduction, improved fuel injection systems (e.g., spray guided for the 

SI, and higher injection pressures for the diesels), more advanced engine controls, and 

improved engine design for faster engine warm-up. EPA reviewed the engine maps 

recommended by Ricardo and generally concurred they were appropriate for the study time 

frame based on EPA’s review of maps for current production engines and for research engines 

described in the literature. 

 

v

 BSFC is measured in units of grams of fuel per kW-hour of energy and is an indicator of engine efficiency. 

Lower numbers indicate more efficient operating regions. As in this case, an engine typically has an “island’ or 

region of best efficiency, in this case between 2000-3000 RPM and 150-180 Nm of torque. This island becomes 

much larger with the advent of advanced technologies such as boosting and downsizing, as well as advanced 

valvetrain technologies. 
 Technologies Considered in the Agencies’ Analysis

3-44 

Figure 3-3: Example baseline engine BSFC map 

3.3.1.2.12.1 Stoichiometric GDI 

The original stoichiometric GDI map that Ricardo proposed was based on laboratory 

data they had published in 2007, showing a peak brake-specific load of just under 20 bar 

BMEP and a minimum BSFC of approximately 235 g/kWh, obtained using a compression 

ratio of 10.5:1.26

 However, based on input from manufacturers and from other, more recent 

published data on developmental and research engines, EPA asked Ricardo to raise the load 

capability of the engine to approximately 27 bar BMEP.27,28,29,30 This allowed a greater degree 

of engine downsizing, which resulted in a downsizing of a 1.5 liter engine to a 0.74 liter 

engine for the nominal small car and a 5.4 liter to a 1.94 liter engine for the nominal large 

truck. A compression ratio of 10.5:1 was maintained for improved efficiency. At the same 

time, EPA asked that Ricardo eliminate the use of high-load enrichment, since water-cooled 

exhaust manifolds, in some cases integrated into the cylinder head, can be incorporated in 

next-generation designs to mitigate the need for fuel enrichment in lowering turbine inlet 

temperatures to 950 degrees C and thus avoid the added costs of high-temperature materials in 

the turbocharger.31,32

 By reducing the need for fuel enrichment fuel consumption is reduced 

over the more aggressive portions of the drive cycle, and PM emissions control at high load is 

improved. 
 Technologies Considered in the Agencies’ Analysis

3-45 

3.3.1.2.13 Lean-burn GDI 

Ricardo’s initial lean-burn GDI map was based on their single-cylinder research 

engine data, in which they operated in lean stratified charge mode at all speeds and loads, 

without due consideration of the potential limitations in lean exhaust NOx aftertreatment 

systems. To address concerns in this area, EPA examined the boundaries of operation of leanNOx catalysts, assuming that manufacturers would adopt either LNTs or metal-zeolite urea 

SCR systems. EPA therefore asked Ricardo to place a constraint on the maximum allowable 

catalyst space velocity (at high engine power) and exhaust gas temperature entering the 

catalyst (at high load, low engine speed conditions) to maintain catalyst efficiency at high 

load and to reduce thermal sintering of PGM under high-temperature, lean operating 

conditions. More specifically, EPA recommended that engine operation switch away from 

lean operation (at air/fuel equivalence ratios up to approximately λ=1.5) to stoichiometric 

operation at turbine outlet temperatures above 600C, and at total exhaust flows corresponding 

to space velocities of 60,000/hour, assuming a catalyst volume of 2.5 times engine 

displacement. This marginally diminished the engine brake thermal efficiency to 

stoichiometric GDI levels over this region of the map, but it provided more certainty that the 

engine would be able to adhere to the emissions levels as assumed in the project ground rules 

by the Advisory Committee. Figure 3-4 shows the engine speed and load region EPA 

proposed as suitable for lean stratified operation. 
 Technologies Considered in the Agencies’ Analysis

3-46 

Figure 3-4 Proposed lean/stoichiometric operating threshold for lean-burn GDI engines 

 

3.3.1.2.13.1 Cooled EGR GDI 

EPA provided technical information from the literature which enabled Ricardo to 

assume a dual loop (both low pressure and high pressure EGR loops), cooled EGR system in 

addition to the stoichiometric turbocharged engine. The development of engine maps for this 

engine configuration was heavily informed by recently published data.30,31,32,33. Cooled EGR 

allowed the use of “λ=1” operation at the same compression ratio with more aggressive spark 

timing at high load and reduced pumping losses at part load while maintaining acceptable 

turbocharger inlet temperatures. 

3.3.1.2.13.2 Motor/generator and power inverter efficiency maps 

EPA recommended that Ricardo update the efficiency maps of the motor and 

generator (referred to as “electric machines” throughout the project), which they had proposed 

BMEP [bar]

0

5

10

15

20

25

30

Engine Speed [rpm]

0 1000 2000 3000 4000 5000 6000 7000

500

550

550

550

700

– 700 – Temperature

– 40000 – NOx Catalyst Space Velocity

Transition from lean to λ=1 operation
 Technologies Considered in the Agencies’ Analysis

3-47 

based on current best-in-class technology. The baseline motor/generator+inverter efficiency 

map is taken from a 2007 Camry and shown in Figure 3-5 below. 

Figure 3-5: 2007 Camry Hybrid motor-inverter efficiency map (Burress, et al, 200834) 

EPA requested that Ricardo provide their assessment of where they believed 

efficiency improvements might be made, based upon trends in research and development for 

both electric machines and power electronics. Ricardo and EPA generally agreed that these 

efficiency improvements were likely to be modest, particularly given the competitive 

pressures on manufacturers to reduce the cost of hybrid components. However, EPA and 

Ricardo assumed that today’s best-in-class efficiency would likely be marginally improved 

through continuous incremental reductions in parasitic losses. To account for this, EPA and 

Ricardo agreed to reduce the losses in the motor/generator by 10% (in other words, raising the 

efficiency of a 90% efficient motor to 91%) and to reduce the losses in the power electronics 

by 25% (mainly through continued improvements in inverter development and electronic 

control systems). 

3.3.1.2.13.3 Battery 

Battery packs were assumed to consist of spinel LiMnO2 cathode chemistry, which is 

consistent with the current state of technology. EPA recommended a maximum usable state of 

charge of 40% (from 30% charge to 70% charge) be incorporated as an operating window in 

Ricardo’s hybrid control logic. This range may increase in subsequent real world examples as 

manufacturers gain more field experience with long term battery durability. Additionally 

there will likely be more advances in battery construction and chemistry by 2025, so EPA 

considers these assumptions as conservative in view of the long term research currently 

underway in many battery research companies. 
 Technologies Considered in the Agencies’ Analysis

3-48 

3.3.1.2.14 Additional Technologies Modeled by Ricardo for 2011 Report 

The previous section discusses in detail those areas of the Ricardo simulation inputs 

which EPA provided recommendations to Ricardo on and which Ricardo agreed and made 

modifications to their initial suggestions. EPA did review modeling inputs for many other 

technologies modeled by Ricardo, but for which we generally agreed with the reasonableness 

of Ricardo’s approach and did not request any changes. This section summarizes at a high 

level some of the additional technologies considered by Ricardo. Additional detail on these 

technologies is contained in the 2011 Ricardo final report. 

Diesel engines - Ricardo started with existing production engines and identified 

technology advances that would lead to further advances in fuel consumption. These included 

many of the same technologies considered for advanced gasoline engines, such as engine 

friction reduction, improved fuel injection systems with higher injection pressures and more 

advanced controls, and better engine design to improve engine warm-up rate. 

Transmission Technologies - Taking a systems approach in the vehicle simulation 

modeling, Ricardo also introduced additional transmission and driveline oriented technologies 

that may be pathways to increased efficiency. Some of these key technological enablers 

include: shift optimization schedules, advanced clutches, torque converter design and lockup 

schedules. 

Automatic and Dual Clutch Transmissions - For the study timeframe, Ricardo 

assumed that eight-speed automatic transmissions will be in common use, as this supports 

more efficient operation, except for small cars, with energy losses expected to be about 20–

33% lower than in current automatic transmissions. Energy losses in both wet clutch and dry 

clutch DCTs are expected to be 40–50% lower than in current automatic transmissions. 

Transmission Shift Optimization - This advanced transmission shift optimization 

strategy tries to keep the engine operating near its most efficient point for a given power 

demand in effort to emulate a CVT. To protect against operating conditions out of normal 

range, several key parameters were identified, such as maximum engine speed, minimum 

lugging speed, and minimum delay between shifts. During development of this strategy, 

Ricardo estimated that fuel economy benefits of up to 5% can be obtained when compared to 

typical MY 2010 shift maps. 

Torque Converter Technology – Ricardo utilized a lockup clutch model with a multidamper system to provide earlier torque converter clutch engagement. The advanced 

automatic transmission applications allow torque converter lockup in any gear except first 

gear, up to sixth for the Small Car or eighth for the other LDV classes. 

 Shifting Clutch Technology - Shift clutch technology improves the thermal capacity 

of the shifting clutch to reduce plate count and lower clutch losses during shifting. Reducing 

the number of plates for the shifting process and reducing the hydraulic cooling requirements 

will increase the overall transmission efficiency for similar drivability characteristics. 

Dry Sump Technology – A dry sump lubrication system provides benefits by keeping 

the rotating members out of oil, which reduces losses due to windage and churning. This 

approach will provide a GHG emissions benefit across all vehicle classes, with the best 

benefits at higher speed. 
 Technologies Considered in the Agencies’ Analysis

3-49 

3.3.1.2.15 Baseline models built and run 

Once all of the inputs were established, Ricardo built the baseline models: For these 

new (2010) baseline models Ricardo added a group of minor technologies, most of which 

already exist today in the market. The technologies included 12V stop-start, 6-speed 

automatic transmission, a high efficiency (70% efficient) alternator, and a strategy – 

“alternator regen” – that charges the 12V battery more aggressively by increasing the 

alternator field upon vehicle deceleration . 

In the 2008 study Ricardo validated their baseline models with 2008 MY certification 

data. Ricardo’s 2010 baseline model results provided effectiveness data for EPA to calibrate 

the lumped parameter model for some of the newly applied technologies. These technologies 

included alternator regeneration, high efficiency alternator, and stop-start. 

For all model runs – the baselines and each of the advanced package nominal runs – 

EPA reviewed an extensive set of detailed intermediate output data for each model run. The 

parameters that were reviewed are shown in Table 3-10.

Table 3-10: Vehicle simulation output data reviewed 

Ricardo outputs EPA-calculated outputs

vehicle speed engine operating point distribution

throttle position engine load (BMEP)

engine torque total accessory energy

engine power round-trip battery loop losses

transmission input shaft torque torque converter lockup time

wheel torque total road load

transmission gear total engine brake thermal energy

torque converter slip ratio

current engine BSFC EPA-calculated metrics

accessory power cycle-average BSFC

engine speed average brake thermal efficiency

road load average engine power

N/V average engine speed

electric power of motor generator average engine torque

mechanical power of motor generator # of idle-off events

motor generator speed % of engine time off

motor generator torque average accessory power

motor generator current time in each gear

motor generator voltage average gear efficiency

power flow through battery average torque converter efficiency

battery state of charge battery state-of-charge statistics

battery voltage battery efficiency

regenerative braking power % of vehicle braking energy recovered

vehicle foundation braking power average motor efficiency

driver braking force average generator efficiency

fuel mass flow rate average motor and generator operating speeds

transmission mechanical loss power average motor and generator operating torque

idle off status total vehicle tractive energy
 Technologies Considered in the Agencies’ Analysis

3-50 

From this data, a set of summary statistics was generated to compare each baseline and 

nominal package run as a quality check. This information was used as the starting point in the 

dialogue between EPA and Ricardo to identify technical issues with the models. An example 

summary table (or “snapshot”) for the 2010 Standard Car baseline is provided in Figure 3-6. 
 Technologies Considered in the Agencies’ Analysis

3-51 

Figure 3-6 Sample output summary sheet for Standard Car (Camry) baseline 

Vehicle FTP Hwy Combined US06

CO2 Emissions (g/mi) 303.8 209.0 261.2 312.2 Engine Engine Trans # MG1 MG2 Battery

Fuel Economy (mpg) 29.9 43.5 34.8 29.1 Disp Torque Type of size size size

2007 Base Vehicle CO2 (g/mi) 337.8 217.5 283.7 L Nm gears kW kW kWh % CO2 Reduction 10.1% 3.9% 7.9% 2.4 220 base auto 6 n/a n/a n/a

Engine FTP Hwy Combined US06

Avg Brake Thermal Efficiency 21.7% 27.8% 23.8% 30.6%

Cycle Avg BSFC (g/kWh) 376 295 344 267

Avg Engine Power (HP) 7.0 14.1 10.2 23.0

Avg Engine Speed (RPM) 1993 1833 1921 2453 0-10mph 0-30mph 0-60mph base 0-60 30-50mph 50-70mph dist @ 3s

Avg Load (BMEP-bar) 2.21 3.27 2.69 5.19 1.0 3.1 8.3 8.3 3.2 5.1 20.5

Avg Torque (Nm) 42.1 62.5 51.3 99.1 for using Ricardo maps

Total Fuel (g) 1026.4 657.8 860.5 764.8

Idle Off Events 20 1 n/a 5 % Time Off 18.0% 0.5% 10.1% 6.5%

Accessory Loss 0.0% 0.5% 0.3% 0.0% % of FC

Avg accessory power (W) 8.2 198.0 93.6 12.4

Avg BSFC temp mult (20F) 1.32 n/a n/a n/a

Avg BSFC temp mult (75F) 1.20 n/a n/a n/a Gear

Transmission FTP Hwy Combined US06 FTP Hwy US06 FTP Hwy US06

Time in gear 1 30% 2% 17% 13% 1 1.7 2.3 4.2 1421 1710 2155

Time in gear 2 9% 1% 5% 5% 2 3.0 3.9 7.1 2309 2463 2881

Time in gear 3 16% 2% 10% 7% 3 2.4 4.5 6.5 2088 2395 2974

Time in gear 4 27% 6% 18% 8% 4 1.6 3.1 6.7 2160 1978 3209

Time in gear 5 9% 35% 21% 10% 5 2.7 3.7 6.7 2028 1869 2561

Time in gear 6 9% 54% 29% 57% 6 2.3 2.8 4.0 1827 1737 2137

Time in gear 7 0% 0% 0% 0% 7 #DIV/0! #DIV/0! #DIV/0! 0 0 0

Time in gear 8 0% 0% 0% 0% 8 #DIV/0! #DIV/0! #DIV/0! 0 0 0

Avg. η (gear) 87.4% 88.0% 87.7% 87.9%

Avg. η (TC) 88.9% 97.8% 92.9% 95.4% Gear

Avg. η (driveline) 77.7% 86% 81.5% 83.8% FTP Hwy US06 FTP Hwy US06

Battery FTP Hwy Combined US06 1 338 330 256 16% 1% 8%

SOC Avg n/a n/a n/a n/a 2 328 282 255 15% 1% 9%

Std Deviation n/a n/a n/a n/a 3 359 268 264 21% 3% 10%

Max SOC n/a n/a n/a n/a 4 482 298 265 24% 7% 10%

Min SOC n/a n/a n/a n/a 5 361 279 251 12% 42% 16%

Max SOC Swing n/a n/a n/a n/a 6 388 311 279 11% 46% 49%

Battery Efficiency (%) n/a n/a n/a n/a 7 0 0 0 0% 0% 0%

Average Voltage (V) n/a n/a n/a n/a 8 0 0 0 0% 0% 0%

Std Dev Voltage (V) n/a n/a n/a n/a

Battery Energy Change (kWh) 0.00 0.00 0.00 0.00 % of braking energy recovered 0.0% 0.0% 0.0% 0.0%

%batt charge via brake recov #DIV/0! #DIV/0! #DIV/0! #DIV/0! %batt charge via engine #DIV/0! #DIV/0! #DIV/0! #DIV/0!

MG1 FTP Hwy Combined US06 MG1=sun on planetary

Test-Avg Motor Power (hp) n/a n/a n/a n/a

Avg Motor Eff n/a n/a n/a n/a

Avg Generator Eff n/a n/a n/a n/a

Avg Torque-Motor (N-m) n/a n/a n/a n/a

Avg Torque-Generator (N-m) n/a n/a n/a n/a

Avg RPM-Motor n/a n/a n/a n/a

Avg RPM-Generator n/a n/a n/a n/a Mech Energy-Motor (kWh) 0.00 0.00 0.00 0.00 Recovered energy returned to wheels Mech Energy-Gen (kWh) 0.00 0.00 0.00 0.00 Gross recovered braking energy

MG2 FTP Hwy Combined US06 MG2=carrier (tractive)

Avg Motor Power (hp)

Avg Motor Eff n/a n/a n/a n/a

Avg Generator Eff n/a n/a n/a n/a

Avg Torque-Motor (N-m) n/a n/a n/a n/a

Avg Torque-Generator (N-m) n/a n/a n/a n/a

Avg RPM-Motor n/a n/a n/a n/a

Avg RPM-Generator n/a n/a n/a n/a Mech Energy-Motor (kWh) 0.00 0.00 0.00 0.00

Mech Energy-Gen (kWh) 0.00 0.00 0.00 0.00

Round-trip MG efficiency #DIV/0! #DIV/0! #DIV/0! #DIV/0!

Buck/Boost Converter FTP Hwy Combined

Avg Discharge Eff n/a n/a n/a n/a

Avg Charging Eff n/a n/a n/a n/a

Avg Bus Voltage (V) n/a n/a n/a n/a

LHV (fuel) 44 kJ/g

SG (fuel) 0.739

Specific CO2 9087 g/gal

Vehicle Energy Audit (kWh) FTP Hwy Combined US06

Total fuel energy 12.54 8.04 10.52 9.35

Total indicated energy 4.48 3.38 3.98 4.22

Engine pumping energy 0.69 0.57 0.63 0.76

Engine friction energy 0.86 0.48 0.69 0.52

Engine braking energy 0.20 0.03 0.12 0.07 From alt regen braking (extra alternator load) x %

Total accessory energy 0.00 0.04 0.02 0.00

Net brake thermal energy 2.73 2.23 2.50 2.86

Torque converter losses 0.30 0.05 0.19 0.13

Transmission losses 0.31 0.26 0.29 0.33

Battery loop losses 0.00 0.00 0.00 0.00

PE losses 0.00 0.00 0.00 0.00

Losses to MG devices 0.00 0.00 0.00 0.00

Total driveline losses 0.61 0.31 0.47 0.46

Vehicle tractive energy 2.12 1.92 2.03 2.40

Total road load energy 1.29 1.76 1.50 1.75

Foundation braking energy 0.50 0.11 0.32 0.49

Alternator regen decel energy 0.32 0.06 0.20 0.12

Total reqd. braking energy 0.82 0.16 0.53 0.62

Avg BSFC (g/kWh) Total Energy (%)

Powertrain Architecture

Performance Metrics

Shift Optimizer Evaluation Tables

Avg BMEP (bar) Avg RPM
 Technologies Considered in the Agencies’ Analysis

3-52 

Summary statistics were used as a first-order quality check on the model. Sample 

checks included: 

were average engine speed and load within or close to the best BSFC region for 

the vehicle’s engine map? 

was transmission gear distribution reasonable and consistent between engine 

types? 

3.3.1.2.16 Nominal runs 

The Ricardo “nominal” runs refer to the initial set of vehicle simulation models built 

for each vehicle architecture and vehicle class. These runs were used by EPA to assess the 

validity of the detailed model outputs (and hence the models themselves) prior to proceeding 

with the full design of experiment runs. Table 3-11 shows the summary results from the raw 

nominal runs for the conventional stop-start vehicles (including 12V stop-start, 70% efficient 

alternator, shift optimizer and alternator regen, as well as a 3.5% improvement due to engine 

friction reduction). Conventional automatic transmissions are assumed in all nominal runs. 

No road load reductions are included in these results. GHG reductions are in reference to the 

2008 baseline vehicles. 

Table 3-11: Nominal Conventional Stop-Start modeling results 

Vehicle Engine Displ. Torque Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm Type mpg mpg mpg s s Reduction

STDI 0.74 157 AT6 53.2 55.1 54.0 4.0 10.0 20%

LBDI 0.74 157 AT6 55.1 56.0 55.5 4.0 10.0 22%

EGRB 0.74 157 AT6 55.1 57.4 56.1 4.0 10.0 23%

2020 Diesel 1.23 221 AT6 55.8 59.4 57.4 3.7 9.8 16%

STDI 1.04 220 AT8 44.8 54.5 48.7 3.1 8.5 28%

LBDI 1.04 220 AT8 46.6 55.5 50.2 3.1 8.5 31%

EGRB 1.04 220 AT8 46.4 56.7 50.5 3.1 8.5 31%

STDI 1.41 298 AT8 37.1 43.2 39.6 3.0 7.4 31%

LBDI 1.41 298 AT8 38.8 44.0 41.0 3.0 7.4 33%

EGRB 1.41 298 AT8 38.6 44.9 41.2 3.0 7.4 33%

2020 Diesel 2.85 503 AT8 38.2 46.5 41.5 2.9 7.5 27%

STDI 1.13 239 AT8 38.8 42.6 40.4 3.3 8.9 25%

LBDI 1.13 239 AT8 40.3 43.1 41.5 3.3 8.9 27%

EGRB 1.13 239 AT8 40.3 44.4 42.0 3.3 8.9 28%

STDI 1.31 277 AT8 34.8 39.2 36.7 3.2 8.6 31%

LBDI 1.31 277 AT8 36.0 39.8 37.6 3.2 8.6 33%

EGRB 1.31 277 AT8 36.2 40.9 38.2 3.2 8.6 34%

2020 Diesel 2.61 460 AT8 37.3 43.3 39.8 3.0 8.6 30%

STDI 1.94 410 AT8 23.8 26.6 25.0 3.0 8.1 26%

LBDI 1.94 410 AT8 24.6 27.0 25.6 3.0 8.1 28%

EGRB 1.94 410 AT8 24.8 27.7 26.0 3.0 8.1 29%

2020 Diesel 4.28 694 AT8 26.4 30.4 28.1 2.9 8.0 26%

STDI 2.3 486 AT8 16.5 18.3 17.3 3.2 9.8 27%

LBDI 2.3 486 AT8 16.8 18.4 17.5 3.2 9.8 28%

EGRB 2.3 486 AT8 17.2 19.1 18.0 3.2 9.8 30%

2020 Diesel 6.6 895 AT8 19.8 21.5 20.5 2.9 8.8 31%

HD Truck

Small Car

Std Car

Large Car

Small MPV

Large MPV

Truck
 Technologies Considered in the Agencies’ Analysis

3-53 

Table 3-12 shows the results from the nominal runs for the P2 hybrid vehicles. Dualclutch transmissions are assumed in all nominal runs. No road load reductions are included in 

these results. GHG reductions are in reference to the 2008 baseline vehicles. 

Table 3-12: Nominal P2 Hybrid modeling results 

Table 3-13 shows the results from the nominal runs for the input powersplit vehiclesw. 

No road load reductions are included in these results. GHG reductions are in reference to the 

2008 baseline vehicles. 

 

w

 While input powersplit hybrids remain a very likely hybrid architecture choice for some manufacturers, the 

Agencies focused on P2 hybrids compared to powersplits due to their apparent cost-effectiveness advantage in 

future years. As a result the powersplit nominal runs did not receive the same level of engineering scrutiny as 

the P2 hybrid nominal runs. 

Vehicle Engine Displ. Torque EM size Batt size Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm kW kWh Type mpg mpg mpg s s Reduction

STDI 0.59 124 14 0.70 DCT6 68.2 57.3 62.8 3.8 9.6 31%

LBDI 0.59 124 14 0.70 DCT6 68.4 57.7 63.2 3.8 9.6 31%

EGRB 0.59 124 14 0.70 DCT6 70.2 59.9 65.2 3.8 9.6 33%

ATKCS 1.66 138 14 0.70 DCT6 70.8 59.0 64.9 3.7 10.0 33%

ATKDVA 1.66 138 14 0.70 DCT6 71.7 60.5 66.2 3.7 10.0 35%

STDI 0.83 176 24 1.00 DCT8 61.9 57.2 59.7 3.6 8.6 42%

LBDI 0.83 176 24 1.00 DCT8 62.9 58.0 60.6 3.6 8.6 42%

EGRB 0.83 176 24 1.00 DCT8 65.1 59.7 62.5 3.6 8.6 44%

ATKCS 2.4 200 24 1.00 DCT8 64.6 59.7 62.3 3.4 8.6 44%

ATKDVA 2.4 200 24 1.00 DCT8 65.9 61.0 63.6 3.4 8.6 45%

STDI 1.13 238 28 1.10 DCT8 49.8 46.5 48.2 3.4 7.7 43%

LBDI 1.13 238 28 1.10 DCT8 50.4 46.8 48.7 3.4 7.7 44%

EGRB 1.13 238 28 1.10 DCT8 51.7 48.3 50.1 3.4 7.7 45%

ATKCS 3.8 317 28 1.10 DCT8 49.9 46.2 48.1 3.0 7.1 43%

ATKDVA 3.8 317 28 1.10 DCT8 51.1 47.4 49.4 3.0 7.1 44%

STDI 0.9 190 20 1.10 DCT8 50.1 44.2 47.2 3.9 9.4 36%

LBDI 0.9 190 20 1.10 DCT8 50.8 44.5 47.8 3.9 9.4 36%

EGRB 0.9 190 20 1.10 DCT8 52.0 46.1 49.2 3.9 9.4 38%

ATKCS 2.6 217 20 1.10 DCT8 52.9 45.5 49.3 3.7 9.3 38%

ATKDVA 2.6 217 20 1.10 DCT8 54.1 46.8 50.5 3.7 9.3 40%

STDI 1.05 221 25 1.15 DCT8 47.7 42.2 45.0 3.8 9.1 44%

LBDI 1.05 221 25 1.15 DCT8 47.4 42.6 45.1 3.8 9.1 44%

EGRB 1.05 221 25 1.15 DCT8 47.6 43.0 45.4 3.8 9.1 44%

ATKCS 3.15 263 25 1.15 DCT8 48.3 42.4 45.4 3.6 8.8 45%

ATKDVA 3.15 263 25 1.15 DCT8 48.8 43.5 46.2 3.6 8.8 45%

STDI 1.55 327 50 1.50 DCT8 32.5 28.4 30.5 3.3 7.9 39%

LBDI 1.55 327 50 1.50 DCT8 33.0 28.6 30.9 3.3 7.9 40%

EGRB 1.55 327 50 1.50 DCT8 33.8 29.6 31.8 3.3 7.9 42%

ATKCS 4.6 384 50 1.50 DCT8 33.2 29.0 31.2 3.1 7.8 40%

ATKDVA 4.6 384 50 1.50 DCT8 33.9 29.7 31.8 3.1 7.8 42%

Truck

Small Car

Std Car

Large Car

Small MPV

Large MPV
 Technologies Considered in the Agencies’ Analysis

3-54 

Table 3-13: Nominal Powersplit hybrid modeling results 

3.3.1.2.17 Response Surface Model matrix runs 

After the nominal runs were completed according to the agreed-upon methodology, 

Ricardo set up a design of experiment matrix for each vehicle architecture. The continuously 

swept variables were randomized in a Latin hypercube fashion to achieve a representative 

sample within each matrix (reference the Ricardo report for more details on the complex 

systems modeling approach used). After a data review and removal of runs with errorsx

 (as 

needed) Ricardo then generated Response Surface Models (RSM) for use in the complex 

systems tool. EPA used the tool to evaluate a range of potential engine displacements, final 

drive ratios and electric motor sizes (hybrids only) for each vehicle package, in an effort to 

find the combination that would provide the greatest effectiveness while meeting EPA’s 

definition of “equivalent performance”. 

 

x

 e.g., model runs in which the vehicles were underpowered to the point where they could not follow the 

prescribed vehicle speed trace, rendering an invalid test or “error”. These configurations were then excluded 

from the data sets. 

Vehicle Engine Displ. Torque EM size Batt size Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm kW kWh Type mpg mpg mpg s s Reduction

STDI 0.59 124 14 0.70 PS 64.7 57.2 61.1 4.8 10.4 29%

LBDI 0.59 124 14 0.70 PS 65.8 57.4 61.7 4.8 10.4 30%

EGRB 0.59 124 14 0.70 PS 67.7 60.1 64.0 4.8 10.4 32%

ATKCS 1.66 138 14 0.70 PS 64.2 59.5 62.0 4.7 9.8 30%

ATKDVA 1.66 138 14 0.70 PS 67.3 60.0 63.8 4.7 9.8 32%

STDI 0.83 176 80 1.00 PS 55.6 51.7 53.8 3.7 8.7 35%

LBDI 0.83 176 80 1.00 PS 57.9 53.5 55.8 3.7 8.7 38%

EGRB 0.83 176 80 1.00 PS 58.0 54.8 56.5 3.7 8.7 38%

ATKCS 2.4 200 80 1.00 PS 53.3 51.7 52.6 3.6 8.0 34%

ATKDVA 2.4 200 80 1.00 PS 56.4 53.3 55.0 3.6 8.0 37%

STDI 1.13 238 28 1.10 PS 46.6 42.0 44.4 3.2 7.8 38%

LBDI 1.13 238 28 1.10 PS 48.0 41.8 45.0 3.2 7.8 39%

EGRB 1.13 238 28 1.10 PS 47.9 43.6 45.9 3.2 7.8 40%

ATKCS 3.8 317 28 1.10 PS 40.3 38.7 39.6 3.2 7.1 31%

ATKDVA 3.8 317 28 1.10 PS 43.0 40.8 42.0 3.2 7.1 35%

STDI 0.9 190 20 1.10 PS 49.1 42.2 45.8 4.7 10.3 33%

LBDI 0.9 190 20 1.10 PS 50.8 42.7 46.8 4.7 10.3 35%

EGRB 0.9 190 20 1.10 PS 51.3 44.9 48.2 4.7 10.3 37%

ATKCS 2.6 217 20 1.10 PS 44.3 39.6 42.1 4.6 9.1 28%

ATKDVA 2.6 217 20 1.10 PS 49.3 42.3 45.9 4.6 9.1 34%

STDI 1.05 221 25 1.15 PS 44.8 39.3 42.1 4.3 9.7 40%

LBDI 1.05 221 25 1.15 PS 45.7 40.6 43.3 4.3 9.7 42%

EGRB 1.05 221 25 1.15 PS 47.0 41.5 44.4 4.3 9.7 43%

ATKCS 3.15 263 25 1.15 PS 41.7 38.6 40.3 4.2 8.8 37%

ATKDVA 3.15 263 25 1.15 PS 44.3 39.6 42.0 4.2 8.8 40%

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3.3.1.2.18 Equivalent performance definition 

The Ricardo output data provides several performance metrics, as discussed in 

3.3.1.2.10. For simplicity, EPA assumed that a range of acceleration times for both a 0-60 

mph test and also a 0-30 mph test (emphasizing launch character) would provide a simple yet 

representative measure of a vehicle’s equivalent performance. A range was chosen rather 

than assuming a single point value equal to the baseline. This provided more acceptable data 

points and reduced error due to “noise” in the datasets. The acceptable acceleration times 

were as follows with respect to the baseline: 

0-60 mph: 5 percent slower to 15 percent faster as compared to baseline 

0-30 mph: 10 percent slower to 20 percent faster as compared to baseline 

The range above reflects a deviation from the actual baseline value that is well within 

the normal variation of acceleration times for different vehicle models within a given vehicle 

class. 

3.3.1.2.19 Treatment of “turbo lag” in performance runs for turbocharged engines 

A common critique of comparisons of the modeled performance of highly 

turbocharged engines with naturally-aspirated engines is that consideration must be given to 

the delay in producing full engine load associated with the turbocharger, commonly referred 

to as “turbo lag”. In technical discussions, Ricardo’s engine experts assured EPA that the 

dual-sequential designs of the turbocharger systems in the engines in this study should 

mitigate most of this phenomenon often seen on older-model vehicles. However, due to the 

heavy reliance on turbocharged engines as a significant source of motive force for the high 

BMEP engines evaluated in this project, EPA took this sensitivity further into account. 

Ricardo’s initial model of WOT operation was based on a steady-state model of 

engine torque, assuming that the engine would be able to instantaneously reach a desired level 

of output torque, without consideration of the intake manifold filling dynamics or the 

mechanical inertia of the engine. EPA raised this as an issue, more in terms of properly 

representing vehicle performance than for effectiveness differences. EPA reviewed its own 

engine development data and proposed a somewhat conservative time constant for both the 

naturally aspirated engines (0.3 s) and the turbocharged engines (1.5 s), to apply to the engine 

torque response in the vehicle performance runs (these are shown below in Figure 3-7). In 

turn, Ricardo recalculated the acceleration times for the 0-30 and 0-60 mph runs to reflect the 

slower time constants. As a result, EPA used these two performance metrics exclusively in 

determining “equivalent performance”. A transient engine/turbo model would have improved 

the accuracy of the model somewhat; however, it was beyond the scope of this project. 
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Figure 3-7: EPA proposed time constants and resulting effect on torque rise time for turbocharging 

3.3.1.2.20 Treatment of engine response and “turbo lag” in cycle simulations and 

control logic algorithms 

The EASY5 model used in the Ricardo simulations included engine and driveline 

inertia effects which account for some of the real-world transient torque delays. However, the 

simulation modeling did not include an adjustment to account for transient engine response 

delays (e.g. inclusion of time constant offsets), to simulate naturally aspirated and 

turbocharged engine response delays associated with intake manifold gas dynamics and 

turbocharger response delay. Consideration of engine response delay might affect how 

transmission shift optimization control logic and advanced HEV control logic is structured, 

and potentially affect GHG and fuel economy projections, particularly for boosted and 

downsized engines. EPA and Ricardo believe that the impact is small over the city and 

highway fuel economy test cycles. The agencies sought comment on the fuel economy impact 

of transient delays over the test cycles not accounted for in the Ricardo modeling, but there 

were no comments received, so the agencies have made no changes in this respect for the final 

rule analysis. 

3.3.1.2.21 “Equivalent performance” results for conventional stop-start vehicles 

The following tables show the results from the complex systems tool, when 

displacement, final drive ratio and electric motor size are varied to optimize GHG and fuel 

consumption reduction effectiveness at equivalent performance for conventional stop-start, P2 

and powersplit hybrids. Most of the vehicles show little change in performance between the 

nominal runs and the equivalent performance results from the complex systems tool. Table 
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3-14 through Table 3-18 illustrate the various effects of changing road loads on the various 

vehicle package configurations. Table 3-14, Table 3-16, and Table 3-16, respectively, show 

the equivalent performance results for the conventional stop-start (for both automatic 

transmissions and DCTs) and the P2 hybrid vehicles (modeled only as DCTs). No road load 

reductions are included in Table 3-14 through Table 3-16. For comparison, a second set of 

tables (Table 3-17 and Table 3-18) give equivalent performance results for conventional stopstart vehicles and P2 hybrids, each including example road load reductionsy

of 20% mass 

reduction, 20% aerodynamic drag reduction and 10% rolling resistance reduction. 

The package effectiveness results from the equivalent performance runs were used in 

the datasets to calibrate the individual technology effectiveness values within the lumped 

parameter model. The development of the lumped parameter model is described in detail in 

Section 1.5 of EPA’s RIA. 

Table 3-14: Equivalent performance results for conventional-stop start vehicles (no road load reductions) 

 

y

 Note that in the regulatory fleet analysis, levels of road load reduction technologies (e.g., mass reduction) will 

vary by vehicle class. These tables are illustrative in nature. 

Vehicle Engine Displ. Torque Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm Type mpg mpg mpg s s Reduction

STDI 0.86 183 AT6 53.1 56.5 54.6 4.1 9.1 21%

LBDI 0.90 190 AT6 56.3 57.5 56.9 4.1 8.9 24%

EGRB 0.72 154 AT6 55.2 59.1 56.9 4.1 10.1 24%

2020 Diesel 1.19 213 AT6 57.3 64.2 60.2 3.8 10.0 20%

STDI 1.13 240 AT8 44.4 54.5 48.5 2.9 7.9 28%

LBDI 1.26 266 AT8 47.0 56.0 50.6 2.8 7.2 31%

EGRB 1.09 230 AT8 46.2 57.0 50.5 3.1 8.3 31%

STDI 1.48 314 AT8 37.0 43.4 39.6 3.0 7.2 31%

LBDI 1.50 317 AT8 39.2 44.3 41.3 2.9 7.1 34%

EGRB 1.56 330 AT8 38.6 45.0 41.2 3.0 7.0 34%

2020 Diesel 2.57 454 AT8 39.1 47.1 42.3 3.0 8.1 28%

STDI 1.32 280 AT8 38.9 42.4 40.4 3.2 8.0 25%

LBDI 1.41 297 AT8 41.1 43.9 42.3 3.2 7.7 28%

EGRB 1.40 296 AT8 40.0 45.1 42.1 3.2 7.7 28%

STDI 1.57 332 AT8 34.8 39.5 36.8 2.9 7.4 31%

LBDI 1.51 319 AT8 36.2 40.6 38.0 3.0 7.7 34%

EGRB 1.47 312 AT8 36.4 40.9 38.3 2.9 7.6 34%

2020 Diesel 2.74 483 AT8 36.7 44.0 39.7 3.0 8.4 29%

STDI 2.30 486 AT8 24.0 26.8 25.2 2.8 7.0 26%

LBDI 2.06 435 AT8 25.0 26.9 25.8 2.9 7.6 28%

EGRB 2.28 482 AT8 24.8 28.1 26.2 2.9 7.2 29%

2020 Diesel 4.12 669 AT8 26.8 31.2 28.6 2.9 8.3 28%

STDI 2.72 575 AT8 16.6 18.6 17.4 3.0 8.4 27%

LBDI 2.69 568 AT8 17.2 18.8 17.9 2.9 8.4 29%

EGRB 2.71 573 AT8 17.3 19.4 18.2 2.9 8.4 30%

2020 Diesel 5.64 764 AT8 21.0 24.6 22.5 3.2 10.3 37%

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Table 3-15: Equivalent performance results for conventional-stop start vehicles with DCT transmissions 

(no road load reductions) 

Vehicle Engine Displ. Torque Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm Type mpg mpg mpg s s Reduction

STDI 0.91 193 dry DCT6 55.0 58.8 56.7 3.9 8.6 23%

LBDI 0.92 196 dry DCT6 58.0 59.8 58.8 3.9 8.5 26%

EGRB 0.89 188 dry DCT6 57.2 61.3 59.0 3.9 8.7 27%

2020 Diesel 1.13 204 dry DCT6 61.4 69.4 64.8 3.9 10.4 26%

STDI 1.08 229 dry DCT8 46.4 55.0 49.9 3.1 8.0 30%

LBDI 1.29 273 dry DCT8 48.7 57.5 52.3 3.0 7.1 33%

EGRB 1.17 248 dry DCT8 48.1 57.6 51.9 3.0 7.6 33%

STDI 1.53 324 dry DCT8 38.4 44.0 40.7 2.9 6.8 33%

LBDI 1.66 352 dry DCT8 40.5 45.4 42.6 2.9 6.5 36%

EGRB 1.48 313 dry DCT8 40.0 45.6 42.3 3.0 7.0 35%

2020 Diesel 2.44 431 dry DCT8 41.0 48.4 44.0 3.0 8.1 31%

STDI 1.30 276 dry DCT8 40.1 43.6 41.6 3.1 7.7 27%

LBDI 1.32 280 dry DCT8 42.1 44.7 43.2 3.2 7.7 30%

EGRB 1.33 282 dry DCT8 41.7 45.6 43.3 3.1 7.6 30%

STDI 1.53 324 wet DCT8 36.0 40.2 37.8 3.1 7.4 33%

LBDI 1.56 330 wet DCT8 38.0 41.1 39.4 3.0 7.3 36%

EGRB 1.56 330 wet DCT8 37.6 41.8 39.4 3.0 7.3 36%

2020 Diesel 2.42 427 wet DCT8 39.2 45.2 41.7 3.1 9.0 33%

STDI 2.23 472 wet DCT8 24.8 27.1 25.8 3.0 7.1 28%

LBDI 2.26 478 wet DCT8 25.9 27.7 26.7 3.0 7.0 31%

EGRB 2.25 475 wet DCT8 25.8 28.1 26.8 3.0 7.0 31%

2020 Diesel 3.78 613 wet DCT8 28.1 32.1 29.8 3.0 8.6 31%

STDI 2.55 538 wet DCT8 17.3 18.1 17.6 3.1 8.5 28%

LBDI 2.62 554 wet DCT8 17.8 18.7 18.2 3.1 8.4 30%

EGRB 2.58 544 wet DCT8 18.0 19.0 18.4 3.1 8.5 31%

2020 Diesel 5.45 739 wet DCT8 21.8 24.2 22.8 3.3 10.3 38%

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Table 3-16: Equivalent performance results for P2 hybrids (no road load reductions) 

Vehicle Engine Displ. Torque EM size Batt size Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm kW kWh Type mpg mpg mpg s s Reduction

STDI 0.68 144 21 0.70 DCT6 68.9 58.7 63.9 3.7 8.5 32%

LBDI 0.68 144 21 0.70 DCT6 70.1 59.2 64.7 3.7 8.5 33%

EGRB 0.67 142 21 0.70 DCT6 72.0 61.2 66.7 3.7 8.5 35%

ATKCS 1.72 143 17 0.70 DCT6 72.0 60.8 66.5 3.9 9.6 35%

ATKDVA 1.68 140 19 0.70 DCT6 74.4 62.0 68.2 3.8 9.6 36%

STDI 1.00 213 26 1.00 DCT8 62.2 57.7 60.1 3.4 7.9 42%

LBDI 0.95 202 27 1.00 DCT8 63.2 58.3 60.9 3.4 8.0 43%

EGRB 1.04 219 26 1.00 DCT8 64.8 60.4 62.7 3.4 7.8 44%

ATKCS 2.54 212 27 1.00 DCT8 64.6 59.5 62.2 3.4 8.6 44%

ATKDVA 2.31 193 28 1.00 DCT8 65.7 60.7 63.4 3.4 8.7 45%

STDI 1.39 292 29 1.10 DCT8 50.6 47.3 49.1 3.3 7.2 44%

LBDI 1.37 289 29 1.10 DCT8 51.3 47.9 49.7 3.4 7.3 45%

EGRB 1.38 291 29 1.10 DCT8 52.6 49.0 50.9 3.4 7.2 46%

ATKCS 3.73 311 30 1.10 DCT8 48.6 46.1 47.5 3.2 7.5 42%

ATKDVA 3.33 278 30 1.10 DCT8 50.7 47.7 49.3 3.3 8.0 44%

STDI 1.40 295 34 1.10 DCT8 52.3 45.5 49.0 3.6 8.1 38%

LBDI 1.39 293 37 1.10 DCT8 53.0 45.9 49.6 3.5 8.0 39%

EGRB 1.41 297 38 1.10 DCT8 54.4 47.2 50.9 3.4 7.9 40%

ATKCS 3.87 322 38 1.10 DCT8 53.6 46.2 50.0 3.6 9.0 39%

ATKDVA 3.59 299 39 1.10 DCT8 55.2 47.4 51.4 3.7 9.3 41%

STDI 1.31 276 30 1.15 DCT8 48.5 42.3 45.5 3.2 7.4 45%

LBDI 1.30 274 31 1.15 DCT8 49.0 42.6 45.9 3.2 7.4 45%

EGRB 1.29 272 32 1.15 DCT8 49.2 42.7 46.0 3.2 7.5 45%

ATKCS 3.13 262 34 1.15 DCT8 48.0 42.3 45.3 3.2 8.2 44%

ATKDVA 3.00 250 34 1.15 DCT8 48.5 43.0 45.9 3.2 8.3 45%

STDI 1.87 394 50 1.50 DCT8 33.3 29.0 31.2 3.3 7.3 40%

LBDI 1.92 404 48 1.50 DCT8 33.6 29.3 31.5 3.4 7.2 41%

EGRB 1.92 405 48 1.50 DCT8 34.6 30.2 32.4 3.3 7.2 43%

ATKCS 5.34 445 53 1.50 DCT8 32.3 28.8 30.6 3.1 7.2 39%

ATKDVA 5.34 445 56 1.50 DCT8 32.7 29.4 31.1 3.0 7.1 40%

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Table 3-17 Equivalent performance results for conventional-stop start vehicles (with 20% mass, 20% 

aerodynamic drag and 10% rolling resistance reductions) 

Vehicle Engine Displ. Torque Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm Type mpg mpg mpg s s Reduction

STDI 0.68 145 AT6 65.0 70.0 67.2 4.1 9.2 35%

LBDI 0.89 189 AT6 68.9 72.4 70.4 4.2 8.4 38%

EGRB 0.69 146 AT6 67.6 73.1 70.0 4.1 9.2 38%

2020 Diesel 0.91 164 AT6 71.8 83.2 76.5 3.7 10.4 37%

STDI 1.04 220 AT8 53.9 67.6 59.3 2.9 7.2 41%

LBDI 1.27 268 AT8 57.3 70.6 62.6 2.8 6.4 44%

EGRB 0.98 207 AT8 56.2 70.1 61.7 3.0 7.6 43%

STDI 1.00 212 AT8 46.5 53.8 49.5 3.1 8.1 45%

LBDI 1.49 315 AT8 48.4 55.0 51.2 3.0 6.5 46%

EGRB 1.00 212 AT8 48.5 55.9 51.6 3.1 8.1 47%

2020 Diesel 2.05 362 AT8 48.5 59.7 53.0 3.0 8.1 42%

STDI 1.20 253 AT8 46.3 51.8 48.6 3.2 7.4 37%

LBDI 1.40 296 AT8 49.1 53.5 51.0 3.3 6.9 40%

EGRB 1.13 238 AT8 48.4 53.6 50.6 3.2 7.7 40%

STDI 1.00 212 AT8 42.4 46.8 44.3 3.2 8.8 43%

LBDI 1.26 266 AT8 44.2 48.1 45.9 2.9 7.3 45%

EGRB 1.02 216 AT8 44.2 48.7 46.2 3.2 8.7 45%

2020 Diesel 1.98 349 AT8 46.4 54.0 49.6 3.0 9.0 43%

STDI 1.44 303 AT8 29.4 32.1 30.6 3.1 8.6 39%

LBDI 1.89 399 AT8 30.2 32.9 31.3 2.8 7.0 41%

EGRB 1.44 305 AT8 30.5 33.6 31.8 3.1 8.6 42%

2020 Diesel 3.20 518 AT8 32.8 38.8 35.3 3.0 8.6 41%

STDI 2.21 466 AT8 20.0 22.2 20.9 3.0 8.4 39%

LBDI 2.24 473 AT8 20.5 22.6 21.4 3.0 8.4 41%

EGRB 2.19 463 AT8 20.9 23.1 21.8 3.0 8.4 42%

2020 Diesel 4.45 603 AT8 25.3 30.1 27.3 3.2 10.3 48%

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Table 3-18: Equivalent performance results for P2 hybrids (with 20% mass, 20% aerodynamic drag and 

10% rolling resistance reductions) 

3.3.1.2.22 Validation of vehicle simulation results 

Ricardo described the process used to validate the baseline vehicles in its report1. 

Ideally it would be desirable to validate the simulation results with actual vehicle certification 

test data. However, due to the nature and intended time frame (10+ years into the future) of 

the technologies modeled within the vehicle classes, it is difficult to find many real-world 

examples of specific technologies at the level of development reflected within the latest 

simulation models. Furthermore, there are no current vehicles in production that contain all 

(or even a majority) of the multiple advanced technologies embedded within the models so it 

is difficult to make meaningful direct comparisons between actual vehicles and model results. 

Finally, there is no direct way to disaggregate the various advanced technologies and isolate 

only the relevant pieces for evaluation (e.g., an advanced turbocharged engine at an interim 

BMEP level with a baseline-level transmission without stop-start): the lumped parameter 

model was developed for this very analytical capability. A full description of the lumped 

parameter model (including example comparisons of existing vehicle models to lumped 

parameter estimates) is provided in 3.3.2.

Vehicle Engine Displ. Torque EM size Batt size Trans FTP HW Comb 0-30mph 0-60mph % GHG

Class Type L Nm kW kWh Type mpg mpg mpg s s Reduction

STDI 0.68 143 11 0.70 DCT6 85.8 72.2 79.1 3.7 7.9 45%

LBDI 0.68 144 11 0.70 DCT6 87.6 73.1 80.4 3.7 7.9 46%

EGRB 0.68 143 11 0.70 DCT6 89.5 75.4 82.5 3.7 8.0 47%

ATKCS 1.60 133 11 0.70 DCT6 89.4 74.9 82.2 3.8 8.9 47%

ATKDVA 1.52 127 11 0.70 DCT6 93.9 76.9 85.4 3.8 9.0 49%

STDI 0.90 191 18 1.00 DCT8 78.1 71.1 74.8 3.2 7.2 53%

LBDI 0.91 194 18 1.00 DCT8 79.7 72.2 76.2 3.3 7.2 54%

EGRB 0.92 194 18 1.00 DCT8 81.4 74.2 78.0 3.2 7.1 55%

ATKCS 2.36 197 18 1.00 DCT8 82.2 73.8 78.2 3.1 7.5 55%

ATKDVA 2.03 169 18 1.00 DCT8 83.5 76.2 80.0 3.3 8.3 56%

STDI 1.21 254 22 1.10 DCT8 63.2 57.3 60.4 3.1 6.6 55%

LBDI 1.25 263 21 1.10 DCT8 64.9 58.5 61.9 3.1 6.5 56%

EGRB 1.25 263 21 1.10 DCT8 65.7 59.8 62.9 3.1 6.6 56%

ATKCS 3.52 293 21 1.10 DCT8 61.1 57.0 59.2 3.0 6.7 54%

ATKDVA 3.29 274 21 1.10 DCT8 63.9 59.3 61.7 3.0 6.8 56%

STDI 1.25 265 21 1.10 DCT8 63.9 53.4 58.7 3.5 7.7 48%

LBDI 1.22 257 22 1.10 DCT8 65.2 53.9 59.5 3.5 7.7 49%

EGRB 1.24 262 21 1.10 DCT8 66.5 55.7 61.1 3.5 7.8 50%

ATKCS 3.71 309 21 1.10 DCT8 65.0 55.1 60.1 3.4 8.2 49%

ATKDVA 3.44 287 21 1.10 DCT8 67.5 56.7 62.1 3.6 8.7 51%

STDI 1.01 213 28 1.15 DCT8 59.5 50.2 54.9 3.2 7.4 54%

LBDI 1.04 219 28 1.15 DCT8 61.0 50.9 56.0 3.2 7.3 55%

EGRB 1.02 215 26 1.15 DCT8 60.6 51.6 56.2 3.2 7.3 55%

ATKCS 2.91 243 21 1.15 DCT8 58.9 51.1 55.1 3.2 7.5 54%

ATKDVA 2.84 237 22 1.15 DCT8 60.1 52.4 56.3 3.2 7.7 55%

STDI 1.57 330 41 1.50 DCT8 39.4 34.4 37.0 3.2 7.0 50%

LBDI 1.60 337 38 1.50 DCT8 40.3 35.0 37.7 3.3 7.0 51%

EGRB 1.58 334 40 1.50 DCT8 41.0 36.0 38.6 3.2 7.0 52%

ATKCS 4.16 347 38 1.50 DCT8 39.9 34.9 37.5 3.0 7.1 50%

ATKDVA 4.15 346 39 1.50 DCT8 41.4 35.9 38.7 3.0 7.2 52%

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3.3.1.2.23 The “efficient frontier” capability in Complex Systems tool 

A powerful feature of the Complex Systems tool is the “efficient frontier” function, 

which provides a graphical representation of the RSM data for the vehicle configuration of 

interest. The user can identify the combination of various attributes (engine displacement, 

final drive ratio, motor size, etc) that project the best model effectiveness. Figure 3-8 below is 

an example of the efficient frontier for a Standard Car with a cooled EGR turbocharged 

engine and a dry clutch DCT. The light red line along the top of the data set represents the 

best fuel economy at each 0-60 mph acceleration time within the desired window. The solid 

dark blue points represent the combinations that achieve both the desired 0-60 and 0-30 mph 

criteria for equivalent performance. In this way, it is easy to quantify the best effectiveness 

for a given technology package. 

Figure 3-8: “Efficient Frontier” function in complex systems tool 

3.3.1.2.24 Significance of the Complex Systems tool 

The complex systems tool was used not only to identify the optimal combination of 

input variables for each vehicle architecture, but also to analyze trends in the input variables 

for quality assurance (i.e., to make sure the response surface models made engineering sense), 

and to establish numerical relationships between these variables for the lumped parameter 

Acceptable 0-60 mph time window

3
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model calibration. Shown below are a few examples of the types of inquiries made via the 

complex systems tool: 

3.3.1.2.24.1 Effects of motor size (HEVs) 

EPA reviewed the effects of motor size on hybrids. As motor size is increased, there 

is more opportunity to recapture energy during braking (because more powerful motors can 

recover all of the energy in more severe braking events). However, oversized motors also 

experience reduced efficiency as they operate in a less efficient operating region. This is 

shown in Figure 3-9 below, which shows a sweep of motor size vs. fuel economy for both the 

FTP/HWFE combined and also the high speed/load US06 cycle. Note that the optimum 

motor size increases with respect to the US06 cycle due to more severe braking and 

acceleration rates. 

Figure 3-9: Electric motor sweeps for Standard Car class, P2 hybrid with stoichiometric GDI engine (left 

= FTP/HWFE test; right = US06 test) 

3.3.1.2.24.2 Effects of engine displacement 

EPA reviewed the effects of engine displacement at equivalent performance to 

determine if there would be an “optimal” range of downsizing for best effectiveness. 

Surprisingly, there was little benefit beyond downsizing the engine past a minimal point. 

Shown in Figure 3-10 is an example complex systems tool graph with fuel economy plotted 

against engine displacement multiplier (compared to the “nominal” engine displacement) for 

the Truck class for three gasoline turbocharged engine packages and one diesel engine 

package (note all packages included 20% weight reduction, 20% aerodynamic drag reduction, 

and 10% rolling resistance reduction): 

The diesel engine result shows that the nominal engine in this case was originally 

oversized because it was scaled on engine power not more accurately on engine 

torque and continued displacement reduction would improve fuel economy. For 

this package, the displacement for optimal fuel economy is smaller than 50% of 
 Technologies Considered in the Agencies’ Analysis

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the nominal value; however, when considering equivalent vehicle performance, the 

minimum diesel displacement increases to roughly 70% of the nominal value. 

In contrast, the gasoline turbo engine results shown reflect a relative insensitivity 

of displacement to fuel economy for these advanced vehicles. 

Figure 3-10: Example displacement sweep for Truck class in complex systems tool 

Figure 3-10 shows that as modeled, the swept displacement range is not large enough 

for the advanced gasoline turbocharged engines. The displacement multiplier for these 

engines must be greater than 1.3x the nominal displacement before the fuel economy would 

degrade substantially. As the displacement drops below about 65% of the nominal (already 

downsized) value, the efficiency decreases, as the engine load must be much higher to provide 

the same required power. Regardless, the total fuel efficiency decrease from optimal is rather 

small compared to today’s engines. A 27-bar cooled EGR turbocharged GDI engine map for 

Diesel 8AT

STDI-8AT

STDI-DCT

EGRB-8AT Expected trend
 Technologies Considered in the Agencies’ Analysis

3-65 

a large carz was reverse-engineered from the Ricardo 10 Hz output data, and is provided in 

Figure 3-11. The efficiency of this family of engines is very robust to changes in engine 

displacement because the highlighted BSFC region of interest (the second one out from the 

minimum BSFC “island”) spans a large speed and load range. As a result, significant changes 

in displacement do not greatly reduce fuel efficiency. As displacement increases, the average 

operating points for the engine over a given test cycle will trend towards the lower left (lower 

speed, lower loadaa) portion of the map. In this case the points on the plot exist within the 

same BSFC contour, so there is little degradation in engine efficiency with increasing 

displacement (and drivetrain efficiency may improve at higher gears, potentially resulting in a 

fuel economy increase). Were the displacement to be increased much further, the operating 

region would cross the contour and fuel efficiency would begin to drop much more 

dramatically. 

 

z

 The 27 bar, cooled EGR turbocharged engine maps are similar for all classes as they originated from a common 

reference map and scaled according to engine displacement, as described in Section 6.3 of the 2011 Ricardo 

report. 

aa Load decreases as it is reflective of a % of the maximum achievable torque and torque is increasing with 

increased displacement. Speed decreases because of the greater torque available combined with the shift 

optimizer algorithm (allowing for a greater propensity to operate in higher gears). 
 Technologies Considered in the Agencies’ Analysis

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Figure 3-11: Advanced engine BSFC map (27-bar cooled EGR turbocharged GDI engine for large car) 

3.3.1.2.25 Effects of mass reduction 

With the complex systems tool EPA isolated the effectiveness of mass reduction on 

advanced vehicle technology packages. Figure 3-12 below shows a mass reduction sweep 

plot of the Large MPV class for a conventional STDI and P2 hybrid vehicle with an Atkinson 

engine. 
 Technologies Considered in the Agencies’ Analysis

3-67 

Figure 3-12: Mass reduction sweep for Large MPV class at baseline equivalent performance. Engine 

displacement and motor size (hybrids) held constant. 

The mass reduction effectiveness, originally estimated at roughly 6% GHG reduction 

for a 10% reduction in mass, has been revised to reflect data such as that shown above. 

Isolated from benefits due to engine downsizing opportunities, the effectiveness of weight 

reduction for the non-hybrid packages is on the order of 5% per 10% weight reduction, while 

mass reduction for the P2 hybrid (or any hybrid) is reduced, on the order of 4.5% per 10% 

reduction due to the synergies with brake energy recovery (less braking energy is recoverable 

because the vehicle weighs less). The lumped parameter tool was also revised to incorporate 

the synergies of weight reduction and hybrids. 

3.3.1.2.26 Vehicle simulation report peer review process 

As previously discussed, vehicle simulation modeling is a very detailed, 

mathematically intensive approach which relies heavily on numerical engineering inputs. 

These inputs (e.g., engine maps, transmission efficiency, control logic, etc.) are the heart of 

the model and are derived directly from proprietary engineering knowledge of components 

and subsystems. To simulate advanced engine and vehicle concepts, state-of-the-art 

knowledge must be applied and converted into modeling inputs. Public domain information 

is rarely at the forefront of technology, and of little use in modeling vehicles in the MYs 

2017-2025 time frame. 

Engineering details on advanced vehicle technologies are closely guarded in industry, 

and engineering services companies which develop and generate this confidential information 

rely on it to remain competitive in the marketplace. Therefore, it is difficult, if not 

≈4.6% per 10% WR

≈5.2% per 10% WR

Atkinson-P2 hybrid

Stoich GDI engine
 Technologies Considered in the Agencies’ Analysis

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impossible, to be completely transparent with an advanced vehicle simulation model and 

make all of the inputs available for public review. EPA commissioned an external peer 

review of the 2011 Ricardo simulation project and report. The peer reviewers selected were 

highly respected members of academia and industry, all with substantial backgrounds in 

automotive technology. The list of peer reviewers and their credentials is provided in the 

associated peer review report35.

EPA charged the peer reviewers to thoroughly evaluate the body of work with respect 

to the following topics: 

Adequacy of the numerical inputs (engine technology selection, battery inputs, 

accessory load assumptions, etc.) and highlight any caveats or limitations that 

would affect the final results. 

Validity and applicability of the simulation methodology, and if it adequately 

addresses synergies 

The results, and their validity and applicability to the light-duty vehicle fleet in the 

2020-2025 timeframe. 

Completeness of the report (does it offer enough detail of the modeling process) 

The overall adequacy of the report for predicting the effectiveness of these 

technologies, and suggest recommendations for improvement 

The first round of comments was reflective of the reviewer’s lack of access to model 

inputs. Because the confidential inputs were initially withheld (for reasons described above), 

“lack of transparency” was a consistent theme amongst the reviewers, so much that they 

expressed frustration with their ability to evaluate the model methodology and the quality of 

the inputs. Additionally, due to the lack of access to Ricardo proprietary input data the peer 

reviewers expressed concern that they could not adequately judge the validity or accuracy of 

the input information or the simulation results. EPA worked with Ricardo to provide the peer 

reviewers with access to all of the detailed confidential modeling inputs under non-disclosure 

agreements. With this necessary information, 3 of the 5 peer reviewers submitted a second 

round of comments which were generally more specific. In turn, Ricardo modified the report 

to address some of the comments, and they developed a response to comments document 

which covered the comments from the peer review. One common theme called for increased 

detail in how the inputs were generated. To address these requests, Ricardo provided the 

detailed case studies that were used in the development of the engine maps for the cooled 

EGR boosted engines and the Atkinson engines for hybrids. Ricardo also elaborated on the 

hybrid control strategy, complete with state flow diagrams of operating modes, as well as a 

discussion of how hybrid control strategy was optimized. Additional transmission input 

details were provided, including an overview of the development of advanced gear 

efficiencies and how the optimized shift strategy was applied. 

The docket to this final rule contains Ricardo’s response to comment document (which 

includes the first version of the Ricardo report that was peer reviewed and both rounds of peer 

review comments), and Ricardo’s final report.36,37

 The agencies sought comment on the all of 

these references and on the responsiveness of the final report to the peer review comments. 
 Technologies Considered in the Agencies’ Analysis

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3.3.1.3 Argonne National Laboratory Simulation Study 

As discussed in the proposal, the U.S. D.O.T. Volpe Center has entered into a contract 

with Argonne National Laboratory (ANL) to provide full vehicle simulation modeling support 

for this MYs 2017-2025 rulemaking. While modeling was not complete in time for use in the 

NPRM, the ANL results were available for the final rule and were used to define the 

effectiveness of mild hybrids for both agencies, and NHTSA used the results to update the 

effectiveness of advanced transmission technologies coupled with naturally-aspirated engines 

for the CAFE analysis, as discussed above and more fully in NHTSA’s RIA. This simulation 

modeling was accomplished using ANL’s full vehicle simulation tool called “Autonomie,” 

which is the successor to ANL’s Powertrain System Analysis Toolkit (PSAT) simulation tool, 

and that includes sophisticated models for advanced vehicle technologies. The ANL 

simulation modeling process and results are documented in multiple reports that can be found 

in NHTSA’s docket38

. 

3.3.2 Lumped parameter Modeling 

3.3.2.1 Overview of the lumped parameter model 

As a more practical alternative to full vehicle simulation, EPA developed a “lumped 

parameter model” that estimates the effectiveness of various technology combinations or 

“packages,” in a manner that accounts for synergies between technologies. In the analysis 

supporting the MYs 2012-2016 light duty vehicle GHG and CAFE rule, EPA built over 140 

packages for use in its OMEGA model, which spanned 19 vehicle classes and over 1100 

vehicle models. Vehicle simulation modeling performed for EPA by Ricardo, PLC, was used 

to calibrate the lumped parameter model. Although DOT’s analysis supporting the MYs 

2012-2016 CAFE rule applied technologies incrementally, rather than specifying packages in 

advance, DOT calibrated CAFE model inputs, using EPA’s lumped parameter model, to 

harmonize as fully as practical with estimates produced by EPA’s lumped parameter model. 

To support this rulemaking, EPA has updated its lumped parameter model and 

calibrated it with updated vehicle simulation work performed for EPA by Ricardo, PLC. As 

in the MYs 2012-2016 rulemaking, DOT has calibrated inputs, including synergy factors, to 

the CAFE model to as fully as practical align with estimates produced by EPA’s lumped 

parameter model. 

Both agencies have continued to conduct and sponsor vehicle simulation efforts to 

improve inputs to the agencies’ respective modeling systems. For the final rule, simulation 

results for the mild hybrid technology have been incorporated into the modeling systems for 

both agencies. Also, NHTSA updated the incremental effectiveness of advanced 

transmissions as applied to naturally-aspirated engines, a change which was only 

implemented in the CAFE model. 

The basis for EPA’s lumped parameter analysis is a first-principles energy balance 

that estimates the manner in which the chemical energy of the fuel is converted into various 

forms of thermal and mechanical energy on the vehicle. The analysis accounts for the 
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dissipation of energy into the different categories of energy losses, including each of the 

following: 

Second law losses (thermodynamic losses inherent in the combustion of fuel), 

Heat lost from the combustion process to the exhaust and coolant, 

Pumping losses, i.e., work performed by the engine during the intake and exhaust 

strokes, 

Friction losses in the engine, 

Transmission losses, associated with friction and other parasitic losses of the 

gearbox, torque converter (when applicable) and driveline 

Accessory losses, related directly to the parasitics associated with the engine 

accessories, 

Vehicle road load (tire and aerodynamic) losses; 

Inertial losses (energy dissipated as heat in the brakes) 

The remaining energy is available to propel the vehicle. It is assumed that the baseline 

vehicle has a fixed percentage of fuel lost to each category. Each technology is grouped into 

the major types of engine loss categories it reduces. In this way, interactions between 

multiple technologies that are applied to the vehicle may be determined. When a technology is 

applied, the lumped parameter model estimates its effects by modifying the appropriate loss 

categories by a given percentage. Then, each subsequent technology that reduces the losses in 

an already improved category has less of a potential impact than it would if applied on its 

own. 

Using a lumped parameter approach for calculating package effectiveness provides 

necessary grounding to physical principles. Due to the mathematical structure of the model, it 

naturally limits the maximum effectiveness achievable for a family of similar technologiesbb

. 

This can prove useful when computer-simulated packages are compared to a “theoretical 

limit” as a plausibility check. Additionally, the reduction of certain energy loss categories 

directly impacts the effects on others. For example, as mass is reduced the benefits of brake 

energy recovery decreases because there is not as much inertia energy to recapture. 

Figure 3-13 is an example spreadsheet used by EPA to estimate the package effectiveness and 

the synergistic impacts of a technology package for a standard-size car. 

 

bb For example, if only 4% of fuel energy is lost (in a baseline engine) to pumping work, leveraging multiple 

technologies to theoretically eliminate all pumping losses would yield an aggregate reduction of no more than 

15% in fuel consumption. 
 Technologies Considered in the Agencies’ Analysis

3-71 

Figure 3-13 Sample lumped parameter model spreadsheet 

The LP model has been updated from the MYs 2012-2016 final rule to support the 

MYs 2017-2025 final standards. Changes were made to include new technologies for 2017 

and beyond, improve fidelity for baseline attributes and technologies, and better represent 

hybrids based on more comprehensive vehicle simulation modeling. EPA RIA Chapter 1 

provides details of the methodology used to update and refine the model. 

3.3.2.2 Calibration of Lumped Parameter model to vehicle simulation data 

The lumped parameter model includes a majority of the new technologies being 

considered as part of this proposed rulemaking. The results from the Ricardo vehicle 

simulation project (See section 3.3.1 for additional information) were used to successfully 

calibrate the predictive accuracy and the synergy calculations that occur within the lumped 
 Technologies Considered in the Agencies’ Analysis

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parameter model. When the vehicle packages Ricardo modeled are estimated in the lumped 

parameter model, the results are comparable. All of the baselines for each vehicle class, as 

predicted by the lumped parameter model, fall within 3% of the Ricardo-modeled baseline 

results. With a few exceptions (discussed in Chapter 1 of EPA’s RIA the lumped parameter 

results for the 2020-2025 “nominal” technology packages are within 5% of the vehicle 

simulation results. Shown below in Figure 3-14 through Figure 3-19 are Ricardo’s vehicle 

simulation package results (for conventional stop-start and P2 hybrid packagescc) compared to 

the lumped parameter estimates. 

Figure 3-14: Comparison of LP to simulation results for Small Car class 

 

cc Refer to 3.3.1for definitions of the baselines, “conventional stop-start” and “P2 hybrid” vehicle architectures. 

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0 mpg

Small Car Nominal Results

Ricardo

LP results

Conventional SS P2 Hybrid
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3-73 

Figure 3-15: Comparison of LP to simulation results for Standard Car class 

Figure 3-16: Comparison of LP to simulation results for Large Car class 

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0 mpg

Standard Car Nominal Results

Ricardo

LP results

Conventional SS P2 Hybrid

0

1 0

2 0

3 0

4 0

5 0

6 0 mpg

Large Car Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid
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3-74 

Figure 3-17: Comparison of LP to simulation results for Small MPV class 

Figure 3-18: Comparison of LP to simulation results for Large MPV class 

0

1 0

2 0

3 0

4 0

5 0

6 0 mpg

Small MPV Nominal Results

Ricardo

LP results

ConventionalSS P2 Hybrid

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

5 0 mpg

Large MPV Nominal Results

Ricardo

LP results

Conventional SS P2 Hybrid
 Technologies Considered in the Agencies’ Analysis

3-75 

Figure 3-19: Comparison of LP to simulation results for Truck class 

The recent ANL modeling results for mild hybrids largely confirmed the effectiveness 

as originally predicted by the lumped parameter model, with minor differences for small cars 

and large trucks. A comparison of the ANL results to the original lumped parameter results 

(for comparable vehicle classes when modeled with a nominal 15 kW motor size) is shown 

below in Table 3-19 and Table 3-20. 

Table 3-19 ANL Effectiveness for Mild Hybrid 

Compact Midsize Small SUV Midsize SUV Pickup

FC reduction 11.6% 11.6% 10.2% 10.5% 8.5%

Table 3-20 Lumped Parameter Model Effectiveness for Mild Hybrid 

Small Car Std Car Small MPV Large MPV Truck

FC reduction 14.1% 11.8% 10.1% 10.1% 6.9%

The underlying structure of the lumped parameter model was not changed to 

accommodate this new information; instead, the nominal 15 kW motor sizes for small cars 

0

5

1 0

1 5

2 0

2 5

3 0

3 5 mpg

Truck Nominal Results

Ricardo

LP results

Conventional SS P2 Hybrid
 Technologies Considered in the Agencies’ Analysis

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and pickup truck mild hybrids were adjusted (to 10 kW and 18 kW, respectively) to reflect the 

updated effectiveness results provided by the ANL simulation work. 

3.3.2.3 Comparison of results to real-world examples 

To validate the lumped parameter model, representations of actual late-model 

production vehicles exhibiting advanced technologies were created. Shown in Table 3-21 are 

a set of select vehicle models containing a diverse array of technologies: included are the 

pertinent technologies and vehicle specifications, along with actual vehicle certification fuel 

economy test data compared to the lumped parameter fuel economy estimates. For the 

vehicles and technologies shown, the predicted fuel economy is within about 3% of the actual 

data. 

Table 3-21: Production vehicle certification data compared to lumped parameter predictions 

Vehicle 2011 Chevy Cruze 

ECO

2011 Sonata Hybrid 2011 Escape Hybrid 2011 F-150 

EcoBoost

Vehicle Class Small Car Standard Car Small MPV Truck

Engine 1.4L I4 Turbo GDI 2.4L I4 Atkinson 2.5L I4 Atkinson 3.5L V6 Turbo GDI

Transmission 6 speed auto 6 speed auto CVT 6 speed auto

HEV motor (kW) n/a 30 67 n/a

ETW (lbs) 3375 3750 4000 6000

City/HW FE (mpg) 40.3 52.2 43.9 22.6

LP estimate (mpg) 40.2 51.7 44.0 21.9

Key technologies

applied in LP model

GDI (stoich)

Turbo (30% 

downsize)

Ultra low R tires

Active grill shutters

P2 hybrid

Aero improvements

Powersplit hybrid GDI (stoich)

Turbo (37% 

downsize)

3.4 What cost and effectiveness estimates have the agencies used for each technology? 

As discussed in the previous sections, many the effectiveness estimates for this final 

rule, consistent with the proposal, including the estimates for the technologies carried over 

from the MYs 2012-2016 final rule, are derived from the 2011 Ricardo study and 

corresponding updated version of the lumped-parameter model. It is important to note that 

the agencies used the average of the range presented when referencing the effectiveness 
 Technologies Considered in the Agencies’ Analysis

3-77 

estimates from the MYs 2012-2016 final rule. If, for example, the effectiveness range for 

technology X was determined to be 1 to 2 percent, the agencies used a value of 1.5 percent in 

their respective analyses. However, the effectiveness ranges that are presented for the MYs 

2017-2025 analysis, as informed by the Ricardo 2011 study, define the range of estimates 

used by the agencies for the different vehicle types. Again using technology X as an example, 

if the range is now defined as 2.0 to 2.5 percent then for small passenger cars (subcompact or 

compact) the estimated effectiveness might be 2.0 percent but for large cars an estimate of 2.5 

percent might be used. 

As noted in section 3.1.3, the effects of learning on individual technology costs can be 

seen in the cost tables presented throughout this section 3.3. For each technology, we show 

direct manufacturing costs for the years 2017 through 2025. The changes shown in the direct 

manufacturing costs from year-to-year reflect the cost changes due to learning effects. 

3.4.1 Engine technologies 

As indicated in the cost tables that found in this section, the agencies updated the 

costing approach for some technologies in an effort to provide better granularity in our 

estimates. This is reflected in Table 3-23, among others, listing costs for technologies by 

engine configuration—in-line or “I” versus “V”—and/or by number of cylinders. In the MYs

2012-2016 final rule, we showed costs for identified vehicle classes such as small car, large 

car, large truck, etc. The identified challenges inherent with that approach are that different 

vehicle classes can have many different sized engines. This condition may become more 

prominent going forward as more turbocharged and downsized engines enter the fleet. For 

example, the agencies project that many vehicles in the large car class, have large 

displacement V8 or V6 engines would move to highly turbocharged I4 engines under the final 

rule, consistent with the proposal. As such, we would not want to estimate the costs of engine 

friction reduction for large cars—which have always and continue to be based on the number 

of cylinders—by assuming that all large cars have V8 or V6 engines. 

3.4.1.1 Low Friction Lubricants 

A basic method of reducing fuel consumption in gasoline engines is using of lower 

viscosity engine lubricants. Advanced multi-viscosity engine oils are available today which 

yield improved performance in a wider temperature band and with better lubricating 

properties. These advances are accomplished by changes to the oil base stock (e.g., switching 

engine lubricants from a Group I base oils to lower-friction, lower viscosity Group III 

synthetic) and through changes to lubricant additive packages (e.g., friction modifiers and 

viscosity improvers). The use of 5W-30 motor oil is now widespread and auto manufacturers 

are introducing the use of lower viscosity oils, such as 5W-20 and 0W-20, to improve coldflow properties and reduce cold start friction. However, in some cases, changes to the 

crankshaft, connecting rod and main crankshaft bearing designs and/or materials along with 

the mechanical tolerances of engine components may be required. In all cases, durability 

testing would be required to ensure that durability is not compromised. Shifting to lower 

viscosity and lower friction lubricants can also improve the management of valvetrain 

technologies such as cylinder deactivation or variable valve timing, which rely on a minimum 

oil temperature (viscosity) for operation. 
 Technologies Considered in the Agencies’ Analysis

3-78 

Several manufacturers have previously commented confidentially that low friction 

lubricants could have an effectiveness value between 0 to 1 percent. The agencies used the 

average effectiveness of 0.5 in the MYs 2012-2016 final rule. For purposes of this final rule, 

consistent with the proposal, the agencies relied on the lumped parameter model and the range 

for the effectiveness of low friction lubricant is 0.5 to 0.8 percent. 

In the MYs 2012-2016 final rule, the 2010 TAR and the MYs 2014-2018 Medium and 

Heavy Duty GHG and Fuel Efficiency final rule, EPA and NHTSA used a direct 

manufacturing cost (DMC) of $3 (2007$) and considered that cost to be independent of 

vehicle class since the engineering work required should apply to any engine size. The 

agencies continue to believe that this cost is appropriate and, having adjusted for 2010$, the 

cost remains the same for this analysis. No learning is applied to this technology so the DMC 

remains $3 (2010$) year-over-year. The agencies have used a low complexity ICM of 1.24 

for this technology through 2018 and 1.19 thereafter. The resultant costs are shown in Table 

3-22.

dd

 Note that low friction lubes are expected to exceed 85 percent penetration by the 2017 

MY. 

Table 3-22 Costs for Engine Modifications to Accommodate Low Friction Lubes (2010$) 

Cost 

type

Engine 

type

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC All $3 $3 $3 $3 $3 $3 $3 $3 $3

IC All $1 $1 $1 $1 $1 $1 $1 $1 $1

TC All $4 $4 $4 $4 $4 $4 $4 $4 $4

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are 

incremental to the baseline.

3.4.1.2 Engine Friction Reduction 

In addition to low friction lubricants, manufacturers can also reduce friction and 

improve fuel consumption by improving the design of engine components and subsystems. 

Approximately 10 percent of the energy consumed by a vehicle is lost to friction, and just 

over half is due to frictional losses within the engine.39

 Example improvements include lowtension piston rings, piston skirt design, roller cam followers, improved crankshaft design and 

bearings, material coatings, material substitution, more optimal thermal management, and 

piston and cylinder surface treatments. Additionally, as computer-aided modeling software 

continues to improve, more opportunities for evolutionary friction reductions may become 

available. 

All reciprocating and rotating components in the engine are potential candidates for 

friction reduction where minute improvements in several components can result in a 

 

dd

 Note that the costs developed for low friction lubes for this analysis reflect the costs associated with any 

engine changes that would be required as well as any durability testing that may be required. 
 Technologies Considered in the Agencies’ Analysis

3-79 

measurable fuel economy improvement. In the MYs 2012-2016 final rule, the agencies relied 

on the 2002 NAS, NESCCAF and EEA reports, as well as, confidential manufacturer data that 

suggested a range of effectiveness for engine friction reduction (EFR1) to be between 1 to 3 

percent. Because of the incremental technology application capability of the CAFE model, 

NHTSA used the narrower range of 1 to 2 percent, which resulted in an average effectiveness 

of 1.5 percent. Based on the 2011 Ricardo study results, the agencies have revised the 

effectiveness for engine friction reduction range to 2.0 to 2.7 percent for this analysis. 

For this final rule, consistent with the proposal, the agencies added a second level of 

incremental improvements in engine friction reduction (EFR2) applicable over multiple 

vehicle redesign cycles. This second level of engine friction reduction forecasts additional 

improvements to low friction lubricants relative to the low friction lubricant technology 

discussed above and is considered to be mature only after MY 2017. The effectiveness for this 

second level, relative to the base engine, is 3.4 to 4.8 percent based on the lumped parameter 

model. Because of the incremental technology application capability of the CAFE model, 

NHTSA used the effectiveness range of 0.83 to 1.37 percent incremental to the first level of 

engine friction reduction and low friction lubricants for a total effectiveness of 2.83 to 4.07 

percent. 

In the MYs 2012-2016 rule, the 2010 TAR and the MYs 2014-2018 Medium and 

Heavy Duty GHG and Fuel Efficiency final rule, NHTSA and EPA used a EFR1 cost estimate 

of $11 (2007$) per cylinder DMC, or $12 (2010$) per cylinder in this analysis. No learning is 

applied to this technology so the DMC remains $12 (2010$) year-over-year. The agencies 

have used a low complexity ICM of 1.24 for this technology through 2018 and 1.19 

thereafter. The resultant costs are shown in Table 3-23. Note that EFR1 is expected to 

exceed 85 percent penetration by MY 2017. 

Table 3-23 Costs for Engine Friction Reduction – Level 1 –EFR1 (2010$) 

Cost 

type

Engine 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC I3 $36 $36 $36 $36 $36 $36 $36 $36 $36

DMC I4 $48 $48 $48 $48 $48 $48 $48 $48 $48

DMC V6 $71 $71 $71 $71 $71 $71 $71 $71 $71

DMC V8 $95 $95 $95 $95 $95 $95 $95 $95 $95

IC I3 $9 $9 $7 $7 $7 $7 $7 $7 $7

IC I4 $11 $11 $9 $9 $9 $9 $9 $9 $9

IC V6 $17 $17 $14 $14 $14 $14 $14 $14 $14

IC V8 $23 $23 $18 $18 $18 $18 $18 $18 $18

TC I3 $44 $44 $43 $43 $43 $43 $43 $43 $43

TC I4 $59 $59 $57 $57 $57 $57 $57 $57 $57

TC V6 $89 $89 $85 $85 $85 $85 $85 $85 $85

TC V8 $118 $118 $113 $113 $113 $113 $113 $113 $113

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are 

incremental to the baseline.

The agencies have estimated the DMC of the second level of friction reduction and 

low friction lubricants at double the combined DMCs of EFR1 (double the DMC relative to 

the baseline). As a result, the costs of EFR2 are as shown in Table 3-24. For EFR2 the 

agencies have used a low complexity ICM of 1.24 through 2024 and 1.19 thereafter. 
 Technologies Considered in the Agencies’ Analysis

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Table 3-24 Costs for Engine Friction Reduction – Level 2 – EFR2 (2010$) 

Cost 

type

Engine 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC I3 $78 $78 $78 $78 $78 $78 $78 $78 $78

DMC I4 $102 $102 $102 $102 $102 $102 $102 $102 $102

DMC V6 $149 $149 $149 $149 $149 $149 $149 $149 $149

DMC V8 $197 $197 $197 $197 $197 $197 $197 $197 $197

IC I3 $19 $19 $19 $19 $19 $19 $19 $19 $15

IC I4 $25 $25 $25 $25 $25 $25 $25 $25 $20

IC V6 $36 $36 $36 $36 $36 $36 $36 $36 $29

IC V8 $48 $48 $48 $48 $48 $48 $48 $48 $38

TC I3 $97 $97 $97 $97 $97 $97 $97 $97 $93

TC I4 $126 $126 $126 $126 $126 $126 $126 $126 $121

TC V6 $185 $185 $185 $185 $185 $185 $185 $185 $178

TC V8 $244 $244 $244 $244 $244 $244 $244 $244 $234

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are 

incremental to the baseline.

3.4.1.3 Cylinder Deactivation 

In conventional spark-ignition engines, throttling the intake airflow controls engine 

torque output. At partial loads, efficiency can be improved by using cylinder deactivation 

instead of throttling. Cylinder deactivation (DEAC) can improve engine efficiency by 

disabling or deactivating (usually) half of the cylinders when the load is less than half of the 

engine’s total torque capability. Cylinder deactivation is achieved by keeping specific 

cylinder valves closed and stopping fuel flow to the specified cylinder. As a result, the 

trapped air within the deactivated cylinders is simply compressed and expanded as an air 

spring, with reduced friction and heat losses. The active cylinders combust at almost double 

the load required if all of the cylinders were operating. Overall engine pumping losses are 

significantly reduced as long as the engine is operated in this “part-cylinder” mode. 

Cylinder deactivation control strategy relies on setting maximum manifold absolute 

pressures or predicted torque ranges where it is acceptable to deactivate engine cylinders. 

Noise and vibration issues reduce the operating range where cylinder deactivation is allowed, 

although manufacturers continue exploring vehicle changes that enable increasing the amount 

of time that cylinder deactivation might be suitable. Some manufacturers may choose to 

adopt active engine mounts and/or active noise cancellation systems to address NVH concerns 

and allow a greater operating range of activation which is also shown in the cost estimates for 

this technology. Most manufacturers have legitimately stated that use of DEAC on 4 cylinder 

engines would cause unacceptable NVH; therefore, as in the MYs 2012-2016 rule and the 

2010 TAR, the agencies are not applying cylinder deactivation to 4-cylinder engines in 

evaluating potential emission reductions/fuel economy improvements and associated costs. 

Cylinder deactivation has seen a recent resurgence thanks to better valvetrain designs 

and engine controls. General Motors and Chrysler Group have incorporated cylinder 

deactivation across a substantial portion of their V8-powered vehicles and Honda offers V6 

models with cylinder deactivation. 
 Technologies Considered in the Agencies’ Analysis

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Effectiveness improvements scale roughly with engine displacement-to-vehicle weight 

ratio: the higher displacement-to-weight vehicles, operating at lower relative loads for normal 

driving, have the potential to operate in part-cylinder mode more frequently. 

NHTSA and EPA reviewed estimates from the MYs 2012-2016 final rule, 2010 TAR, 

the RIA for the MYs 2014-2018 Medium and Heavy Duty GHG and Fuel Efficiency final 

rule. The lumped parameter model applied a 6 percent reduction in CO2 emissions depending 

on vehicle class. The CAFE model, due to its incremental technology application capability, 

used a range depending on the engine valvetrain configuration. For example, DOHC engines 

already equipped with DCP and DVVLD achieve little benefit, 0.5 percent for DEACD, from 

adding cylinder deactivation since the pumping work has already been minimized and internal 

Exhaust Gas Recirculation (EGR) rates are maximized. However, SOHC engines, which 

have CCP and DVVLS applied, achieve effectiveness ranging from 2.5 to 3 percent for 

DEACS. And finally, OHV engines, without VVT or VVL technologies, achieved 

effectiveness for DEACO ranging from 3.9 to 5.5 percent. 

For this final rule, consistent with the proposal, the agencies, taking into account the 

additional review and the work performed for the 2011 Ricardo study, have revised the 

effectiveness estimates for cylinder deactivation. The effectiveness relative to the base engine 

is 4.7 to 6.5 percent based on the lumped parameter model. Because of the incremental 

technology application capability of the CAFE model, NHTSA used the effectiveness range 

of 0.44 to 0.66 percent incremental for SOHC and DOHC applications. For OHV applications 

having no incremental application of VVT or VVL, the effectiveness was increased to a range 

of 4.66 to 6.30 percent. 

In the MYs 2012-2016 final rule and the 2010 TAR, the agencies used a DMC 

estimate of $140 (2007$) and $157 (2007$) for cylinder deactivation technology on V6 and 

V8 engines, respectively. Adjusted for 2010$, the DMCs become $146 (2010$) and $165 

(2010$) for this analysis and are considered applicable in MY 2015. This technology is 

considered to be on the flat-portion of the learning curve. The agencies have applied a low 

complexity ICM of 1.24 to this technology through 2018 and 1.19 thereafter. The resultant 

costs are shown in Table 3-25. 

Table 3-25 Costs for Cylinder Deactivation (2010$) 

Cost 

type

Engine 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC V6 $139 $136 $134 $131 $128 $126 $123 $121 $118

DMC V8 $157 $153 $150 $147 $144 $142 $139 $136 $133

IC V6 $56 $56 $42 $42 $42 $42 $42 $42 $42

IC V8 $63 $63 $47 $47 $47 $47 $47 $47 $47

TC V6 $196 $193 $176 $173 $170 $168 $165 $162 $160

TC V8 $220 $217 $198 $195 $191 $189 $186 $183 $180

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are 

incremental to the baseline.

There is potential that, on engines already equipped with the mechanisms required for 

cylinder deactivation capability, the cost of DEAC as applied to SOHC and DOHC engines 

could be as low as $32 in MY 2017. This $32 accounts for the potential additional 
 Technologies Considered in the Agencies’ Analysis

3-82 

application of active engine mounts on SOHC and DOHC engines that, while having the 

potential to apply cylinder deactivation, may or would require these additional NVH 

improving devices for consumer acceptance. For this analysis, this additional expanded 

application and expense is only applied on 50 percent of the vehicles. Further, this SOHC and 

DOHC engine estimate is relevant to the CAFE model only because the OMEGA model does 

not apply technologies in the same incremental fashion as the CAFE model. 

3.4.1.4 Variable Valve Timing (VVT) 

Variable valve timing (VVT) encompasses a family of valve-train designs that alter 

the timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, 

increase specific power, and control the level of residual gases in the cylinder. VVT reduces 

pumping losses when the engine is lightly loaded by controlling valve timing closer to an 

optimum needed to sustain horsepower and torque. VVT can also improve volumetric 

efficiency at higher engine speeds and loads. Additionally, VVT can be used to alter (and 

optimize) the effective compression ratio where it is advantageous for certain engine 

operating modes (e.g., in the Atkinson Cycle). 

VVT has now become a widely adopted technology: in MY 2011, approximately 93.8 

percent of all new cars and light trucks had engines with some method of variable valve 

timing.40

 Manufacturers are currently using many different types of variable valve timing, 

which have a variety of different names and methods. Manufacturers are currently using 

many different types of variable valve timing, which have a variety of different names and 

methods. Therefore, the degree of further improvement across the fleet is limited by the level 

of valvetrain technology already implemented on the vehicles. Information found in the 2008 

and 2010 baseline vehicle fleet files is used to determine the degree to which VVT 

technologies have already been applied to particular vehicles to ensure the proper level of 

VVT technology, if any, is applied. The three major types of VVT are listed below. 

Each of the three implementations of VVT uses a cam phaser to adjust the camshaft 

angular position relative to the crankshaft position, referred to as “camshaft phasing.” The 

phase adjustment results in changes to the pumping work required by the engine to 

accomplish the gas exchange process. The majority of current cam phaser applications use 

hydraulically-actuated units, powered by engine oil pressure and managed by a solenoid that 

controls the oil pressure supplied to the phaser. 

3.4.1.4.1 Intake Cam Phasing (ICP) 

Valvetrains with Intake Cam Phasing (ICP), which is the simplest of the cam phasing 

technologies, can modify the timing of the inlet valves by phasing the intake camshaft while 

the exhaust valve timing remains fixed. This requires the addition of a cam phaser on each 

bank of intake valves on the engine. An in-line 4-cylinder engine has one bank of intake 

valves, while V-configured engines have two banks of intake valves. 

In the MYs 2012-2016 final rule and 2010 TAR, NHTSA and EPA assumed an 

effectiveness range of 2 to 3 percent for ICP. Based on the additional information from the 
 Technologies Considered in the Agencies’ Analysis

3-83 

2011 Ricardo study and updated lumped parameter model the agencies have been able to finetuned the effectiveness range to be 2.1 to 2.7 percent for this analysis. 

In the MYs 2012-2016 rule and the 2010 TAR, the agencies estimated the DMC of a 

single cam phaser for ICP at $37 (2007$). This DMC, adjusted for 2010$, becomes $39 

(2010$) for this analysis and is considered applicable in the 2015 MY. This cost would be 

required for each cam shaft controlling intake valves. As such an OHC I4 and OHV V6 or V8 

would need one cam phaser while an OHC V6 or V8 would need two cam phasers. This 

technology is considered to be on the flat-portion of the learning curve. The agencies have 

applied a low complexity ICM of 1.24 to this technology through 2018 and 1.19 thereafter. 

The resultant costs are shown in Table 3-26. 

Table 3-26 Costs for VVT-Intake Cam Phasing - ICP (2010$) 

Cost 

type Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC OHC-I4 $37 $36 $35 $35 $34 $33 $33 $32 $31

DMC OHC-V6/V8 $74 $72 $71 $70 $68 $67 $65 $64 $63

DMC OHV-V6/V8 $37 $36 $35 $35 $34 $33 $33 $32 $31

IC OHC-I4 $9 $9 $7 $7 $7 $7 $7 $7 $7

IC OHC-V6/V8 $19 $19 $15 $15 $15 $15 $15 $15 $15

IC OHV-V6/V8 $9 $9 $7 $7 $7 $7 $7 $7 $7

TC OHC-I4 $46 $46 $43 $42 $42 $41 $40 $40 $39

TC OHC-V6/V8 $93 $91 $86 $84 $83 $82 $80 $79 $78

TC OHV-V6/V8 $46 $46 $43 $42 $42 $41 $40 $40 $39

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; OHC=overhead cam; 

OHV=overhead valve; all costs are incremental to the baseline.

 

3.4.1.4.2 Coupled Cam Phasing (CCP) 

Valvetrains with coupled (or coordinated) cam phasing can modify the timing of both 

the inlet valves and the exhaust valves an equal amount by phasing the camshaft of a single 

overhead cam (SOHC) engine or an overhead valve (OHV) engine. For SOHC engines, this 

requires the addition of a cam phaser on each bank of the engine. Thus, an in-line 4-cylinder 

engine has one cam phaser, while SOHC V-engines have two cam phasers. For OHV 

engines, which have only one camshaft to actuate both inlet and exhaust valves, CCP is the 

only VVT implementation option available and requires only one cam phaser.ee

The agencies’ MYs 2012-2016 final rule estimated the effectiveness of CCP to be 

between 1 to 4 percent. Due to the incremental technology application capability of the 

 

ee It is also noted that coaxial camshaft developments would allow other VVT options to be applied to OHV 

engines. However, since they would potentially be adopted on a limited number of OHV engines NHTSA did 

not include them in the decision tree. 
 Technologies Considered in the Agencies’ Analysis

3-84 

CAFE model, NHTSA estimated the effectiveness for CCP to be 1 to 3 percent for a SOHC 

engine and 1 to 1.5 percent for an overhead valve engine. 

For this final rule, consistent with the proposal, the agencies, have revised the 

estimates for CCP taking into account the additional review and the work performed for the 

2011 Ricardo study. The effectiveness relative to the base engine is 4.1 to 5.5 percent based 

on the lumped parameter model. Because of the incremental nature of the CAFE model, 

NHTSA used the incremental effectiveness range of 4.14 to 5.36 percent for SOHC 

applications; an increase over the MYs 2012-16 final rule and 2010 TAR. For OHV 

applications, CCP was paired with discrete variable valve lift (DVVL) to form a new 

technology descriptor called variable valve actuation (VVA). Effectiveness values for this 

new descriptor is discussed later in Section 3.4.1.6. 

In regard to CCP costs, the same cam phaser has been assumed for intake cam phasing 

as for coupled cam phasing, thus the DMCs for CCP is identical to those presented for ICP in 

Table 3-26. 

3.4.1.4.3 Dual Cam Phasing (DCP) 

The most flexible VVT design is dual (independent) cam phasing (DCP), where the 

intake and exhaust valve opening and closing events are controlled independently. This 

allows the option of controlling valve overlap, which can be used as an internal EGR strategy. 

At low engine loads, DCP creates a reduction in pumping losses, resulting in improved fuel 

consumption/reduced CO2 emissions. Increased internal EGR also results in lower engine-out 

NOX emissions. Fuel consumption and CO2 emissions improvements enabled by DCP are 

dependent on the residual tolerance of the combustion system. Additional improvements are 

observed at idle, where low valve overlap could result in improved combustion stability, 

potentially reducing idle fuel consumption. For forward looking technology application, DCP 

is only applicable to dual overhead cam (DOHC) engines.ff

For the MYs 2012-2016 final rule and 2010 TAR, the EPA and NHTSA assumed an 

effectiveness range for DCP of 3 to 5 percent relative to a base engine or 2 to 3 relative to an 

engine with ICP. The agencies have updated this range, based on the updated lumpedparameter model, to be 4.1 to 5.5 percent relative to a base engine or 2.0 to 2.7 percent 

relative to an engine with ICP. 

 

ff The agencies note at least one production implementation of an OHV dual cam phasing is included in the 

baseline fleet. This consisted of a single concentric camshaft (a “camshaft within a camshaft”) and a single dual 

vane phaser assemblies enabling independent phasing of the intake and exhaust camshaft profiles. However, this 

technology was applied to a limited production sports car versus a mass market application with significant sales 

volume. The agencies are not aware of any similar application moving forward. 
 Technologies Considered in the Agencies’ Analysis

3-85 

The costs for DCP are the same per phaser as described above for ICP. However, for 

DCP, an additional cam phaser is required for each camshaft controlling exhaust valves. As a 

result, a dual overhead cam I4 would need two phasers and a dual overhead cam V6 or V8 

would need four phasers, and an overhead valve V engine would need two.gg

This technology is considered to be on the flat-portion of the learning curve. The 

agencies have applied a medium complexity ICM of 1.39 to this technology through 2018 and 

1.29 thereafter. The resultant costs are shown in Table 3-27. 

Table 3-27 Costs for VVT-Dual Cam Phasing (2010$) 

Cost 

type Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC OHC-I4 $68 $66 $65 $64 $62 $61 $60 $59 $58

DMC OHC-V6/V8 $146 $143 $140 $137 $134 $132 $129 $127 $124

DMC OHV-V6/V8 $74 $72 $71 $70 $68 $67 $65 $64 $63

IC OHC-I4 $27 $27 $20 $20 $20 $20 $20 $20 $20

IC OHC-V6/V8 $59 $59 $44 $44 $44 $44 $44 $44 $44

IC OHV-V6/V8 $30 $30 $22 $22 $22 $22 $22 $22 $22

TC OHC-I4 $95 $94 $86 $84 $83 $82 $80 $79 $78

TC OHC-V6/V8 $205 $202 $184 $181 $178 $176 $173 $170 $168

TC OHV-V6/V8 $104 $102 $93 $92 $90 $89 $88 $86 $85

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; OHC=overhead cam; 

OHV=overhead valve; all costs are incremental to the baseline.

3.4.1.5 Variable Valve Lift (VVL) 

Varying and controlling the amount of cylinder valve lift across and engine operating 

range provides a potential for further efficiency improvements. By optimizing the valve-lift 

profile for specific engine operating regions, the pumping losses can be reduced by reducing 

the amount of throttling required to produce the desired engine power output. By moving the 

throttling losses further downstream of the throttle valve, the heat transfer losses that occur 

from the throttling process are directed into the fresh charge-air mixture just prior to 

compression, delaying the onset of knock-limited combustion processes. Variable valve lift 

control can also be used to induce in-cylinder mixture motion, which improves fuel-air 

mixing and can result in improved thermodynamic efficiency. Variable valve lift control can 

also potentially reduce overall valvetrain friction. At the same time, such systems may also 

incur increased parasitic losses associated with their actuation mechanisms. A number of 

manufacturers have already implemented VVL into their fleets (Toyota, Honda, and BMW), 

but overall this technology is still available as an efficiency improving technology for most of 

the fleet. There are two major classifications of variable valve lift, described below: 

 

gg Ibid. 
 Technologies Considered in the Agencies’ Analysis

3-86 

3.4.1.5.1 Discrete Variable Valve Lift (DVVL) 

Discrete variable valve lift (DVVL) systems allow the selection between two or three 

discrete cam profiles by means of a hydraulically-actuated mechanical system. These cam 

profiles consist of a low and a high-lift lobe, and may include an inert or blank lobe to 

incorporate cylinder deactivation (in the case of a 3-step DVVL system). DVVL is normally 

applied together with VVT control. DVVL is also known as Cam Profile Switching (CPS). 

DVVL is a mature technology with low technical risk. 

The effectiveness of DVVL has been estimated to range from 1 to 4 percent in addition to that 

realized by VVT systems. These values were based on the research supporting MYs 2012-16 

final rule, confidential manufacturer data, and a research conducted by the Northeast States 

Center for a Clean Air Future (NESCCAF). Based on additional information contained in the 

2011 Ricardo study, NHTSA and EPA have revised the effectiveness range of DVVL systems 

to 2.8 to 3.9 percent above that realized by VVT systems. 

In the MYs 2012-2016 rule and the 2010 TAR, the agencies estimated the DMC of 

DVVL at $116 (2007$), $169 (2007$) and $241 (2007$) for an I4, V6 and V8 engine, 

respectively. Adjusted for 2010$, these DMCs become $122 (2010$), $177 (2010$) and $253 

(2010$) for this analysis all of which are considered applicable in MY 2015. This technology 

is considered to be on the flat-portion of the learning curve and is applicable only to engines 

with overhead cam configurations. The agencies have applied a medium complexity ICM of 

1.39 to this technology through 2018 and 1.29 thereafter. The resultant costs are shown in 

Table 3-28. 

Table 3-28 Costs for Discrete Variable Valve Lift – DVVL (2010$) 

Cost 

type Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC OHC-I4 $116 $114 $111 $109 $107 $105 $103 $101 $99

DMC OHC-V6 $168 $165 $161 $158 $155 $152 $149 $146 $143

DMC OHC-V8 $240 $235 $231 $226 $222 $217 $213 $209 $204

IC OHC-I4 $47 $47 $35 $35 $35 $35 $35 $35 $35

IC OHC-V6 $68 $68 $51 $51 $51 $50 $50 $50 $50

IC OHC-V8 $97 $97 $73 $72 $72 $72 $72 $72 $72

TC OHC-I4 $163 $161 $146 $144 $142 $140 $137 $135 $133

TC OHC-V6 $236 $233 $212 $209 $206 $202 $199 $196 $193

TC OHC-V8 $338 $333 $303 $298 $294 $289 $285 $280 $276

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; OHC=overhead cam; 

OHV=overhead valve; all costs are incremental to the baseline.

3.4.1.5.2 Continuously Variable Valve Lift (CVVL) 

In CVVL systems, valve lift is varied by means of a mechanical linkage, driven by an 

actuator controlled by the engine control unit. The valve opening and phasing vary as the lift 

is changed and the relation depends on the geometry of the mechanical system. BMW has 

considerable production experience with CVVL systems and has sold port-injected 

“Valvetronic” engines since 2001. Fiat is now offering “MultiAir” engines enabling precise 

control over intake valve lift. CVVL allows the airflow into the engine to be regulated by 
 Technologies Considered in the Agencies’ Analysis

3-87 

means of intake valve opening reduction, which improves engine efficiency by reducing 

pumping losses from throttling the intake system further upstream as with a conventionally 

throttled engine. 

Variable valve lift gives a further reduction in pumping losses compared to that which 

can be obtained with cam phase control only, with CVVL providing greater effectiveness than 

DVVL, since it can be fully optimized for all engine speeds and loads, and is not limited to a 

two or three step compromise. There may also be a small reduction in valvetrain friction 

when operating at low valve lift, resulting in improved low load fuel consumption for cam 

phase control with variable valve lift as compared to cam phase control only. Most of the fuel 

economy effectiveness is achieved with variable valve lift on the intake valves only. CVVL 

is only applicable to double overhead cam (DOHC) engines. 

The MYs 2012-2016 final rule estimated the effectiveness for CVVL at 1.5 to 3.5 

percent over an engine with DCP, but also recognized that it could go up as high as 5 percent 

above and beyond DCP to account for the implementation of more complex CVVL systems 

such as BMW’s “Valvetronic” and Fiat “MultiAir” systems. Thus, the effectiveness range for 

CVVL in this Joint TSD ranges from 1.5 to 7 percent depending on the complexity level of 

the application 

. For this rulemaking, NHTSA has increased the incremental effectiveness values for 

this technology to a range of 3.6 to 4.9 percent from 1.5 to 3.5 percent in the MYs 2012-2016 

final rule. 

In the 2012-2016 rule and the 2010 TAR, the agencies estimated the DMC of CVVL 

at $174 (2007$), $320 (2007$), $349 (2007$), $866 (2007$) and $947 (2007$) for an OHCI4, OHC-V6, OHC-V8, OHV-V6 and OHV-V8 engine, respectively. Adjusted for 2010$, 

these DMCs become $183 (2010$), $335 (2010$), $366 (2010$), $893 (2010$) and $977 

(2010$) for this analysis all of which are considered applicable in MY 2015. As indicated in 

this section, CVVL is considered only applicable to DOHC engine designs. The DMCs for 

OHV engines are meant to reflect additional costs associated with moving to a DOHC engine 

design. 

This technology is considered to be on the flat-portion of the learning curve. The 

agencies have applied a medium complexity ICM of 1.39 to this technology through 2018 and 

1.29 thereafter. The resultant costs are shown in Table 3-29. 

Table 3-29 Costs for Continuous Variable Valve Lift – CVVL (2010$) 

Cost 

type

Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC OHC-I4 $174 $170 $167 $164 $160 $157 $154 $151 $148

DMC OHC-V6 $319 $313 $306 $300 $294 $288 $283 $277 $271

DMC OHC-V8 $348 $341 $334 $327 $321 $314 $308 $302 $296

DMC OHV-V6 $857 $840 $823 $807 $791 $775 $760 $744 $729 

DMC OHV-V8 $937 $919 $901 $883 $865 $847 $830 $814 $798 

IC OHC-I4 $70 $70 $53 $52 $52 $52 $52 $52 $52

IC OHC-V6 $129 $129 $96 $96 $96 $96 $96 $95 $95

IC OHC-V8 $141 $141 $105 $105 $105 $104 $104 $104 $104
 Technologies Considered in the Agencies’ Analysis

3-88 

IC OHV-V6 $347 $346 $259 $259 $258 $258 $257 $257 $256 

IC OHV-V8 $380 $379 $283 $283 $282 $282 $281 $281 $280 

TC OHC-I4 $244 $241 $220 $216 $213 $209 $206 $203 $200

TC OHC-V6 $448 $441 $403 $396 $390 $384 $378 $372 $367

TC OHC-V8 $489 $482 $439 $432 $426 $419 $412 $406 $400

TC OHV-V6 $1,205 $1,187 $1,083 $1,066 $1,048 $1,032 $1,016 $1,001 $986 

TC OHV-V8 $1,317 $1,298 $1,184 $1,166 $1,147 $1,129 $1,112 $1,095 $1,078 

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; OHC=overhead cam; OHV=overhead 

valve; all costs are incremental to the baseline.

3.4.1.6 Variable Valve Actuation (VVA) 

For this final rule, consistent with the proposal, NHTSA has combined two valve 

control technologies for OHV engines. Coupled cam phasing (CCPO) and discrete valve lift 

(DVVLO) into one technology defined as variable valve actuation (VVA). The agency 

estimates the incremental effectiveness for VVA applied to and OHV engine as 2.71 to 3.59 

percent. This effectiveness value is slightly lower than coupled cam phasing for overhead cam 

applications (CCPS) based on the assumption that VVA would be applied to an OHV engine 

after cylinder deactivation (DEAC). For more information on combining these technologies 

please refer to NHTSA’s FRIA. 

3.4.1.7 Stoichiometric Gasoline Direct Injection (SGDI) 

Stoichiometric gasoline direct injection (SGDI), or Spark Ignition Direct injection 

(SIDI), engines inject fuel at high pressure directly into the combustion chamber (rather than 

the intake port in port fuel injection). SGDI requires changes to the injector design, an 

additional high pressure fuel pump, new fuel rails to handle the higher fuel pressures and 

changes to the cylinder head and piston crown design. Direct injection of the fuel into the 

cylinder improves cooling of the air/fuel charge within the cylinder, which allows for higher 

compression ratios and increased thermodynamic efficiency without the onset of combustion 

knock. Recent injector design advances, improved electronic engine management systems 

and the introduction of multiple injection events per cylinder firing cycle promote better 

mixing of the air and fuel, enhance combustion rates, increase residual exhaust gas tolerance 

and improve cold start emissions. SGDI engines achieve higher power density and match 

well with other technologies, such as boosting and variable valvetrain designs. 

Several manufacturers are manufacturing vehicles with SGDI engines, including 

VW/Audi, BMW, Toyota, Ford, and General Motors. Additionally, BMW, GM, Ford and 

VW/Audi have announced plans to significantly increase the number of SGDI engines in their 

portfolios. 

NHTSA and EPA reviewed estimates from the MYs 2012-2016 final rule and 2010 

TAR, which stated an effectiveness range of SGDI to be between 2 and 3 percent. NHTSA 

and EPA reviewed estimates from the Alliance of Automobile Manufactures, which projects 3 

percent gains in fuel efficiency and a 7 percent improvement in torque. The torque increase 

provides the opportunity to downsize the engine allowing an increase in efficiency of up to a 

5.8 percent. NHTSA and EPA also reviewed other published literature, reporting 3 percent 

effectiveness for SGDI.41 Confidential manufacturer data reported an efficiency effectiveness 
 Technologies Considered in the Agencies’ Analysis

3-89 

range of 1 to 2 percent. Based on data from the 2011 Ricardo study and reconfiguration of the 

new lumped parameter model, EPA and NHTSA have revised this value to 1.5 percenthh. 

Combined with other technologies (i.e., boosting, downsizing, and in some cases, cooled 

EGR), SGDI can achieve greater reductions in fuel consumption and CO2 emissions 

compared to engines of similar power output. 

The NHTSA and EPA cost estimates for SGDI take into account the changes required 

to the engine hardware, engine electronic controls, ancillary and Noise Vibration and 

Harshness (NVH) mitigation systems. Through contacts with industry NVH suppliers, and 

manufacturer press releases, the agencies believe that the NVH treatments will be limited to 

the mitigation of fuel system noise, specifically from the injectors and the fuel lines and have 

included corresponding cost estimates for these NVH controls. In the 2012-2016 FRM, the 

agencies estimated the DMC for SGDI at $213 (2007$), $321 (2007$) and $386 (2007$) for 

I3/I4, V6 and V8 engines, respectively. Adjusted for 2010$, these DMCs become $222 

(2010$), $334 (2010$) and $402 (2010$) for this analysis all of which are considered 

applicable in MY 2012. This technology is considered to be on the flat-portion of the learning 

curve. The agencies have applied a medium complexity ICM of 1.39 to this technology 

through 2018 and 1.29 thereafter. The resultant costs are shown in Table 3-30.

Table 3-30 Costs for Stoichiometric Gasoline Direct Injection (2010$) 

Cost type Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC I3/I4 $192 $188 $185 $181 $177 $174 $170 $167 $164

DMC V6 $290 $284 $278 $273 $267 $262 $257 $251 $246

DMC V8 $348 $341 $335 $328 $321 $315 $309 $302 $296

IC I3/I4 $84 $84 $63 $63 $63 $63 $63 $62 $62

IC V6 $127 $127 $95 $95 $95 $94 $94 $94 $94

IC V8 $153 $153 $114 $114 $114 $114 $113 $113 $113

TC I3/I4 $277 $273 $248 $244 $240 $236 $233 $229 $226

TC V6 $417 $411 $373 $367 $362 $356 $351 $346 $340

TC V8 $501 $494 $449 $442 $435 $429 $422 $416 $409

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the baseline.

3.4.1.8 Turbocharging and Downsizing (TRBDS) 

The specific power of a naturally aspirated engine is primarily limited by the rate at 

which the engine is able to draw air into the combustion chambers. Turbocharging and 

supercharging (grouped together here as boosting) are two methods to increase the intake 

manifold pressure and cylinder charge-air mass above naturally aspirated levels. Boosting 

increases the airflow into the engine, thus increasing the specific power level, and with it the 

ability to reduce engine displacement while maintaining performance. This effectively 

reduces the pumping losses at lighter loads in comparison to a larger, naturally aspirated 

engine. 

 

hh However, because GDI is a key enabler for modern, highly downsized turbocharged engines, this difference 

will be overshadowed by the higher effectiveness for turbocharging and downsizing when they are combined 

into packages. 
 Technologies Considered in the Agencies’ Analysis

3-90 

Almost every major manufacturer currently markets a vehicle with some form of 

boosting. While boosting has been a common practice for increasing performance for several 

decades, turbocharging has considerable potential to improve fuel economy and reduce CO2

emissions when the engine displacement is also reduced. Specific power levels for a boosted 

engine often exceed 100 hp/L, compared to average naturally aspirated engine power densities 

of roughly 70 hp/L. As a result, engines can be downsized roughly 30 percent or higher while 

maintaining similar peak output levels. In the last decade, improvements to turbocharger 

turbine and compressor design have improved their reliability and performance across the 

entire engine operating range. New variable geometry turbines and ball-bearing center 

cartridges allow faster turbocharger spool-up (virtually eliminating the once-common “turbo 

lag”) while maintaining high flow rates for increased boost at high engine speeds. Low speed 

torque output has been dramatically improved for modern turbocharged engines. However, 

even with turbocharger improvements, maximum engine torque at very low engine speed 

conditions, for example launch from standstill, is increased less than at mid and high engine 

speed conditions. The potential to downsize engines may be less on vehicles with low 

displacement to vehicle mass ratios for example a very small displacement engine in a vehicle 

with significant curb weight, in order to provide adequate acceleration from standstill, 

particularly up grades or at high altitudes. 

Use of GDI systems with turbocharged engines and air-to-air charge air cooling also 

reduces the fuel octane requirements for knock limited combustion and allows the use of 

higher compression ratios. Ford’s “Ecoboost” downsized, turbocharged GDI engines 

introduced on MY 2010 vehicles allow the replacement of V8 engines with V6 engines with 

improved in 0-60 mph acceleration and with fuel economy improvements of up to 12 

percent.42

Recently published data with advanced spray-guided injection systems and more 

aggressive engine downsizing targeted towards reduced fuel consumption and CO2 emissions 

reductions indicate that the potential for reducing CO2 emissions for turbocharged, downsized 

GDI engines may be as much as 15 to 30 percent relative to port-fuel-injected 

engines.27,28,29,30,31 Confidential manufacturer data suggests an incremental range of fuel 

consumption and CO2 emission reduction of 4.8 to 7.5 percent for turbocharging and 

downsizing. Other publicly-available sources suggest a fuel consumption and CO2 emission 

reduction of 8 to 13 percent compared to current-production naturally-aspirated engines 

without friction reduction or other fuel economy technologies: a joint technical paper by 

Bosch and Ricardo suggesting fuel economy gain of 8 to 10 percent for downsizing from a 5.7 

liter port injection V8 to a 3.6 liter V6 with direct injection using a wall-guided direct 

injection system;43 a Renault report suggesting a 11.9 percent NEDC fuel consumption gain 

for downsizing from a 1.4 liter port injection in-line 4-cylinder engine to a 1.0 liter in-line 4-

cylinder engine, also with wall-guided direct injection;44 and a Robert Bosch paper suggesting 

a 13 percent NEDC gain for downsizing to a turbocharged DI engine, again with wall-guided 

injection.45

 These reported fuel economy benefits show a wide range depending on the SGDI 

technology employed. 

NHTSA and EPA reviewed estimates from the 2012-2016 final rule, the TAR, and 

existing public literature. The previous estimate from the MYs 2012-2016 suggested a 12 to 

14 percent effectiveness improvement, which included low friction lubricant (level one), 
 Technologies Considered in the Agencies’ Analysis

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engine friction reduction (level one), DCP, DVVL and SGDI, over baseline fixed-valve 

engines, similar to the estimate for Ford’s Ecoboost engine, which is already in production. 

Additionally, the agencies analyzed Ricardo vehicle simulation data for various turbocharged 

engine packages. Based on this data, and considering the widespread nature of the public 

estimates, the effectiveness of turbocharging and downsizing is highly dependent upon 

implementation and degree of downsizing. 

In alignment with these variances, for this final rule, consistent with the proposal, the 

agencies evaluated 4 different levels of downsized and turbocharged high Brake Mean 

Effective Pressure (BMEP)ii. engines; 18-bar, 24-bar, 24-bar with cooled exhaust gas 

recirculation (EGR) and 27-bar with cooled EGR All engines are assumed to include 

gasoline direct injection (SGDI) and effectiveness values include the benefits of this 

technology. In addition, the agencies believe to implement in production a 27 bar boost level, 

it is necessary to incorporate cooled exhaust gas recirculation (EGR) and also require a 2-

stage turbocharger as well as engine changes to increase robustness. The cooled EGR 

technology is discussed later in this section. 

NHTSA and EPA have revised the effectiveness to reflect this new information and 

assume that turbocharging and downsizing, alone, will provide a 12 to 24.6 percent 

effectiveness improvement (dependent upon degree of downsizing and boost levels) over 

naturally aspirated, fixed-valve engines. More specifically, 12.1 to 14.9 percent for 18-bar 

engines, which is equal to the boost levels evaluated in the MYs 2012-2016 final rule, 

assuming 33 percent downsizing, 16.4 to 20.1 percent for 24-bar engines, assuming 50 

percent downsizing, 19.3 to 23.0 percent for 24-bar engines with cooled EGR, assuming 50 

percent downsizing and 20.6 to 24.6 percent for 27-bar engines with cooled EGR, assuming 

56 percent downsizing. For comparison purposes an 18-bar engine with low friction lubricant 

(level one), engine friction reduction (level one), DCP, DVVL and SGDI, which is equivalent 

to MYs 2012-2016 assumed turbocharging and downsizing technology, now results in a 16.8 

to 20.9 percent effectiveness improvement. Coupling turbocharging and downsizing with low 

friction lubricant (level one and two), engine friction reductions (level one and two), DCP, 

DVVL and SGDI, for the MYs 2017-2025 timeframe, yields 18.0 to 22.4 percent for 18-bar 

engines 20.4 to 25.2 percent for 24-bar engines, 23.2 to 27.9 percent for 24-bar engine with 

cooled EGR and 24.0 to 28.8 percent for 27-bar with cooled EGR over naturally aspirated, 

fixed-valve engines. 

As noted above, the agencies relied on engine teardown analyses conducted by EPA, 

FEV and Munro to develop costs for turbocharged GDI engines.46 In the 2012-2016 FRM, 

the agencies estimated the DMC for turbocharging to 18 bar BMEP at $404 (2007$) and $681 

(2007$) for I4 and V6/V8 engines, respectively, where the higher cost for the V-configuration 

 

ii Brake Mean Effective Pressure is the average amount of pressure in pounds per square inch (psi) that must be 

exerted on the piston to create the measured horsepower. This indicates how effective an engine is at filling the 

combustion chamber with an air/fuel mixture, compressing it and achieving the most power from it. A higher 

BMEP value contributes to higher overall efficiency. 
 Technologies Considered in the Agencies’ Analysis

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engines represents twin turbochargers versus the single turbocharger in the I-configuration 

engine. These DMCs become $420 (2010$) and $708 (2010$), respectively, for this analysis. 

In the 2010 TAR, the agencies presented costs for 24 bar BMEP turbocharging at 1.5x the 

cost of the 18 bar BMEP technology. This additional cost covered the incremental cost 

increase of a variable geometry turbocharger (see 2010 TAR at page B-12). Thus, the DMC 

for 24 bar BMEP would be $630 (2010$) and $1,062 (2010$) for I-configuration and Vconfiguration engines, respectively. Note also for this final rule, the agencies are estimating 

the DMC of the 27 bar BMEP technology at 2.5x the 18 bar BMEP technology, or $1,050 

(2010$) and $1,771 (2010$) for I-configuration and V-configuration engines, respectively. All 

of these turbocharger-related DMCs are considered applicable in the 2012MY. The agencies 

consider each turbocharger technology to be on the flat portion of the learning curve and have 

applied a medium complexity ICM of 1.39 through 2018 for 18 bar and through 2024 for 24 

and 27 bar, then 1.29 to each thereafter. The resultant costs are shown in Table 3-31. 

Table 3-31 Costs for Turbocharging (2010$) 

Cost 

type

Technology 

(BMEP) Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC 18 bar I-engine $365 $357 $350 $343 $336 $330 $323 $316 $310

DMC 18 bar V-engine $614 $602 $590 $578 $567 $555 $544 $533 $523

DMC 24 bar I-engine $547 $536 $525 $515 $504 $494 $484 $475 $465

DMC 24 bar V-engine $922 $903 $885 $867 $850 $833 $816 $800 $784

DMC 27 bar I-engine $911 $893 $875 $858 $841 $824 $807 $791 $775

DMC 27 bar V-engine $1,536 $1,505 $1,475 $1,446 $1,417 $1,389 $1,361 $1,334 $1,307

IC 18 bar I-engine $160 $160 $120 $119 $119 $119 $119 $118 $118

IC 18 bar V-engine $270 $270 $202 $201 $201 $200 $200 $200 $199

IC 24 bar I-engine $240 $240 $239 $239 $238 $238 $238 $237 $177

IC 24 bar V-engine $405 $404 $403 $403 $402 $401 $400 $400 $299

IC 27 bar I-engine $401 $400 $399 $398 $397 $397 $396 $395 $296

IC 27 bar V-engine $675 $674 $672 $671 $670 $669 $667 $666 $499

TC 18 bar I-engine $525 $517 $470 $462 $455 $448 $442 $435 $428

TC 18 bar V-engine $885 $872 $792 $779 $768 $756 $744 $733 $722

TC 24 bar I-engine $787 $776 $765 $754 $743 $732 $722 $712 $643

TC 24 bar V-engine $1,327 $1,308 $1,289 $1,270 $1,252 $1,234 $1,217 $1,200 $1,083

TC 27 bar I-engine $1,312 $1,293 $1,274 $1,256 $1,238 $1,220 $1,203 $1,186 $1,071

TC 27 bar V-engine $2,211 $2,179 $2,148 $2,117 $2,087 $2,057 $2,028 $2,000 $1,805

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the baseline.

The costs for the downsizing portion of the turbo/downsize technology is more 

complex. The agencies have described those cost and how they were developed—based 

primarily on FEV teardowns but some were scaled based on teardowns to generate costs for 

downsizing situations that were not covered by teardowns—in both the 2012-2016 FRM and 

the 2010 TAR. The DMCs used for this analysis are identical to those used in the 2010 TAR 

except that they have been updated to 2010 dollars. Notable is the fact that many of the 

downsizing costs are negative because they result in fewer parts and less material than the 

engine from which they are “derived.” For example a V8 engine could be replaced by a 

turbocharged V6 engine having two fewer cylinders and as many as eight fewer valves (in the 

case of a V8 DOHC downsized to a V6 DOHC). Importantly, the agencies have used an 

approach to calculating indirect costs that results in positive indirect costs regardless of 

whether the DMC is positive or negative. This is done by calculating indirect costs based on 

the absolute value of the DMC, then adding the indirect cost to the DMC to arrive at the total 
 Technologies Considered in the Agencies’ Analysis

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cost. This way, the agencies are never making a negative DMC “more negative” when 

accounting for the indirect costs. This approach has been used in the 2012-2016 final rule and 

the 2010 TAR. Given the history of the downsizing costs used by the agencies, many are 

considered applicable in the 2012MY and many in the 2017MY.jj All are considered to be on 

the flat portion of the learning curve. The agencies have applied a medium complexity ICM 

of 1.39 through 2018 and 1.29 thereafter. The resultant costs are shown in Table 3-32. 

Table 3-32 Costs for Engine Downsizing (2010$) 

Cost 

type Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC I4 DOHC to I3 -$174 -$171 -$167 -$164 -$161 -$157 -$154 -$151 -$148

DMC I4 DOHC to I4 -$77 -$75 -$74 -$72 -$71 -$69 -$68 -$67 -$65

DMC V6 DOHC to I4 -$494 -$484 -$474 -$465 -$455 -$446 -$437 -$429 -$420

DMC V6 SOHC 2V to 

I4 -$345 -$338 -$331 -$325 -$318 -$312 -$306 -$300 -$294

DMC V6 OHV to I4 $281 $272 $264 $256 $249 $241 $236 $232 $227

DMC V8 DOHC to I4 -$854 -$828 -$804 -$779 -$756 -$733 -$719 -$704 -$690

DMC V8 DOHC to V6 -$247 -$242 -$237 -$233 -$228 -$223 -$219 -$215 -$210

DMC V8 SOHC 2V to 

I4 -$656 -$637 -$617 -$599 -$581 -$564 -$552 -$541 -$530

DMC V8 SOHC 3V to 

I4 -$731 -$709 -$687 -$667 -$647 -$627 -$615 -$603 -$591

DMC V8 SOHC 2V to 

V6 -$76 -$74 -$73 -$71 -$70 -$68 -$67 -$66 -$64

DMC V8 SOHC 3V to 

V6 -$140 -$137 -$135 -$132 -$129 -$127 -$124 -$122 -$119

DMC V8 OHV to I4 -$242 -$234 -$227 -$220 -$214 -$207 -$203 -$199 -$195

DMC V8 OHV to V6 $328 $318 $308 $299 $290 $281 $276 $270 $265

IC I4 DOHC to I3 $77 $76 $57 $57 $57 $57 $57 $57 $57

IC I4 DOHC to I4 $34 $34 $25 $25 $25 $25 $25 $25 $25

IC V6 DOHC to I4 $217 $217 $162 $162 $161 $161 $161 $161 $160

IC V6 SOHC 2V to 

I4 $152 $151 $113 $113 $113 $113 $112 $112 $112

IC V6 OHV to I4 $109 $108 $81 $81 $80 $80 $80 $80 $80

IC V8 DOHC to I4 $331 $330 $246 $245 $244 $244 $243 $243 $242

IC V8 DOHC to V6 $109 $108 $81 $81 $81 $81 $80 $80 $80

IC V8 SOHC 2V to 

I4 $254 $253 $189 $188 $188 $187 $187 $187 $186

IC V8 SOHC 3V to 

I4 $283 $282 $210 $210 $209 $208 $208 $208 $207

IC V8 SOHC 2V to 

V6 $33 $33 $25 $25 $25 $25 $25 $25 $25

IC V8 SOHC 3V to 

V6 $62 $61 $46 $46 $46 $46 $46 $46 $45

IC V8 OHV to I4 $94 $93 $70 $69 $69 $69 $69 $69 $69

IC V8 OHV to V6 $127 $126 $94 $94 $94 $93 $93 $93 $93

TC I4 DOHC to I3 -$98 -$94 -$110 -$107 -$104 -$101 -$98 -$95 -$92

 

jj The engine downsize costs based on actual FEV teardowns were considered applicable to the 2012MY, as was 

explained for some downsize costs in the 2012-2016 final rule and others in the 2010 TAR. For other downsize 

costs—the two changes from OHV engines to DOHC engines—the agencies did not use FEV teardowns or 

extrapolations from FEV teardowns, and instead used the methodology employed in the 2008 EPA Staff Report, 

a methodology determined by both agencies to result in cost estimates more appropriate for the 2017MY. The 

new downsize costs—those for V8 engines downsized to I4 engines—use a combination of V8 to V6 then V6 to 

I4 downsize costs and are considered applicable to the 2017MY within the context of this analysis. 
 Technologies Considered in the Agencies’ Analysis

3-94 

TC I4 DOHC to I4 -$43 -$41 -$48 -$47 -$46 -$44 -$43 -$42 -$40

TC V6 DOHC to I4 -$277 -$267 -$312 -$303 -$294 -$285 -$277 -$268 -$260

TC V6 SOHC 2V to 

I4 -$193 -$187 -$218 -$212 -$205 -$199 -$193 -$187 -$182

TC V6 OHV to I4 $390 $381 $345 $337 $329 $321 $316 $311 $307

TC V8 DOHC to I4 -$523 -$499 -$558 -$534 -$512 -$490 -$476 -$462 -$448

TC V8 DOHC to V6 -$139 -$134 -$156 -$152 -$147 -$143 -$138 -$134 -$130

TC V8 SOHC 2V to 

I4 -$402 -$383 -$429 -$411 -$393 -$376 -$365 -$355 -$344

TC V8 SOHC 3V to 

I4 -$448 -$427 -$477 -$457 -$438 -$419 -$407 -$395 -$383

TC V8 SOHC 2V to 

V6 -$42 -$41 -$48 -$46 -$45 -$44 -$42 -$41 -$40

TC V8 SOHC 3V to 

V6 -$79 -$76 -$89 -$86 -$83 -$81 -$78 -$76 -$74

TC V8 OHV to I4 -$148 -$141 -$158 -$151 -$145 -$139 -$134 -$131 -$127

TC V8 OHV to V6 $454 $444 $403 $393 $384 $375 $369 $363 $358

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the baseline; all 

resultant engines are DOHC.

Note that the V8 to I4 engine downsize is new for this final rule, consistent with the 

proposal. This level of engine downsizing is considered for this analysis only if it also 

includes 27 bar BMEP turbo boost which, in addition, requires the addition of cooled EGR 

(discussed below). As a result, any 27 bar BMEP engine in this analysis will be I4 

configuration and will include cooled EGR. 

With the information shown in Table 3-31 and Table 3-32, the costs for any 

turbo/downsize change can be determined. These costs are shown in Table 3-33. 

Table 3-33 Total Costs for Turbo/Downsizing (2010$) 

Downsize

Technology

Turbo

Technology 

(BMEP)

2017 2018 2019 2020 2021 2022 2023 2024 2025

I4 DOHC to I3 18 bar $427 $423 $359 $356 $352 $348 $344 $340 $337

I4 DOHC to I3 24 bar $690 $681 $654 $647 $639 $632 $624 $617 $551

I4 DOHC to I3 27 bar $1,214 $1,199 $1,164 $1,149 $1,134 $1,120 $1,106 $1,092 $979

I4 DOHC to I4 18 bar $482 $476 $421 $415 $410 $404 $399 $393 $388

I4 DOHC to I4 24 bar $744 $734 $716 $707 $697 $688 $679 $670 $602

I4 DOHC to I4 27 bar $1,269 $1,251 $1,226 $1,209 $1,192 $1,176 $1,160 $1,145 $1,031

V6 DOHC to I4 18 bar $248 $250 $157 $159 $161 $163 $165 $167 $169

V6 DOHC to I4 24 bar $510 $508 $452 $450 $449 $447 $445 $444 $383

V6 DOHC to I4 27 bar $1,035 $1,026 $962 $953 $944 $935 $927 $918 $811

V6 SOHC 2V to 

I4 18 bar $331 $330 $251 $251 $250 $249 $248 $248 $247

V6 SOHC 2V to 

I4 24 bar $594 $589 $546 $542 $537 $533 $529 $524 $461

V6 SOHC 2V to

I4 27 bar $1,119 $1,106 $1,056 $1,044 $1,032 $1,021 $1,010 $999 $890

V6 OHV to I4 18 bar $914 $898 $815 $799 $784 $770 $758 $746 $735

V6 OHV to I4 24 bar $1,177 $1,156 $1,110 $1,090 $1,072 $1,053 $1,038 $1,023 $949

V6 OHV to I4 27 bar $1,701 $1,674 $1,619 $1,593 $1,567 $1,542 $1,519 $1,498 $1,378

V8 DOHC to I4 18 bar $1 $18 -$88 -$72 -$56 -$41 -$34 -$27 -$19

V8 DOHC to I4 24 bar $264 $277 $207 $219 $231 $243 $246 $250 $195

V8 DOHC to I4 27 bar $789 $794 $716 $722 $726 $731 $728 $725 $623

V8 DOHC to V6 18 bar $746 $738 $635 $628 $620 $613 $606 $599 $592

V8 DOHC to V6 24 bar $1,188 $1,174 $1,132 $1,118 $1,105 $1,092 $1,078 $1,066 $953

V8 DOHC to V6 27 bar $2,073 $2,045 $1,991 $1,965 $1,940 $1,914 $1,890 $1,866 $1,675
 Technologies Considered in the Agencies’ Analysis

3-95 

V8 SOHC 2V to 

I4 18 bar $123 $134 $41 $52 $62 $72 $76 $80 $84

V8 SOHC 2V to 

I4 24 bar $385 $392 $336 $343 $350 $356 $357 $357 $298

V8 SOHC 2V to 

I4 27 bar $910 $910 $846 $845 $845 $844 $838 $832 $727

V8 SOHC 3V to 

I4 18 bar $77 $90 -$8 $5 $18 $29 $35 $40 $45

V8 SOHC 3V to 

I4 24 bar $339 $349 $287 $296 $305 $313 $315 $317 $259

V8 SOHC 3V to 

I4 27 bar $864 $866 $797 $799 $800 $801 $796 $791 $688

V8 SOHC 2V to 

V6 18 bar $842 $831 $744 $733 $723 $712 $702 $692 $682

V8 SOHC 2V to 

V6 24 bar $1,284 $1,267 $1,241 $1,224 $1,207 $1,191 $1,175 $1,159 $1,043

V8 SOHC 2V to 

V6 27 bar $2,169 $2,138 $2,100 $2,071 $2,042 $2,014 $1,986 $1,959 $1,766

V8 SOHC 3V to 

V6 18 bar $806 $796 $703 $693 $684 $675 $666 $657 $648

V8 SOHC 3V to 

V6 24 bar $1,248 $1,232 $1,200 $1,184 $1,169 $1,153 $1,138 $1,124 $1,010

V8 SOHC 3V to 

V6 27 bar $2,133 $2,103 $2,059 $2,031 $2,003 $1,976 $1,950 $1,924 $1,732

V8 OHV to I4 18 bar $377 $376 $312 $311 $311 $310 $307 $304 $302

V8 OHV to I4 24 bar $639 $635 $607 $602 $598 $594 $587 $581 $516

V8 OHV to I4 27 bar $1,164 $1,152 $1,116 $1,105 $1,093 $1,082 $1,069 $1,056 $944

V8 OHV to V6 18 bar $1,339 $1,316 $1,194 $1,172 $1,151 $1,131 $1,113 $1,096 $1,080

V8 OHV to V6 24 bar $1,781 $1,752 $1,691 $1,663 $1,636 $1,609 $1,586 $1,563 $1,441

V8 OHV to V6 27 bar $2,666 $2,623 $2,550 $2,510 $2,471 $2,432 $2,397 $2,363 $2,163

All costs are total costs (Direct manufacturing costs + Indirect costs); all costs are incremental to the 

baseline; all resultant engines are DOHC; note that costs are shown for 27 bar BMEP engines with V6 

engines. In fact, the agencies do not believe that manufacturers will employ 27 bar BMEP technology on V6 

engines to comply with the final standards, instead using the additional boost to allow for downsizing V6 

engines to smaller I4 engines than would be used for 18 bar BMEP or 24 bar BMEP I4 engines and/or 

downsizing V8 engines to I4 engines. As a result, whenever a 27 bar BMEP engine is chosen by either 

agency’s model, the engine configuration will be an I4 and will include cooled EGR, as discussed in section 

3.4.1.8.

3.4.1.9 Cooled Exhaust-Gas Recirculation (EGR) 

While not considered in the technology packages used for assessing potential 

compliance pathways in the 2012-2016 light-duty rule, the agencies have considered an 

emerging technology referred to as cooled exhaust gas recirculation (cooled-EGR) as applied 

to downsized, turbocharged GDI engines. In the 2010 TAR, the agencies considered this 

technology as an advanced gasoline technology since it was considered an emerging and not 

yet available technology in the light-duty gasoline market. While a cooled or “boosted” EGR 

technology was discussed in the 2012-2016 light-duty rule record, the technology considered 

here is comparatively more advanced as described in the 2010 TAR. As such, the agencies 

have considered new costs and new effectiveness values for it. The effectiveness values used 

for vehicle packages with cooled EGR within this analysis reflect a conservative estimate of 

system performance at approximately 24-bar BMEP. Vehicle simulation modeling of 

technology packages using the more highly boosted and downsized cooled EGR engines (up 
 Technologies Considered in the Agencies’ Analysis

3-96 

to 27-bar BMEP, and utilizing EGR rates of 20-25%) with dual-stage turbocharging has been 

completed as part of EPA’s contract with Ricardo Engineering as described in 3.3.1.2. For 

this FRM, consistent with the proposal, the agencies have updated the effectiveness of vehicle 

packages with cooled EGR using the new Ricardo vehicle simulation modeling runs. 

Cooled exhaust gas recirculation or Boosted EGR is a combustion concept that 

involves utilizing EGR as a charge dilutent for controlling combustion temperatures and 

cooling the EGR prior to its introduction to the combustion system. Higher exhaust gas 

residual levels at part load conditions reduce pumping losses for increased fuel economy. The 

additional charge dilution enabled by cooled EGR reduces the incidence of knocking 

combustion and obviates the need for fuel enrichment at high engine power. This allows for 

higher boost pressure and/or compression ratio and further reduction in engine displacement 

and both pumping and friction losses while maintaining performance. Engines of this type 

use GDI and both dual cam phasing and discrete variable valve lift. The EGR systems 

considered in this final rule, consistent with the proposal, would use a dual-loop system with 

both high and low pressure EGR loops and dual EGR coolers. The engines would also use 

single-stage, variable geometry turbocharging with higher intake boost pressure available 

across a broader range of engine operation than conventional turbocharged SI engines. Such a 

system is estimated to be capable of an additional 3 to 5 percent effectiveness relative to a 

turbocharged, downsized GDI engine without cooled-EGR.47,48

 The agencies have also 

considered a more advanced version of such a cooled EGR system that employs very high 

combustion pressures by using dual stage turbocharging. This modeling work has been 

completed by Ricardo Engineering. The simulation modeling is similar to work that Ricardo 

conducted for EPA for its 2008 staff report on GHG effectiveness of light-duty vehicle 

technologies.49 The agencies have considered this more advanced cooled EGR approach for 

this final rule, consistent with the proposal. 

For the MYs 2012-2016 final rule and TAR, NHTSA and EPA assumed a 5 

percent fuel consumption effectiveness for cooled EGR compared to a conventional 

downsized DI turbocharged engine. 50 Based on the data from the Ricardo and Lotus 

reports, NHTSA and EPA estimate the incremental reduction in fuel consumption for 

EGR Boost to be 5 percent over a turbocharged and downsized DI engine. Thus, if 

cooled EGR is applied to 24-bar engine, adding the 19.3 percent from the turbocharging 

and downsizing to the 5 percent gain from cooled EGR results in total fuel consumption 

reduction of 22.1 percent. This is in agreement with the range suggested in the Lotus 

and Ricardo reports.

In the 2010 TAR, the agencies estimated the DMC of the cooled EGR system at $240 

(2007$, see 2010 TAR at page B-12)). This DMC becomes $244 (2010$) for this analysis. 

This DMC is considered applicable in the 2012MY. The agencies consider cooled EGR 

technology to be on the flat portion of the learning curve and have applied a medium 

complexity ICM of 1.39 through 2024 then 1.29 thereafter. The resultant costs are shown in 

Table 3-34. 

Table 3-34 Costs for Cooled EGR (2010$) 

Cost 

type Engine type 2017 2018 2019 2020 2021 2022 2023 2024 2025
 Technologies Considered in the Agencies’ Analysis

3-97 

DMC All $212 $208 $204 $199 $195 $192 $188 $184 $180

IC All $93 $93 $93 $93 $92 $92 $92 $92 $69

TC All $305 $301 $296 $292 $288 $284 $280 $276 $249

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are 

incremental to the baseline.

Note that, in the 2010 TAR, the agencies presented the cooled EGR system costs 

inclusive of turbo charging costs (see 2010 TAR, Table B2.2-1 at page B-12). For this 

analysis, the agencies are presenting the cooled EGR costs as a stand-alone technology that 

can be added to any turbo/downsized engine provided sufficient boost is provided and 

sufficient engine robustness is accounted for. As such, the cooled EGR system is considered 

applicable only the 24 bar BMEP and 27 bar BMEP engines. Further, the agencies believe 

that 24 bar BMEP engines are capable of maintaining NOx control without cooled EGR, so 

each agency’s respective models may choose 24 bar BMEP engines with and/or without 

cooled EGR. However, as noted above, 27 bar BMEP engines are considered to require 

cooled EGR to maintain NOx emission control. As such, neither agency’s model is allowed 

to choose 27 bar BMEP technology without also adding cooled EGR. 

3.4.1.10 Diesel Engine Technology (DSL) 

Diesel engines have several characteristics that give them superior fuel efficiency 

compared to conventional gasoline, spark-ignited engines. Pumping losses are much lower 

due to lack of (or greatly reduced) throttling in a diesel engine. The diesel combustion cycle 

operates at a higher compression ratio than does a gasoline engine. As a result, turbocharged 

light-duty diesels typically achieve much higher torque levels at lower engine speeds than 

equivalent-displacement naturally-aspirated gasoline engines. Future high BMEP 

turbocharged and downsized engines, mentioned above, are projected to improve torque 

levels at lower engine speeds thus reducing the diesel advantage in this area. Diesels also 

operate with a very lean air/fuel mixture. These attributes – reduced pumping losses, higher 

compression ratio and lean/air fuel mixture -- allow the engine to extract more energy from a 

given mass of fuel than a gasoline engine, and thus make it more efficient. Additionally, 

diesel fuel has higher energy content per gallon than does gasoline. While diesel fuel has a 

higher energy content than gasoline, it also contains more carbon per gallon than does 

gasoline: diesel produces 22.2 pounds of CO2 per gallon when burned, while gasoline 

produces 19.4 pounds of CO2 per gallon. This higher carbon content slightly offsets the GHG 

emissions benefit of diesel fuel relative to gasoline, however, the disbenefit is more than 

compensated by the greater efficiency of the diesel engine. Since diesel engines are more fuel 

efficient than current naturally aspirated PFI gasoline engines, the agencies anticipate that 

manufacturers will evaluate and potentially invest in diesel engine production as a way to 

comply with more stringent CAFE standards. However, there are two primary reasons why 

manufacturers might not choose to invest significantly in diesel engine technologies as a way 

to comply with the CAFE and GHG standards for MYs 2017-2025. 

As discussed above, even though diesel has higher energy content than gasoline it also 

has a higher carbon density that results in higher amounts of CO2 emitted per gallon, 

approximately 15 percent more than a gallon of gasoline. This is commonly referred to as the 

“carbon penalty” associated with using diesel fuel – a diesel vehicle yields greater fuel 
 Technologies Considered in the Agencies’ Analysis

3-98 

economy improvements compared to its CO2 emissions reduction improvements, so a 

manufacturer that invests in diesel technology to meet CAFE standards may have more 

trouble meeting the GHG standards than if it used a different and more cost effective (from a 

GHG perspective) technology. 

And second, diesel engines also have emissions characteristics that present challenges 

to meeting federal Tier 2 NOx emissions standards. By way of comparison for readers 

familiar with the European on-road fleet, which contains many more diesel vehicles than the 

U.S. on-road fleet, U.S. Tier 2 emissions fleet average requirement of bin 5 require roughly 

45 to 65 percent more NOx reduction compared to the Euro VI standards. 

Despite considerable advances by manufacturers in developing Tier 2-compliant diesel 

engines, it remains somewhat of a systems-engineering challenge to maintain the full fuel 

consumption advantage of the diesel engine while meeting Tier 2 emissions regulations 

because some of the emissions reduction strategies can increase fuel consumption (relative to 

a Tier 1 compliant diesel engine), depending on the combination of strategies employed. A 

combination of combustion improvements (that reduce NOX emissions leaving the engine) 

and aftertreatment (capturing and reducing NOX emissions via a NOx adsorption catalyst, or 

via selective catalytic reduction (SCR) using a reductant such as urea) that have left the 

engine before they leave the vehicle tailpipe) are being introduced on Tier 2 compliant lightduty diesel vehicles today. However, recently there have been a small number of 

announcements that diesel engines will be added to some passenger cars, in some cases a 

segment first for a manufacturer51, or that new passenger car diesel engines are being 

designed to meet all global emissions regulations.52 This suggests to the agencies that some 

manufacturers may be planning to use diesel engines in their plans to meet the tighter CAFE 

standards in the mid-term, which may be enabled by advances in diesel engine and emission 

control technology. Manufacturers that focus on diesel engines have also stated to the 

agencies their expectation that diesel engines will continue to be a viable technology for 

improving fuel economy and GHG emissions in the future. 

We spend time here discussing available emissions reduction technologies for diesel 

engines as part of this rulemaking because of the potential they have to impact fuel economy 

and GHG emissions for the vehicles that have them. With respect to combustion 

improvements, we note that several key advances in diesel engine combustion technology 

have made it possible to reduce emissions coming from the engine prior to aftertreatment, 

which reduces the need for aftertreatment. These technologies include improved fuel systems 

(higher injection pressure and multiple-injection capability), advanced controls and sensors to 

optimize combustion and emissions performance, higher EGR levels and EGR cooling to 

reduce NOx, and advanced turbocharging systems. These systems are available today and 

they do not adversely impact fuel efficiency. However, additional improvements in these 

technologies will be needed to reduce engine emissions further, should future emissions 

standards become more stringent. Further development may also be needed to reduce the fuel 

efficiency penalty associated with EGR. 

With respect to catalytic exhaust emission control systems, typical 3-way exhaust 

catalysts without NOx storage capability are not able to reduce NOx emissions from engines 

operated lean of stoichiometry (diesel or lean-burn gasoline). To reduce NOx, hydrocarbons, 
 Technologies Considered in the Agencies’ Analysis

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and particulate emissions, all diesels will require a catalyzed diesel particulate filter (CDPF) 

and sometimes a separate diesel oxidation catalyst (DOC), and either a lean NOx trap (LNT)

kk or the use of a selective catalytic reduction system, typically base-metal zeolite urea-SCRll

. 

The increased cost of diesel emissions control technologies relative to powertrains 

with stoichiometric gasoline engines that are approaching comparable efficiency may also 

make diesels less attractive to manufacturers as a technology solution for more stringent 

CAFE and GHG standards. However, recognizing that some manufacturers may still employ 

diesel technology to meet the future standards, the agencies have included diesels in our 

analysis as follows: 

The agencies sought to ensure that diesel engines would have equivalent performance 

to comparable gasoline engine vehicles. For the Subcompact, Compact, and Midsize 

Passenger Car, Performance Subcompact Car, and Small Light Truck vehicle subclasses, the 

agencies assumed that an I4 gasoline base engine would be replaced by an in-line 4-cylinder 

diesel engine with displacement varying around 2.0 liters. For the Performance Compact, 

Performance Midsize, Large Passenger Car, Minivan, and Midsize Truck vehicle subclasses 

for the CAFE model, the agencies assumed that a V6 gasoline base engine would be replaced 

by an in-line 4-cylinder diesel engine with displacement varying around 2.8 liters. For the 

Large Truck and Performance Large Car vehicle subclasses for the CAFE model, the agencies 

assumed that a V8 gasoline base engine would be replaced with a V6 diesel engine with 

displacement varying around 4.0 liters to meet vehicle performance requirements. It was also 

assumed that diesel engines for all of these classes would utilize SCR aftertreatment systems 

given recent improvements in zeolite-based SCR systems and system efficiency. These 

assumptions impacted our estimates of the costs of implementing diesel engines as compared 

to the base gasoline engines. 

 

kk A lean NOx trap operates by oxidizing NO to NO2 in the exhaust and storing NO2 on alkali sorbent material, 

most often BaO. When the control system determines (via mathematical model and typically a NOx sensor) that 

the trap is saturated with NOx, it switches the engine into a operating mode just rich of stoichiometry that allow 

NOx to be released from the alkali storage and temporarily allow three-way function of the catalyst similar to 

three-way catalysts used in stoichiometric gasoline applications. LNTs preferentially store sulfate compounds 

from the fuel, which reduces NOx storage capacity over time, thus the system must undergo periodic 

desulfurization by operating at a net-fuel-rich condition at high temperatures in order to retain NOx trapping 

efficiency.

ll An SCR aftertreatment system uses a reductant (typically, ammonia derived from urea) that is injected into the 

exhaust stream ahead of the SCR catalyst. Ammonia is a strong reductant even under net lean conditions. It 

combines with NOx in the SCR catalyst to form N2 and water. The hardware configuration for an SCR system is 

sometimes more complicated than that of an LNT, due to the onboard urea storage and delivery system (which 

requires a urea pump and injector to inject urea into the exhaust stream), which generally makes an SCR system 

cost more than an LNT system. While a rich engine-operating mode is not required for NOx reduction, the urea 

is typically injected at a rate of approximately 3 percent of the fuel consumed. The agencies understand that 

manufacturers designing SCR systems intend to align urea tank refills with standard maintenance practices such 

as oil changes as more diesel vehicles are introduced into the market. For diesel vehicles currently on the 

market, this is generally already the practice, and represents an ongoing maintenance cost for vehicles with this 

technology.

 
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Diesel engines are more costly than port-injected spark-ignition gasoline engines. 

These higher costs result from more costly components, more complex systems for emissions 

control, and other factors. The vehicle systems that are impacted include: 

Fuel systems (higher pressures and more responsive injectors); 

Controls and sensors to optimize combustion and emissions performance; 

Engine design (higher cylinder pressures require a more robust engine, but higher 

torque output means diesel engines can have reduced displacement); 

Turbocharger(s); 

Aftertreatment systems, which tend to be more costly for diesels; 

In the MYs 2012-2016 final rule, the agencies estimated the DMC for converting a 

gasoline PFI engine with 3-way catalyst aftertreatment to a diesel engine with diesel 

aftertreatment at $1,697 (2007$), $2,399 (2007$), $1,956 (2007$) and $2,676 (2007$) for a 

small car, large car, medium/large MPV & small truck, and large truck, respectively (see final 

Joint TSD, Table 3-12 at page 3-44). All of these costs were for SCR-based diesel systems, 

with the exception of the small car, which was a LNT-based system. For this final rule, 

consistent with the proposal, we are using the same methodology as used in the MYs 2012-

2016 final rule, but have made four primary changes to the cost estimates as was also done in 

the proposal for this rule. First, the agencies have not estimated costs for a LNT-based 

system, and instead have estimated costs for all vehicle types assuming they will employ 

SCR-based systems. Second, the agencies assumed that manufacturers would meet a Tier 2 

bin 2 average rather than a Tier 2 bin 5 average, assuming that more stringent levels of 

compliance will be required in the future. In order to estimate costs for Tier 2 bin 2 compliant 

vehicles, catalyst volume costs were estimated based on an assumed increase in volume of 20 

percent. This was the estimated necessary increase needed to meet Tier 2, bin 2 emission 

level of 0.02 grams of NOx per mile. Increased catalyst volume resulted in a higher cost 

estimate for diesel aftertreatment than was estimated for the MYs 2012-2016 final rule. The 

third is to update all platinum group metal costs from the March 2009 values used in the 

2012-2016 final rule to February 2011 values.mm

 The February 2011 values were used for 

purposes of the NPRM analysis, at which time they represented the most recent monthly 

average prices available at the time the agencies “locked-down” all cost estimates for the 

purposes of moving into the modeling phase of analysis.nn For the final rule analysis, the 

 

mm As reported by Johnson-Matthey, the March 2009 monthly average costs were $1,085 per Troy ounce and 

$1,169 per Troy ounce for platinum (Pt) and rhodium (Rh), respectively. As also reported by Johnson-Matthey, 

the February 2011 monthly average costs were $1,829 per Troy ounce and $2,476 per Troy ounce for Pt and Rh, 

respectively. See www.platinum.matthey.com. 

nn Note that there is no good way of determining what PGM prices to use when conducting cost analyses. Spot 

prices are inherently dangerous to use because spot prices, like stock prices on the stock market, can vary 

considerably from day to day. One could argue that an average price is best, but average prices can vary 

considerably depending on the length of time included in the average. And if too much time is included in the 

average, then average prices from a time prior to PGM use in diesel engines may be included which would lead 

some to conclude that we had cherry picked our values. Given no good option, it seems most transparent and 
 Technologies Considered in the Agencies’ Analysis

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agencies did not update the cost for platinum group metals. The fourth is to include an 

additional $50 DMC for all costs to cover costs associated with improvements to fuel and urea 

controls. All of the diesel costs are considered applicable to MY 2012. The agencies 

consider diesel technology to be on the flat portion of the learning curve and have applied a 

medium complexity ICM of 1.39 through 2018, and then an ICM of 1.29 thereafter. The 

resultant costs are shown in Table 3-35. 

Table 3-35 Costs for Conversion to Advanced Diesel (2010$) 

Cost type Vehicle class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car $2,059 $2,018 $1,978 $1,938 $1,900 $1,862 $1,824 $1,788 $1,752

DMC Standard car $2,059 $2,018 $1,978 $1,938 $1,900 $1,862 $1,824 $1,788 $1,752

DMC Large car $2,522 $2,472 $2,422 $2,374 $2,326 $2,280 $2,234 $2,190 $2,146

DMC Small MPV $2,064 $2,023 $1,982 $1,943 $1,904 $1,866 $1,828 $1,792 $1,756

DMC Large MPV $2,082 $2,040 $1,999 $1,959 $1,920 $1,882 $1,844 $1,807 $1,771

DMC Large truck $2,886 $2,828 $2,772 $2,716 $2,662 $2,609 $2,556 $2,505 $2,455

IC Small car $905 $903 $675 $674 $673 $672 $671 $669 $668

IC Standard car $905 $903 $675 $674 $673 $672 $671 $669 $668

IC Large car $1,109 $1,106 $827 $826 $824 $823 $821 $820 $819

IC Small MPV $907 $905 $677 $676 $674 $673 $672 $671 $670

IC Large MPV $915 $913 $683 $681 $680 $679 $678 $677 $676

IC Large truck $1,268 $1,266 $946 $945 $943 $941 $940 $938 $937

TC Small car $2,965 $2,922 $2,653 $2,612 $2,572 $2,533 $2,495 $2,457 $2,420

TC Standard car $2,965 $2,922 $2,653 $2,612 $2,572 $2,533 $2,495 $2,457 $2,420

TC Large car $3,631 $3,578 $3,249 $3,200 $3,151 $3,103 $3,056 $3,010 $2,964

TC Small MPV $2,971 $2,928 $2,659 $2,618 $2,578 $2,539 $2,501 $2,463 $2,426

TC Large MPV $2,996 $2,953 $2,682 $2,641 $2,600 $2,561 $2,522 $2,484 $2,446

TC Large truck $4,154 $4,094 $3,718 $3,661 $3,605 $3,550 $3,496 $3,443 $3,392

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the baseline.

For the MYs 2012-16 final rule and TAR, NHTSA and EPA estimated the fuel 

consumption reduction of a SCR-based diesel engine to be between 20 to 25 percent over a 

baseline gasoline engine. NHTSA and EPA have revisited these values and have now 

estimated, based on the Ricardo 2011 study, the effectiveness of a SCR-based diesel engine to 

be 28.4 to 30.5 percent. For purposes of CO2 reduction, EPA estimates a 7 to 20 percent for 

light-duty diesels equipped with SCR.

3.4.2 Transmission Technologies 

NHTSA and EPA have also reviewed the transmission technology estimates used in 

the 2012-2016 final rule and the 2010 TAR. In doing so, NHTSA and EPA considered or 

reconsidered all available sources and updated the estimates as appropriate. The section 

below describes each of the transmission technologies considered for this rulemaking. As 

discussed above, for the final rule NHTSA has updated the effectiveness values for advanced 

transmissions when coupled to naturally-aspirated engines based on the ANL simulation 

 

least self-serving to simply choose a price and report its basis. In the end, the PGM costs represent 16-23 

percent of the diesel DMC in this analysis. Further, diesels play very little to no role in enabling compliance 

with the final standards. 
 Technologies Considered in the Agencies’ Analysis

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modeling. These changes are documented in detail in NHTSA’s RIA. These changes are not 

included in this joint TSD because they are specific to NHTSA’s analysis only. 

3.4.2.1 Improved Automatic Transmission Control (Aggressive Shift Logic and Early 

Torque Converter Lockup) 

Calibrating the transmission shift schedule to upshift earlier and quicker, and to lockup or partially lock-up the torque converter under a broader range of operating conditions can 

reduce fuel consumption and CO2 emissions. However, this operation can result in a 

perceptible degradation in noise, vibration, and harshness (NVH). The degree to which NVH 

can be degraded before it becomes noticeable to the driver is strongly influenced by 

characteristics of the vehicle, and although it is somewhat subjective, it always places a limit 

on how much fuel consumption can be improved by transmission control changes. 

Aggressive Shift Logic and Early Torque Converter Lockup are best optimized 

simultaneously when added to an automatic transmission due to the fact that adding both of 

them requires only minor modifications to the transmission mechanical components or 

calibration software. As a result, these two technologies are combined in the modeling when 

added to an automatic transmission. Since a dual clutch transmission (DCT) has no torque 

converter, the early torque converter lockup technology is not included when adding ASL to 

the DCT. 

3.4.2.2 Aggressive Shift Logic 

During operation, a transmission’s controller manages the operation of the 

transmission by scheduling the upshift or downshift, and, in automatic transmissions, locking 

or allowing the torque converter to slip based on a preprogrammed shift schedule. The shift 

schedule contains a number of lookup table functions, which define the shift points and torque 

converter lockup based on vehicle speed and throttle position, and other parameters such as 

temperature. Aggressive shift logic (ASL) can be employed in such a way as to maximize 

fuel efficiency by modifying the shift schedule to upshift earlier and inhibit downshifts under 

some conditions, which reduces engine pumping losses and engine friction. The application 

of this technology does require a manufacturer to confirm that drivability, durability, and 

NVH are not significantly degraded. 

For this final rule, consistent with the proposal, the agencies considered two levels of 

ASL. The first level is that discussed in the 2012-2016 final rule and the 2010 TAR. ASLlevel 1 is an early upshift strategy whereby the transmission shifts to the next higher gear 

“earlier” (or at lower RPM during a gradual acceleration) than would occur in a traditional 

automatic transmission. This early upshift reduces fuel consumption by allowing the engine 

to operate at a lower RPM and higher load, which typically moves the engine into a more 

efficient operating region. 

ASL-level 2 is a shift optimization strategy whereby the engine and/or transmission 

controller(s) continuously evaluate all possible gear options that would provide the necessary 

tractive power (while limiting the adverse effects on driveline NVH) and select the gear that 

lets the engine run in the most efficient operating zone. Ricardo acknowledged in its report 

that the ASL-level 2 (“shift optimization”) strategy currently causes significant implications 
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for drivability and hence affects consumer acceptability. However, Ricardo recommended the 

inclusion of this technology for the 2020-2025 timeframe with the assumption that 

manufacturers will develop a means of yielding the fuel economy benefit without adversely 

affecting driver acceptability. The agencies believe these drivability challenges could include 

shift busyness – that is, a high level of shifting compared to current vehicles as perceived by 

the customers. The agencies note that in confidential discussions with two major transmission 

suppliers, the suppliers described transmission advances which reduce shifting time and 

provide smoother torque transitions than today’s designs, making the shifting event less 

apparent to the driver, however these improvements will not influence the customer’s 

perception of shift business related to the changes in engine speed. 

In addition, the agencies note that several auto companies and transmission firms have 

announced future introduction of transmissions into the U.S. market with even a higher 

number of gears than were included in the Ricardo simulation and in the agencies’ feasibility 

assessment for this final rule, consistent with the proposal (which is 8 forward speeds). These 

announcements include both 9 and 10 speed transmissions which may present further 

challenges with shift busyness, given the availability of one or two additional gears. At the 

same time, the associated closer gear spacing will generally result in smaller engine speed 

changes during shifting that may be less noticeable to the driver. 

The agencies are including shift optimization in the analysis under the premise that 

manufacturers and suppliers are developing means to mitigate these drivability issues by MY 

2017, as assumed in the 2011 Ricardo study (more information on Ricardo’s treatment of the 

optimized shift strategy is described in Section 6.4 of the 2011 Ricardo report). If 

manufacturers are not able to solve these drivability issues, the assumed effectiveness could 

be lower and the cost could be higher or both. The agencies sought comment on the feasibility 

of ASL-level 2 and the likelihood that manufacturers will be able to overcome the drivability 

issues, however no comments were submitted on this issue. 

In MYs 2012-2016 final rule, the agencies estimated an effectiveness improvement of 

1 to 2 percent for aggressive shift logic which was supported by the 2002 NAS and 

NESCCAF reports as well as confidential manufacturer data. The agencies updated the 

effectiveness of ASL-level 1 ranging from 1.9 to 2.7 based on 2010 Ricardo study. In CAFE 

model an incremental effectiveness ranging for both ASL and early torque converter lockup 

ranging from 2.3 to 3.1 percent is applied (Early torque converter has effectiveness of 0.5 

percent). 

ASL-level 2 is new to this analysis which is based on the shift optimization algorithm 

in 2011 Ricardo study. The effectiveness for ASL-level 2 ranges from 5.1 to 7.0 percent 

improvement over transmission with unimproved shift logic or roughly 4 to 5 percent over a 

transmission that already incorporates aggressive shift logic. In the CAFE model, an 

incremental effectiveness ranging from 3.27 to 4.31 percent is applied. 

In the 2012-2016 rule, the agencies estimated the DMC at $26 (2007$) which was 

considered applicable to the 2015MY. This DMC becomes $27 (2010$) for this analysis. 

The agencies consider ASL-level 1 technology to be on the flat portion of the learning curve 

and have applied a medium complexity ICM of 1.39 through 2018 then 1.29 thereafter. For 
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ASL-level 2, the agencies are estimating the DMC at an equivalent $27 (2010$) except that 

this cost is considered applicable to the 2017MY. Essentially this yields a nearly negligible 

incremental cost for ASL-level 2 over ASL-level 1. The agencies consider ASL-level 2 

technology to be on the flat portion of the learning curve and have applied a medium 

complexity ICM of 1.39 through 2024 then 1.29 thereafter. The timing of the ASL-level 2 

ICMs is different than that for the level 1 technology because the level 2 technology is newer 

and not yet being implemented in the fleet. The resultant costs are shown in Table 3-36. 

Note that both levels of ASL technology are incremental to the baseline system, so ASL-level 

2 is not incremental to ASL-level 1. 

Table 3-36 Costs for Aggressive Shift Logic Levels 1 & 2 (2010$) 

Cost type Technology Transmission 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC ASL-level 1 All $26 $26 $25 $24 $24 $24 $23 $23 $22

DMC ASL-level 2 All $27 $27 $26 $25 $24 $24 $23 $23 $22

IC ASL-level 1 All $7 $7 $5 $5 $5 $5 $5 $5 $5

IC ASL-level 2 All $7 $7 $7 $7 $7 $7 $7 $7 $5

TC ASL-level 1 All $33 $32 $30 $30 $29 $29 $28 $28 $27

TC ASL-level 2 All $34 $33 $32 $32 $31 $30 $30 $29 $27

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the baseline.

 

3.4.2.3 Early Torque Converter Lockup 

A torque converter is a fluid coupling located between the engine and transmission in 

vehicles with automatic transmissions and continuously-variable transmissions (CVT). This 

fluid coupling allows for slip so the engine can run while the vehicle is idling in gear (as at a 

stop light), provides for smoothness of the powertrain, and also provides for torque 

multiplication during acceleration, and especially launch. During light acceleration and 

cruising, the inherent slip in a torque converter causes increased fuel consumption, so modern 

automatic transmissions utilize a clutch in the torque converter to lock it and prevent this 

slippage. Fuel consumption can be further reduced by locking up the torque converter at 

lower vehicle speeds, provided there is sufficient power to propel the vehicle, and noise and 

vibration are not excessive.oo If the torque converter cannot be fully locked up for maximum 

efficiency, a partial lockup strategy can be employed to reduce slippage. Early torque 

converter lockup is applicable to all vehicle types with automatic transmissions. Some torque 

converters will require upgraded clutch materials to withstand additional loading and the 

slipping conditions during partial lock-up. As with aggressive shift logic, confirmation of 

acceptable drivability, performance, durability and NVH characteristics is required to 

successfully implement this technology. 

 

oo Although only modifications to the transmission calibration software are considered as part of this technology, 

very aggressive early torque converter lock up may require an adjustment to damper stiffness and hysteresis 

inside the torque converter. 
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Regarding the effectiveness of Early Torque Converter Lockup, the 2012-2016 final 

rule, TAR, and the 2010 Ricardo study estimated an effectiveness improvement of 0.4 to 0.5 

percent. 

In the 2012-2016 rule, the agencies estimated the DMC at $24 (2007$) which was 

considered applicable to the 2015MY. This DMC remains $25 (2010$) for this analysis.pp

 

The agencies consider early torque converter lockup technology to be on the flat portion of 

the learning curve and have applied a low complexity ICM of 1.24 through 2018 then 1.19 

thereafter. The resultant costs are shown in Table 3-37.

Table 3-37 Costs for Early Torque Converter Lockup (2010$) 

Cost type Transmission 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Automatic $24 $23 $23 $22 $22 $21 $21 $20 $20

IC Automatic $6 $6 $5 $5 $5 $5 $5 $5 $5

TC Automatic $30 $29 $27 $27 $27 $26 $26 $25 $25

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to 

the baseline.

3.4.2.4 High Efficiency Gearbox 

For this rule, a high efficiency gearbox refers to some or all of a suite of incremental 

gearbox improvement technologies that should be available within the 2017 to 2025 

timeframe. The majority of these improvements address mechanical friction within the 

gearbox. These improvements include but are not limited to: shifting clutch technology 

improvements (especially for smaller vehicle classes), improved kinematic design, dry sump 

lubrication systems, more efficient seals, bearings and clutches (reducing drag), component 

superfinishing and improved transmission lubricants. More detailed description can be found 

in the 2011 Ricardo report53. Note that the high efficiency gearbox technology is applicable 

to any type of transmission. 

EPA analyzed detailed transmission efficiency input data provided by Ricardo and 

implemented it directly into the lumped parameter model. Based on the LP effectiveness 

resulting from these inputs, EPA and NHTSA estimate that a high efficiency gearbox can 

provide a GHG or fuel consumption reduction in the range of 3.8 to 5.7 percent (3.8% for 

4WD trucks with an unimproved rear axle) over a baseline automatic transmission in 

MY2017 and beyond. 

The agencies estimate the DMC of the high efficiency gearbox at $200 (2009$). We 

have based this on the DMC for engine friction reduction in a V8 engine which, as presented 

in Table 3-24 is $197 (2010$). In the proposal, we rounded this value up to $200 (2009$) 

 

pp As is true throughout this presentation of cost estimates, the agencies round costs to the nearest dollar. In the 

actual model input files, the cost in 2007$ would have been $23.68 and the cost in 2009$ is $24.42. So an 

impact of the dollar-year conversion is reflected in the analysis even when it does not appear so in this 

presentation. 
 Technologies Considered in the Agencies’ Analysis

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which becomes $202 (2010$) for the final analysis. This DMC is considered applicable for 

the 2017MY. The agencies consider high efficiency gearbox technology to be on the flat 

portion of the learning curve and have applied a low complexity ICM of 1.24 through 2024 

then 1.19 thereafter. The resultant costs are shown in Table 3-38. 

Table 3-38 Costs for High Efficiency Gearbox (2010$) 

Cost type Transmission 

type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Automatic/Dual 

clutch $202 $196 $190 $184 $179 $173 $170 $167 $163

IC Automatic/Dual 

clutch $49 $49 $49 $49 $49 $49 $48 $48 $39

TC Automatic/Dual 

clutch $251 $245 $239 $233 $227 $222 $218 $215 $202

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; all costs are incremental to the 

baseline.

3.4.2.5 Automatic 6-, 7- and 8-Speed Transmissions (NAUTO and 8SPD) 

Manufacturers can also choose to replace 4- and 5-speed transmission with 6-, 7-, or 

8-speed automatic transmissions. Additional ratios allow for further optimization of engine 

operation over a wider range of conditions, but this is subject to diminishing returns as the 

number of speeds increases. As additional planetary gear sets are added (which may be 

necessary in some cases to achieve the higher number of ratios), additional weight and friction 

are introduced. Also, the additional shifting of such a transmission can be perceived as 

bothersome to some consumers, so manufacturers need to develop strategies for smooth 

shifts. Some manufacturers are replacing 4- and 5-speed automatics with 6-speed automatics, 

and 7- and 8-speed automatics have also entered production. While a six speed transmission 

application was most prevalent for the 2012-2016 final rule, eight speed transmissions are 

expected to be readily available and applied in the 2017 through 2025 timeframe. 

As discussed in the MY 2011 CAFE final rule, confidential manufacturer data 

projected that 6-speed transmissions could incrementally reduce fuel consumption by 0 to 5 

percent from a baseline 4-speed automatic transmission, while an 8-speed transmission could 

incrementally reduce fuel consumption by up to 6 percent from a baseline 4-speed automatic 

transmission. GM has publicly claimed a fuel economy improvement of up to 4 percent for 

its new 6-speed automatic transmissions.54

 The 2008 EPA Staff Technical Report found a 4.5 

to 6.5 percent fuel consumption improvement for a 6-speed over a 4-speed automatic 

transmission.55 Based on this information, NHTSA estimated in the MY 2011 rule, that the 

conversion to a 6-,7- and 8-speed transmission (NAUTO) from a 4 or 5-speed automatic 

transmission with IATC would have an incremental fuel consumption benefit of 1.4 percent to 

3.4 percent, for all vehicle classes. From a baseline 4 or 5 speed transmission without IATC, 

the incremental fuel consumption benefit would be approximately 3 to 6 percent, which is 

consistent with the EPA Staff Report estimate. In MYs 2012-2016 final rule, NHTSA and 

EPA reviewed these effectiveness estimates and concluded that they remain accurate. While 

the CAFE model follows the incremental approach discussed above, the GHG model 

estimates the packaged effectiveness of 4.5 to 6.5 percent 
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In this FRM analysis, consistent with the proposal, the agencies divided the 

improvement for this technology into two steps, first from 4 or 5 speed transmission to 6 or 7 

speed transmission (NAUTO), then from 6 or 7 speed transmission to 8 speed transmission 

(8SPD). The effectiveness estimates for NAUTO and 8SPD are based on 2011 Ricardo study. 

In this FRM analysis, consistent with the proposal, the effectiveness for a 6-speed 

transmission relative to a 4-speed base transmission ranges from 3.1 to 3.9 percent (2.1 

percent for large truck with unimproved rear axle) including 7 percent of transmission 

gearbox efficiency improvement that the agencies assumed accompanying the new 6 speed 

transmission after MY 2010. NHTSA incorporated this effectiveness estimate into the CAFE 

model as incremental improvement over IATC ranging from 1.89 to 2.13 percent. In this 

FRM analysis, consistent with the proposal, the agencies assumed that 8-speed transmission 

will not start to phase in until MY2017. NHTSA applied 8-speed automatic transmission 

succeeding 6-speed automatic transmission to vehicles with towing requirement, such as 

Minivan, Midsize light truck and large light truck. All other vehicle subclasses use 8-speed 

DCT to succeed 6-speed DCT. The effectiveness for an 8-speed DCT relative to a 4-speed 

DCT transmission ranges from 11.1 to 13.1 percent for subcompact car, small car and small 

light truck. The effectiveness for an 8-speed automatic transmission relative to 4-speed 

automatic transmission ranges for large CUV and large truck ranges from 8.7 to 9.2 percent in 

the lumped parameter model. This translates into effectiveness in the range of 3.85 to 4.57 

percent for an 8-speed DCT relative to a 6-speed DCT and 4.9 to 5.34 percent for 8-speed 

automatic transmission relative to 6-speed automatic transmission in CAFE model. 

In the 2010 TAR, the agencies estimated the DMC at -$13 (2008$) for a 6 speed 

automatic transmission relative to a 4 speed auto transmission, applicable in the 2017MY (see 

2010 TAR, Table B2.1-1 at page B-10). For the 2012MY, that DMC was -$15 (2008$), 

although that value was not presented in the TAR. The latter DMC remains -$15 (2010$) for 

this analysis which is considered to be applicable in the 2012MY. The agencies consider 6 

speed automatic transmission technology to be on the flat portion of the learning curve and 

have applied a low complexity ICM of 1.24 through 2018 then 1.19 thereafter. The resultant 

costs are shown in Table 3-39.

New for the proposal was the cost of an 8 speed automatic transmission. For the cost 

of this technology, the agencies have relied on a tear-down study completed by FEV since 

publication of the TAR.56 In that study, the 8 speed auto transmission was found to be $62 

(2007$) more costly than the 6 speed auto transmission. This DMC becomes $64 (2010$) for 

this analysis. Adding the $64 (2010$) to the -$15 (2010$) DMC for a 6 speed relative to a 4 

speed, the 8 speed auto transmission relative to a 4 speed auto transmission would be $50 

(2010$). The agencies consider this DMC to be applicable to the 2012MY. The agencies 

consider the 8 speed auto transmission technology to be on the flat portion of the learning 

curve and have applied a medium complexity ICM of 1.39 through the 2018MY then 1.29 
 Technologies Considered in the Agencies’ Analysis

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thereafter.qq The resultant costs for both 6 speed and 8 speed auto transmissions are shown in 

Table 3-39. 

Table 3-39 Costs for 6 and 8 Speed Automatic Transmissions (2010$) 

Cost 

type Transmission type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC 6spAT from 4spAT -$13 -$13 -$12 -$12 -$12 -$12 -$11 -$11 -$11

DMC 8spAT from 6spAT $56 $55 $54 $53 $51 $50 $49 $48 $47

DMC 8spAT from 4spAT $43 $42 $41 $40 $40 $39 $38 $37 $37

IC 6spAT from 4spAT $4 $4 $3 $3 $3 $3 $3 $3 $3

IC 8spAT from 6spAT $25 $24 $18 $18 $18 $18 $18 $18 $18

IC 8spAT from 4spAT $19 $19 $14 $14 $14 $14 $14 $14 $14

TC 6spAT from 4spAT -$9 -$9 -$10 -$9 -$9 -$9 -$9 -$8 -$8

TC 8spAT from 6spAT $80 $79 $72 $71 $70 $69 $68 $67 $66

TC 8spAT from 4spAT $62 $61 $55 $54 $54 $53 $52 $51 $50

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; sp=speed; AT=automatic transmission

 Note that the cost for the 8 speed automatic transmission relative to the 6 speed 

automatic transmission is lower here than that used in the recent heavy-duty GHG rule. In 

that rule, we remained consistent with the proposal for that rule which carried an estimated 

DMC of $210 (2008$). That DMC was based on an estimate derived by NAS (see NAS 

2010, Table 7-10). For this final rule, consistent with the proposal, we have chosen to use the 

more recent DMC shown in Table 3-39 which is based on a tear-down analysis done by FEV. 

3.4.2.6 Dual Clutch Transmissions / Automated Manual Transmissions (DCTAM) 

An Automated Manual Transmission (AMT) is mechanically similar to a conventional 

manual transmission, but shifting and launch functions are automatically controlled by the 

electronics. There are two basic types of AMTs, single-clutch and dual-clutch (DCT). A 

single-clutch AMT is essentially a manual transmission with automated clutch and shifting. 

Because of shift quality issues with single-clutch designs, DCTs are far more common in the 

U.S. and are the basis of the estimates that follow. A DCT uses separate clutches (and 

separate gear shafts) for the even-numbered gears and odd-numbered gears. In this way, the 

next expected gear is pre-selected, which allows for faster and smoother shifting. For 

example, if the vehicle is accelerating in third gear, the shaft with gears one, three and five 

has gear three engaged and is transmitting power. The shaft with gears two, four, and six is 

idle, but has gear four engaged. When a shift is required, the controller disengages the oddgear clutch while simultaneously engaging the even-gear clutch, thus making a smooth shift. 

If, on the other hand, the driver slows down instead of continuing to accelerate, the 

transmission will have to change to second gear on the idling shaft to anticipate a downshift. 

This shift can be made quickly on the idling shaft since there is no torque being transferred on 

it.

 

qq This ICM would be applied to the 6 speed to 8 speed increment of $64 (2010$) applicable in 2012. The 4 

speed to 6 speed increment would carry the low complexity ICM. 
 Technologies Considered in the Agencies’ Analysis

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In addition to single-clutch and dual-clutch AMTs, there are also wet clutch and dry 

clutch designs which are used for different types of vehicle applications. Wet clutch AMTs 

offer a higher torque capacity that comes from the use of a hydraulic system that cools the 

clutches. Wet clutch systems are less efficient than the dry clutch systems due to the losses 

associated with hydraulic pumping. Additionally, wet AMTs have a higher cost due to the 

additional hydraulic hardware required. 

Overall, DCTs likely offer the greatest potential for effectiveness improvements 

among the various transmission options presented in this report because they offer the 

inherently lower losses of a manual transmission with the efficiency and shift quality 

advantages of electronic controls. The lower losses stem from the elimination of the 

conventional lock-up torque converter, and a greatly reduced need for high pressure hydraulic 

circuits to hold clutches or bands to maintain gear ratios (in automatic transmissions) or hold 

pulleys in position to maintain gear ratio (in Continuously Variable Transmissions). 

However, the lack of a torque converter will affect how the vehicle launches from rest, so a 

DCT will most likely be paired with an engine that offers sufficient torque at low engine 

speeds to allow for adequate launch performance or provide lower launch gears to 

approximate the torque multiplication of the torque converter to provide equivalent 

performance. 

In MYs 2012-2016 final rule, EPA and NHTSA estimated a 5.5 to 9.5 percent 

improvement in fuel consumption over a baseline 4/5-speed automatic transmission for a wet 

clutch DCT, which was assumed for all but the smallest of vehicle subclasses, Subcompact 

and Compact cars and small LT. This results in an incremental effectiveness estimate of 2.7 

to 4.1 percent over a 6-speed automatic transmission with IATC. For Subcompact and 

Compact Cars and small LT, which were assumed to use a dry clutch DCT, NHTSA 

estimated an 8 to 13 percent fuel consumption improvement over a baseline 4/5-speed 

automatic transmission, which equates to a 5.5 to 7.5 percent incremental improvement over 

the 6-speed transmission. 

Based on the 2011 Ricardo study, EPA and NHTSA have concluded that 8 to 13 

percent effectiveness is appropriate for 6-speed DCTs and 11 to 16 percent is appropriate for 

8-speed DCTs for this final rule, consistent with the proposal. These values include not only 

the DCT but also the increase in stepped gears and also a high efficiency gearbox (mentioned 

later). Independent of other technologies, this translates to an effectiveness for the DCT, 

alone, of 4 to 5% (for wet-clutch designs) and 5 to 6% (for dry-clutch designs) compared to a 

baseline automatic transmission of similar vintage and number of fixed gears. 

In this FRM analysis, consistent with the proposal, NHTSA applied an incremental 

effectiveness of 4 percent for a 6-speed dry DCT and 3.4 to 3.8 percent for a wet DCT 

compared to a 6-speed automatic transmission based on the lumped parameter model which 

includes the accompanied transmission efficiency improvement for MY 2010 and after 

transmissions. This translates to an effectiveness range of 7.4 to 8.6 percent compared to a 4 

speed automatic transmission for dry clutch design and 7.4 to 7.9 percent for a wet clutch 

design. NHTSA did not apply DCTs to vehicles with towing requirements, such as Minivan, 

Midsize light truck and large pickup truck. EPA did not apply DCTs to vehicle types 

classified as towing as described in Chapter 1 of EPA’s RIA. 
 Technologies Considered in the Agencies’ Analysis

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In the 2010 TAR, the agencies estimated the DMC at -$234 (2008$) for a 6 speed dryclutch DCT and -$165 for a 6 speed wet-clutch DCT with both DMCs applicable in the 

2017MY (see 2010 TAR, Table B2.1-1 at page B-10) and both incremental to a 4 speed 

automatic transmission. In the 2010 TAR, we pointed to Chapter 3 of the 2012-2016 final 

joint TSD where we noted that the DCT costs of -$147 (2007$ and incremental to a 6-speed 

automatic transmission) were based on a FEV tear-down study that assumed 450,000 units of 

production. We went on to state that we did not consider there to be sufficient US capacity in 

the 2012-2016 timeframe to produce 450,000 units and for that reason we were adjusting the 

tear-down values accordingly. The TAR timeframe for consideration was 2017-2025, and in 

the TAR we argued that production capacity would exist and that the FEV tear-down results 

be valid without adjustment. As noted in the proposal to this rule, we continue to believe that 

to be the case. In the final joint TSD supporting the 2012-2016 rule we also noted that the 

negative tear-down estimates found by FEV were not surprising when considering the relative 

simplicity of a dual-clutch transmission compared to an automatic transmission. Again, we 

continue to consider this to be true. 

For this analysis, we consider the 2010 TAR DMCs to be applicable to the 2012MY, 

thus the DMCs become -$238 (2010$) and -$168 (2010$) for 6 speed dry- and wet-clutch 

DCTs, respectively, both applicable in the 2012MY and incremental to a 4 speed auto 

transmission. The agencies consider the 6 speed DCT technology to be on the flat portion of 

the learning curve and have applied a medium complexity ICM of 1.39 through 2018 then 

1.29 thereafter. The resultant costs are shown in Table 3-40. 

New for this rulemaking is costing for an 8 speed DCT. For the cost of this 

technology, the agencies have relied on a tear-down study completed by FEV since 

publication of the TAR.57 In that study, the 8 speed DCT was found to be $198 (2007$) more 

costly than the 6 speed DCT. This DMC increment becomes $206 (2010$) for this analysis. 

Adding the $206 (2010$) to the -$238 (2010$) DMC and the -$168 (2010$) DMC for a 6 

speed dry- and wet-clutch DCT, the 8 speed dry- and wet-clutch DCTs relative to a 4 speed 

auto transmission would be -$32 (2010$) and $38 (2010$), respectively. The agencies 

consider this DMC to be applicable to the 2012MY. The agencies consider the 8 speed DCT 

technology to be on the flat portion of the learning curve and have applied a medium 

complexity ICM of 1.39 through the 2024MY then 1.29 thereafter. The 8 speed DCT has a 

later switch to long term ICMs because it is a newer technology that is not currently 

implemented in the fleet. The resultant costs for both 6 speed and 8 speed DCTs are shown in 

Table 3-40. 

Table 3-40 Costs for 6 & 8 Speed Dual Clutch Transmissions (2010$) 

Cost 

type Transmission type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC 6spDCT-dry -$207 -$203 -$199 -$195 -$191 -$187 -$183 -$179 -$176

DMC 6sp DCT-wet -$146 -$143 -$140 -$137 -$134 -$132 -$129 -$127 -$124

DMC 8sp DCT-dry -$28 -$27 -$27 -$26 -$26 -$25 -$25 -$24 -$24

DMC 8sp DCT-wet $33 $32 $32 $31 $30 $30 $29 $29 $28

IC 6spDCT-dry $91 $91 $68 $68 $68 $67 $67 $67 $67

IC 6sp DCT-wet $64 $64 $48 $48 $48 $48 $47 $47 $47

IC 8sp DCT-dry $12 $12 $12 $12 $12 $12 $12 $12 $9

IC 8sp DCT-wet $14 $14 $14 $14 $14 $14 $14 $14 $11
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TC 6spDCT-dry -$116 -$112 -$131 -$127 -$123 -$119 -$116 -$112 -$109

TC 6sp DCT-wet -$82 -$79 -$92 -$89 -$87 -$84 -$82 -$79 -$77

TC 8sp DCT-dry -$16 -$15 -$15 -$14 -$14 -$13 -$13 -$12 -$15

TC 8sp DCT-wet $47 $47 $46 $45 $45 $44 $44 $43 $39

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; sp=speed; dry=dry clutch; wet=wet-clutch

Note that all costs are relative to a 4 speed automatic transmission.

3.4.2.7 6-Speed Manual Transmissions (6MAN) 

Manual transmissions are entirely dependent upon driver input to shift gears: the 

driver selects when to perform the shift and which gear to select. This is the most efficient 

transfer of energy of all transmission layouts, because it has the lowest internal gear losses, 

with a minimal hydraulic system, and the driver provides the energy to actuate the clutch. 

From a systems viewpoint, however, vehicles with manual transmissions have the drawback 

that the driver may not always select the optimum gear ratio for fuel economy. Nonetheless, 

increasing the number of available ratios in a manual transmission can improve fuel economy 

by allowing the driver to select a ratio that optimizes engine operation more often. Typically, 

this is achieved through adding overdrive ratios to reduce engine speed at cruising velocities 

(which saves fuel through reduced engine pumping losses) and pushing the torque required of 

the engine towards the optimum level. However, if the gear ratio steps are not properly 

designed, this may require the driver to change gears more often in city driving, resulting in 

customer dissatisfaction. Additionally, if gear ratios are selected to achieve improved launch 

performance instead of to improve fuel economy, then no fuel saving effectiveness is realized. 

The 2012-2016 final rule estimated an effectiveness increase of 0.5 percent for 

replacing a 5-speed manual with a 6-speed manual transmission, which was derived from 

confidential manufacturer data Based on the updated LPM for this 2017-2025 rule, NHTSA 

has found that an effectiveness increase of 2.0 to 2.5 percent is possible when moving from a 

5-speed to a 6-speed manual transmission with improved internals. NHTSA updated costs 

from the 2012-2016 final rule to reflect the ICM low complexity markup of 1.11 which 

resulted in an incremental compliance cost of $250 as compared to $338 for MY 2012. This 

represents a DMC of $225 (2007$) which becomes $234 (2010$) for this analysis, applicable 

in the 2012MY. NHTSA continues to consider a 6 speed manual transmission to be on the 

flat portion of the learning curve and has applied a low complexity ICM of 1.24 through 2018 

then 1.19 thereafter. NHTSA’s resultant costs for a 6 speed manual transmission are shown in 

Table 3-41.

Table 3-41 Costs for 6 Speed Manual Transmission (2010$) 

Cost 

type Transmission type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC 6sp manual $204 $199 $196 $192 $188 $184 $181 $177 $173

IC 6sp manual $57 $57 $45 $44 $44 $44 $44 $44 $44

TC 6sp manual $260 $256 $240 $236 $232 $229 $225 $221 $218

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost; sp=speed; dry=dry clutch; wet=wet-clutch

Note that all costs are relative to a 5 speed manual transmission.
 Technologies Considered in the Agencies’ Analysis

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3.4.3 Vehicle electrification and hybrid electric vehicle technologies 

For the costs presented in this electrification and hybrid vehicle section, we have 

estimated costs for vehicle classes since the technologies are closely linked to the size of the 

vehicle as opposed to the number of cylinders on the engine or its valvetrain configuration. 

The vehicle classes for which we have estimated costs are consistent with the six vehicle 

classes developed for the lumped parameter model. Each agency has used the vehicle class 

specific costs and mapped those into their respective model-specific vehicle classes or types 

as shown in Table 3-42. This table simply presents the mapping of lumped parameter model 

vehicle classes (or cost vehicle classes) into model-specific vehicle classes (or vehicle types in 

the case of EPA’s OMEGA model, please refer to Chapter 1 of EPA’s final RIA for more 

details) to help the reader understand how the vehicle classes used for costing relate to the 

vehicle classes used for modeling. Note that there have been changes in the EPA data since 

the proposal. EPA now characterizes cost vehicle classes more consistently with the way they 

are classified in the lumped parameter model to avoid any confusion that the proposed cost 

vehicle classes may have generated. EPA has also reconfigured its 19 vehicle types in an 

effort to more closely align the vehicle types with the actual vehicles contained in each. Both 

of these changes are detailed in Chapter 1 of EPA’s final RIA. 

Table 3-42 Mapping of Vehicle Class into each Agency’s Model-Specific Vehicle Classes or Types 

EPA Vehicle 

Class for Cost 

Purpose

Lumped 

Parameter 

Classification

Example OMEGA Model 

Vehicle Type*

NHTSA/CAFE 

Model 

Classification

Subcompact/ 

Small Car Small Car

Fiesta

Focus

Yaris

1

Subcompact

Subcompact Perf PC

Compact

Compact Perf PC

Standard Car Standard Car

Fusion

Taurus

Camry

2, 3, 4

Mid-size PC

Mid-size Perf PC

Large Car Large Car

Crown 

Victoria

Mustang

5, 6

Large PC

Large Perf PC

Small MPV Small MPV

Escape

Rav4

Tacoma

7, 13 Small LT

Large MPV Large MPV 

Edge

Explorer

4Runner

Sienna

8, 9, 10, 14, 15

Midsize LT

Minivan LT

Truck Truck F150

Tundra 11, 12, 16, 17, 18, 19 Large LT

* OMEGA uses 19 vehicle types as shown here and described in detail in Chapter 1 of EPA’s final RIA.
 Technologies Considered in the Agencies’ Analysis

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3.4.3.1 Electrical Power Steering (EPS) / Electrohydraulic Power Steering (EHPS) 

Electric power steering (EPS) and Electrohydraulic power steering (EHPS) provide a 

potential reduction in CO2 emissions and fuel consumption over hydraulic power steering 

because of reduced overall accessory loads. This eliminates the parasitic losses associated 

with belt-driven power steering pumps which consistently draw load from the engine to pump 

hydraulic fluid through the steering actuation systems even when the wheels are not being 

turned. EPS is an enabler for all vehicle hybridization technologies since it provides power 

steering when the engine is off. EPS may be implemented on most vehicles with a standard 

12V system. Some heavier vehicles may require a higher voltage system or EHPS which may 

add cost and complexity. 

The 2012-2016 final rule, EPA and NHTSA estimated a 1 to 2 percent effectiveness 

for light duty vehicles based on the 2002 NAS report, Sierra Research Report and confidential 

OEM data. The 2010 Ricardo study also confirmed this estimate. NHTSA and EPA 

reviewed these effectiveness estimates and found them to be accurate, thus they have been 

retained for this final rule, consistent with the proposal. For large pickup truck the agencies 

used EHPS due to the utility requirement of these vehicles. The effectiveness of EHPS is 

estimated to be 0.8 percent. 

In the MY 2012-2016 final rule, the agencies estimated the DMC at $88 (2007$). 

Converting to 2010$, this DMC becomes $92 for this analysis, consistent with the recent 

heavy-duty GHG rule, which is considered applicable in the 2015MY. The agencies use the 

same DMC for EPS as for EHPS. Technically, EHPS is less costly than EPS. However, we 

believe that EHPS is likely to be used, if at all, on the largest trucks and utility vehicles. As 

such, it would probably need to be heavier-duty than typical EPS systems and the agencies 

consider the net effect to place EHPS on par with EPS in terms of costs. The agencies 

consider EPS/EHPS technology to be on the flat portion of the learning curve and have 

applied a low complexity ICM of 1.24 through 2018 then 1.19 thereafter. The resultant costs 

are shown in Table 3-43. 

Table 3-43 Costs of Electrical/Electro-hydraulic Power Steering (2010$) 

Cost type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC $87 $86 $84 $82 $80 $79 $77 $76 $74

IC $22 $22 $18 $18 $18 $18 $18 $18 $18

TC $109 $108 $101 $100 $98 $96 $95 $93 $92

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.3.2 Improved Accessories 

The accessories on an engine, including the alternator, coolant and oil pumps are 

traditionally mechanically-driven. A reduction in CO2 emissions and fuel consumption can be 

realized by driving them electrically, and only when needed (“on-demand”). 

Electric water pumps and electric fans can provide better control of engine cooling. 

For example, coolant flow from an electric water pump can be reduced and the radiator fan 
 Technologies Considered in the Agencies’ Analysis

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can be shut off during engine warm-up or cold ambient temperature conditions which will 

reduce warm-up time, reduce warm-up fuel enrichment, and reduce parasitic losses. 

Indirect benefit may be obtained by reducing the flow from the water pump 

electrically during the engine warm-up period, allowing the engine to heat more rapidly and 

thereby reducing the fuel enrichment needed during cold starting of the engine. Further 

benefit may be obtained when electrification is combined with an improved, higher efficiency 

engine alternator. Intelligent cooling can more easily be applied to vehicles that do not 

typically carry heavy payloads, so larger vehicles with towing capacity present a challenge, as 

these vehicles have high cooling fan loads. Both agencies also included a higher efficiency 

alternator in this category to improve the cooling system. 

The agencies considered whether to include electric oil pump technology for the 

rulemaking. Because it is necessary to operate the oil pump any time the engine is running, 

electric oil pump technology has insignificant effect on efficiency. Therefore, the agencies 

decided to not include electric oil pump technology for this final rule, consistent with the 

proposal. 

In MYs 2012-2016 final rule, the agencies used the effectiveness value in the range of 

1 to 2 percent based on technologies discussed above. NHTSA did not apply this technology 

to large pickup truck due to the utility requirement concern for this vehicle class. 

For this final rule, consistent with the proposal, the agencies considered two levels of 

improved accessories. For level one of this technology (IACC1) NHTSA now incorporates a 

high efficiency alternator (70 percent efficiency). The second level of improved accessories 

(IACC2) adds the higher efficiency alternator and incorporates a mild regenerative alternator 

strategy, as well as intelligent cooling. NHTSA and EPA jointly reviewed the estimates of 1 

to 2 percent effectiveness estimates used in the 2012-2016 final rule and TAR for level 

IACC1. More precisely, the agencies used effectiveness value in 1.2 to 1.8 percent range 

varying based on different vehicle subclasses. The incremental effectiveness for this 

technology in relative to EPS in the CAFE model is 0.91 to 1.61 percent. The combined 

effectiveness for IACC1 and IACC2 ranges from 3.1 to 3.9 percent and NHTSA applied 

incremental effectiveness of IACC2 in relative to IACC1 ranging from 1.74 to 2.55 percent. 

In the 2012-2016 rule, the agencies estimated the DMC of IACC1 at $71 (2007$). 

Converting to 2010$, this DMC becomes $75 for this analysis, applicable in the 2015MY, and 

consistent with the heavy-duty GHG rule. The agencies consider IACC1 technology to be on 

the flat portion of the learning curve and have applied a low complexity ICM of 1.24 through 

2018 then 1.19 thereafter. 

Cost is higher for IACC2 due to the inclusion of a higher efficiency alternator and a 

mild level of regeneration. The agencies estimate the DMC of the higher efficiency alternator 

and the regeneration strategy at $45 (2010$) incremental to IACC1, applicable in the 

2015MY. Including the costs for IACC1 results in a DMC for IACC2 of $120 (2010$) 

relative to the baseline case and applicable in the 2015MY. The agencies consider the IACC2 

technology to be on the flat portion of the learning curve. The agencies have applied a low 
 Technologies Considered in the Agencies’ Analysis

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complexity ICM of 1.24 through 2018 then 1.19 thereafter. The resultant costs are shown in 

Table 3-44. 

Table 3-44 Costs for Improved Accessory Technology – Levels 1 & 2 (2010$) 

Cost type IACC 

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC IACC1 $71 $70 $68 $67 $65 $64 $63 $62 $60

DMC IACC2 $114 $112 $110 $107 $105 $103 $101 $99 $97

IC IACC1 $18 $18 $14 $14 $14 $14 $14 $14 $14

IC IACC2 $29 $29 $23 $23 $23 $23 $23 $23 $23

TC IACC1 $89 $88 $82 $81 $80 $78 $77 $76 $75

TC IACC2 $143 $141 $133 $131 $128 $126 $124 $122 $120

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Note that both levels of IACC technology are incremental to the baseline case.

3.4.3.3 Air Conditioner Systems 

We have a detailed description of the A/C program in Chapter 5 of this joint TSD. 

The reader is directed to that chapter to learn the specifics of the program, the credits 

involved, and details behind the costs we have estimated. Table 3-45 is a copy of Table 5-18 

showing the total costs for A/C controls used in this final rule. 

Table 3-45 Total Costs for A/C Control Used in This Final rule (2010$) 

Car/

Truck Cost type Rule 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car

TC Reference $76 $75 $70 $69 $68 $67 $66 $65 $64

TC Control $25 $40 $57 $65 $79 $77 $72 $71 $69

TC Both $101 $115 $127 $134 $147 $144 $138 $135 $133

Truck

TC Reference $58 $57 $54 $53 $52 $51 $50 $49 $49

TC Control $2 $46 $73 $82 $95 $93 $88 $86 $84

TC Both $60 $103 $127 $134 $147 $144 $138 $135 $133

Fleet TC Both $86 $111 $127 $134 $147 $144 $138 $135 $133

TC=Total cost

3.4.3.4 Stop-start (12V Micro Hybrid) 

The stop-start technology we consider for this final rule, consistent with the 

proposal—also known as idle-stop or 12-volt micro-hybrid—is the most basic hybrid system 

that facilitates idle-stop capability. When vehicle comes to a stop, the system will 

automatically shut down the internal combustion engine and restarts the engine when vehicle 

starts to move again. This is especially beneficial to reduce emission and fuel consumption 

when vehicle spends significant amount of time stopping in inner city driving or a traffic jam. 

Along with other enablers, this system typically replaces the standard 12-volt starter with an 

improved unit capable of higher power and increased cycle life. These systems typically 

incorporate an improved battery to prevent voltage-droop on restart. Different from MY 

2012-2016 rule, this technology is applied to all vehicle classes, including large pickup truck. 
 Technologies Considered in the Agencies’ Analysis

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In MYs 2012-2016 final rule, even though EPA did not use 12 volt stop-start technology, 

NHTSA and EPA jointly reviewed the assumption. The effectiveness NHTSA used in the 

CAFE model for MYs 2012-2016 final rule ranged from 2 to 4 percent, depending on whether 

the vehicle is equipped with a 4-, 6- or 8-cylinder engine, with the 4-cylinder engine having 

the lowest range and the 8-cylinder having the highest. In this FRM analysis, consistent with 

the proposal, when combining IACC1, IACC2 and 12V stop-start system, the estimated 

effectiveness based on 2010 Ricardo study ranges from 4.8 percent to 5.9 percent. The 

agencies applied this effectiveness in the FRM analysis, consistent with the proposal. For 

CAFE modeling, the incremental effectiveness for 12V stop-start relative to IACC2 is 1.68 to 

2.2 percent. Importantly, the effectiveness values presented here represent two-cycle 

effectiveness. Because stop-start technology provides considerable off-cycle benefits, both 

agencies apply a credit value to the technology. Off-cycle credits are discussed in Chapter 5 

of this Joint TSD. 

In the 2012-2016 rule, the agencies estimated the DMC at $282 (2007$) to $350 

(2007$) for small cars through large trucks, respectively. Converting to 2010$, these DMCs 

become $295 (2010$) through $367 (2010$) for this analysis which are considered applicable 

in the 2015MY. The agencies consider 12V stop-start technology to be on the steep portion 

of the learning curve in the 2012-2016 timeframe and flat thereafter and have applied a 

medium complexity ICM of 1.39 through 2018 then 1.29 thereafter. The resultant costs are 

shown in Table 3-46. 

Table 3-46 EPA and NHTSA Costs for 12V Micro Hybrid or 12V Stop-Start (2010$) 

Cost 

type

Vehicle 

Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car $287 $278 $270 $261 $254 $246 $239 $232 $225

DMC Standard car $287 $278 $270 $261 $254 $246 $239 $232 $225

DMC Large car $325 $315 $306 $296 $288 $279 $271 $262 $255

DMC Small MPV $325 $315 $306 $296 $288 $279 $271 $262 $255

DMC Large MPV $325 $315 $306 $296 $288 $279 $271 $262 $255

DMC Truck $356 $346 $335 $325 $315 $306 $297 $288 $279

IC Small car $114 $114 $85 $85 $84 $84 $84 $84 $83

IC Standard car $114 $114 $85 $85 $84 $84 $84 $84 $83

IC Large car $129 $129 $96 $96 $96 $95 $95 $95 $94

IC Small MPV $129 $129 $96 $96 $96 $95 $95 $95 $94

IC Large MPV $129 $129 $96 $96 $96 $95 $95 $95 $94

IC Truck $142 $141 $105 $105 $105 $105 $104 $104 $104

TC Small car $401 $392 $354 $346 $338 $330 $322 $315 $308

TC Standard car $401 $392 $354 $346 $338 $330 $322 $315 $308

TC Large car $454 $444 $402 $392 $383 $374 $366 $357 $349

TC Small MPV $454 $444 $402 $392 $383 $374 $366 $357 $349

TC Large MPV $454 $444 $402 $392 $383 $374 $366 $357 $349

TC Truck $498 $487 $441 $430 $420 $410 $401 $392 $383

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost
 Technologies Considered in the Agencies’ Analysis

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3.4.3.5 Mild Hybrid

Mild hybrid systems, also called Higher Voltage Stop-Start and Belt Mounted 

Integrated Starter Generator (BISG) systems are similar to a micro-hybrid system, offering 

idle-stop functionality, except that they utilize larger electric machine and a higher capacity 

battery, typically 42 volts or above, thus enabling a limited level of regenerative braking 

unavailable for a MHEV. The larger electric machine and battery also enables a limited 

degree of power assist, which MHEV cannot provide. However, because of the limited torque 

capacity of the belt-driven design, these systems have a smaller electric machine, and thus 

less capability than crank-integrated or stronger hybrid systems. These systems replace the 

conventional alternator with a belt-driven starter/alternator and may add high voltage 

electrical accessories (which may include electric power steering and an auxiliary automatic 

transmission pump). The limited electrical requirements of these systems allow the use of 

lead-acid batteries or supercapacitors for energy storage, or the use of a small lithium-ion 

battery pack, as is modeled in this analysis. While the mild hybrid system was not applied in 

the NPRM analysis because the agencies did not have solid information regarding its likely 

architecture, effectiveness or cost, the agencies are including the technology in the final rule 

because we now have good information about it. Further, the agencies are making available 

credits for mild hybrid pickup trucks in an effort to encourage such technologies. Lastly, the 

simulation modeling and cost estimation results show that the mild hybrid system could be a 

cost effective technology. 

For the BISG technology the agencies sized the system using a 15 kW 

starter/generator and 0.25 kWh Li-ion battery pack, which is similar to General Motors’ 

eAssist BISG, which is available in MY 2012 Buick LaCrosse, Buick Regal, and Chevrolet 

Malibu vehicles. The agencies made this size system available to all vehicle subclasses, 

believing that manufacturers might use a similar strategy to control component complexity 

across the subclasses. As mentioned above, estimates were developed by ANL using 

Autonomie full vehicle simulation software. The absolute effectiveness for the CAFE analysis 

ranged from 8.5 to 11.6 percent depending on vehicle subclass. The effectiveness values 

include technologies that would be expected to incorporated with BISG which are stop/start 

(MHEV) and improved accessories (IACC1 and IACC2), however the effectiveness values do 

not include electric power steering (EPS). 

The costs for the mild hybrid technology are all new for this final rule and were 

developed in a manner consistent with costs generated for strong hybrids. These costs are 

presented in sections 3.4.3.7 through 3.4.3.10 of this Joint TSD. The same cost and 

effectiveness results were applied by both NHTSA and EPA. 

3.4.3.5.1 Integrated Motor Assist (IMA)/Crank Integrated Starter Generator (CISG) 

IMA is a system developed and marketed by Honda58 and is similar to CISG. They 

both utilize a thin axial electric motor bolted to the engine’s crankshaft and connected to the 

transmission through a torque converter or clutch. The axial motor is motor/generator that 

typically operates above 100 volts (but lower than the stronger hybrid systems discussed 

below, which typically operate at around 300 volts) and can provide sufficient torque for 

launch as well as generate sufficient current to provide significant levels of brake energy 
 Technologies Considered in the Agencies’ Analysis

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recovery. The motor/generator also acts as the starter for the engine and can replace a typical 

accessory-driven alternator. Current IMA/CISG systems typically do not launch the vehicle 

on electric power alone, although some commercially available systems can cruise on electric 

power and dual-clutch IMA and CISG could be applied to all classes of vehicles. This 

technology is not used as an enabling technology in this FRM analysis, consistent with the 

proposal, by either EPA or NHTSA due to our expectation that manufacturers will be moving 

to more cost effective technologies. 

EPA relied on a combination of certification data (comparing vehicles available with 

and without a hybrid system and backing out other components where appropriate) and 

manufacturer-supplied information to determine that the effectiveness of these systems in 

terms of CO2 reduction is 30 percent for small cars, 25 percent for large cars, and 20 percent 

for minivans and small trucks similar to the range estimated by NHTSA for the respective 

vehicle classes. The effectiveness for small cars assumes engine downsizing to maintain 

approximately equivalent performance. The large car, minivan, and small truck effectiveness 

values assume less engine downsizing in order to improve vehicle performance and/or 

maintain towing and hauling performance. 

In the 2012-2016 final rule, the agencies estimated the DMC at $1,973, $2,497, 

$2,508, $2,366 and $3,063 (all values in 2007$) for a small car, large car, minivan, small 

truck and large truck, respectively. For this final rule, the DMCs are $2,070, $2,620, $2,631 

and $3,214 (all values in 2010$) for small car/standard car, large car, small MPV and large 

MPV/truck. All of these DMCs are considered applicable in the 2015MY. The agencies 

consider the IMA technology to be on the steep portion of the learning curve and have applied 

a high1 complexity ICM of 1.56 through 2018 then 1.35 thereafter. The resultant costs are as 

shown in Table 3-47. As noted earlier, the IMA technology is not included as an enabling 

technology in this analysis, although it is included as a baseline technology because it exists 

in the baseline fleet. The agencies moved away from this technology and applied P2 hybrids 

instead because P2 is more cost effective than IMA. 

Table 3-47 Costs for IMA Hybrids (2010$) 

Cost 

type Vehicle Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car/Standard 

car

$2,008 $1,947 $1,889 $1,832 $1,777 $1,724 $1,672 $1,622 $1,573

DMC Large car $2,541 $2,465 $2,391 $2,319 $2,250 $2,182 $2,117 $2,053 $1,992

DMC Small MPV $2,552 $2,475 $2,401 $2,329 $2,259 $2,191 $2,126 $2,062 $2,000

DMC Large MPV/Truck $3,118 $3,024 $2,933 $2,845 $2,760 $2,677 $2,597 $2,519 $2,443

IC Small car/Standard 

car

$1,162 $1,159 $709 $707 $706 $704 $702 $701 $699

IC Large car $1,471 $1,467 $898 $895 $893 $891 $889 $887 $885

IC Small MPV $1,478 $1,473 $901 $899 $897 $895 $893 $891 $889

IC Large MPV/Truck $1,805 $1,799 $1,101 $1,098 $1,096 $1,093 $1,090 $1,088 $1,086

TC Small car/Standard 

car

$3,170 $3,106 $2,598 $2,540 $2,483 $2,428 $2,375 $2,323 $2,273

TC Large car $4,013 $3,932 $3,289 $3,215 $3,143 $3,073 $3,006 $2,940 $2,877

TC Small MPV $4,029 $3,948 $3,302 $3,228 $3,156 $3,086 $3,018 $2,952 $2,889

TC Large MPV/Truck $4,923 $4,823 $4,034 $3,944 $3,856 $3,770 $3,687 $3,607 $3,529

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost
 Technologies Considered in the Agencies’ Analysis

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3.4.3.6 HEV, PHEV, EV and Fuel Cell Vehicle Technologies 

A hybrid vehicle is a vehicle that combines two or more sources of propulsion energy, 

where one uses a consumable fuel (like gasoline), and one is rechargeable (during operation, 

or by another energy source). Hybrid technology is well established in the U.S. market and 

more manufacturers are adding hybrid models to their lineups. Hybrids reduce fuel 

consumption through three major mechanisms: 

The internal combustion engine can be optimized (through downsizing, modifying 

the operating cycle, or other control techniques) to operate at or near its most 

efficient point more of the time. Power loss from engine downsizing can be 

mitigated by employing power assist from the secondary power source. 

Some of the energy normally lost as heat while braking can be captured and stored 

in the energy storage system for later use. 

The engine is turned off when it is not needed, such as when the vehicle is coasting 

or when stopped. 

Hybrid vehicles utilize some combination of the three above mechanisms to reduce 

fuel consumption and CO2 emissions. A fourth mechanism to reduce petroleum fuel 

consumption, available only to plug-in hybrids, is by substituting the petroleum fuel energy 

with energy from another source, such as the electric grid. The effectiveness of fuel 

consumption and CO2 reduction depends on the utilization of the above mechanisms and how 

aggressively they are pursued. One area where this variation is particularly prevalent is in the 

choice of engine size and its effect on balancing fuel economy and performance. Some 

manufacturers choose not to downsize the engine when applying hybrid technologies. In 

these cases, performance is vastly improved, while fuel efficiency improves significantly less 

than if the engine was downsized to maintain the same performance as the conventional 

version. While this approach has been used in cars such as the Lexus 600h luxury vehicle, it 

is more likely to be used in the future for vehicles like trucks where towing and/or hauling are 

an integral part of their performance requirements. In these cases, if the engine is downsized, 

the battery can be quickly drained during a long hill climb with a heavy load, leaving only a 

downsized engine to carry the entire load. Because towing capability is currently a heavilymarketed truck attribute, manufacturers are hesitant to offer a truck with downsized engine 

which can lead to a significantly diminished towing performance when the battery state of 

charge level is low, and therefore engines are traditionally not downsized for these vehicles. 

Although hybrid vehicles using other energy storage concepts (flywheel, hydraulic) 

have been developed, the automotive systems in production for passenger cars and light 

trucks are all hybrid electric vehicles (HEV) that use battery storage and electric drive 

systems. This appears likely to be the case for the foreseeable future. HEVs are part of a 

continuum of vehicles using systems with differing levels of electric drive and electric energy 

storage. This range of vehicles includes relatively basic system without electric energy 

storage such as engine start/stop systems; HEV systems with varying degrees of electric 

storage and electric drive system capability including mild-hybrid electric vehicles (MHEV) 
 Technologies Considered in the Agencies’ Analysis

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with limited capability but lower cost; strong hybrid electric vehicles (SHEV) with full 

hybridization capability such as the P2 hybrid technology which the agencies evaluate as a 

compliance option in this FRM; plug-in hybrid electric vehicles (PHEV) with differing 

degrees of all electric range and battery electric vehicles (EV) that rely entirely on electric 

drive and battery electric energy storage. 

Different HEV, PHEV and EV concepts utilize these mechanisms differently, so they 

are treated separately for the purposes of this analysis. In many applications, particularly with 

PHEV and EV, the battery represents the most costly and system-limiting sub-component of 

the hybrid system. Currently, there are many battery chemistries being developed and refined 

for hybrid applications that are expected to enhance the performance of future hybrid 

vehicles. Section 3.4.3.6.4 contains a discussion of battery energy storage and the major 

hybrid concepts that were determined to be available during the MY 2017-2015 timeframe. 

Fuel cell vehicles are a separate category of electric vehicle that rely entirely on 

electric propulsion with electricity produced on-board the vehicle using a proton-exchangemembrane fuel cell (PEMFC) fueled with hydrogen. Fuel cell vehicles under development 

are typically configured as a hybrid with battery storage used to provide brake energy 

recovery and improved response to fast transients in vehicle energy demand. 

3.4.3.6.1 Power-split hybrid 

Power-split hybrid (PSHEV) – a hybrid electric drive system that replaces the 

traditional transmission with a single planetary gear set and two motor/generators. The 

smaller motor/generator uses the engine to either charge the battery or to supply additional 

power to the drive motor. The second, more powerful motor/generator is permanently 

connected to the vehicle’s final drive and always turns with the wheels. The planetary gear 

splits engine power between the first motor/generator and the drive motor to either charge the 

battery or supply power to the wheels. Power-split hybrids are not used as an enabling 

technology in this final rule, consistent with the proposal. 

In MYs 2012-2016 final rule, EPA and NHTSA used a combination of manufacturersupplied information and a comparison of vehicles available with and without a hybrid system 

from EPA’s fuel economy test data to determine that the effectiveness is 19 to 36 percent for 

the classes to which it is applied. The estimate would depend on whether engine downsizing 

is also assumed. In the CAFE incremental model, the range of effectiveness used was 23 to 

33 percent as engine downsizing is not assumed (and accounted for elsewhere). 

For this analysis, in order to estimate baseline costs, the agencies are using power-split 

HEV costs generated by FEV as part of a tear-down study. In that study, FEV found the 

DMC of the entire power-split system (battery-pack and non-battery components) to be 

$2,853 (2007$), $3,175 (2007$), $3,435 (2007$), $4,168 (2007$) for vehicle sized, for 

example, like a Ford Fiesta, Ford Focus, Ford Fusion and Ford Flex, respectively. For this 

analysis, these values become $2,967, $3,302, $3,572 and $4,335, respectively, all in 2010 

dollars. In the 2012-2016 final rule, the agencies estimated the DMC of a large truck powersplit system at $5,137 (2007$) which becomes $5,391 for this analysis (2010$) and we are 

using this value for the large MPV vehicle class. All of these DMCs are considered 
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applicable in the 2015MY. The agencies consider the power-split technology to be on the flat 

portion of the learning curve and have applied a high1 complexity ICM of 1.56 through 2018 

then 1.35 thereafter. The resultant costs are as shown in Table 3-48. As noted earlier, the 

Power-split technology is not included as an enabling technology in this analysis, although it 

is included as a baseline technology because it exists in the baseline fleet. 

Table 3-48 Costs for Power-Split Hybrids (2010$) 

Cost 

type Vehicle Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car $2,820 $2,764 $2,709 $2,655 $2,602 $2,549 $2,498 $2,449 $2,400

DMC Standard car $3,139 $3,076 $3,015 $2,954 $2,895 $2,837 $2,781 $2,725 $2,671

DMC Large car $3,396 $3,328 $3,261 $3,196 $3,132 $3,070 $3,008 $2,948 $2,889

DMC Small MPV $4,120 $4,038 $3,957 $3,878 $3,801 $3,725 $3,650 $3,577 $3,505

DMC Large MP $5,125 $5,023 $4,922 $4,824 $4,727 $4,633 $4,540 $4,449 $4,360

IC Small car $1,663 $1,659 $1,017 $1,015 $1,013 $1,012 $1,010 $1,008 $1,007

IC Standard car $1,851 $1,846 $1,131 $1,129 $1,128 $1,126 $1,124 $1,122 $1,120

IC Large car $2,002 $1,998 $1,224 $1,222 $1,220 $1,218 $1,216 $1,214 $1,212

IC Small MPV $2,429 $2,424 $1,485 $1,483 $1,480 $1,478 $1,475 $1,473 $1,471

IC Large MP $3,021 $3,015 $1,847 $1,844 $1,841 $1,838 $1,835 $1,832 $1,829

TC Small car $4,483 $4,423 $3,725 $3,669 $3,615 $3,561 $3,508 $3,457 $3,406

TC Standard car $4,990 $4,923 $4,146 $4,084 $4,023 $3,963 $3,905 $3,847 $3,791

TC Large car $5,398 $5,326 $4,485 $4,418 $4,352 $4,288 $4,224 $4,162 $4,101

TC Small MPV $6,549 $6,462 $5,442 $5,361 $5,281 $5,202 $5,125 $5,050 $4,976

TC Large MP $8,146 $8,037 $6,769 $6,668 $6,568 $6,471 $6,375 $6,281 $6,190

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.3.6.2 2-mode hybrid 

2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an adaptation of 

a conventional stepped-ratio automatic transmission by replacing some of the transmission 

clutches with two electric motors that control the ratio of engine speed to vehicle speed, while 

clutches allow the motors to be bypassed. This improves both the transmission torque 

capacity for heavy-duty applications and reduces fuel consumption and CO2 emissions at 

highway speeds relative to other types of hybrid electric drive systems. 2-mode hybrids were 

not been considered in the proposal and the agencies sought comments on whether or not 2-

mode hybrids should be considered for vehicles with towing requirements, such as pickup 

trucks. However, no comments were received on their applicability in the future and thus 

consistent with the proposal, 2-mode hybrids were not included in the final rule analysis. 

For MYs 2012-2016 final rule, the CAFE model considered a range of 23 to 33 

percent with a midpoint of 28 percent, assuming no engine downsizing to preserve the utility 

nature of medium and large trucks (e.g., maintaining full towing capability even in situations 

with low battery charge) and EPA estimates CO2 emissions reduction effectiveness to be 25 

percent for large trucks (LDT3 and LDT4 categories) based on vehicle certification data. 

EPA estimates an effectiveness of 40 percent for smaller vehicles. 

The agencies have estimated the costs for 2-mode hybrids using costs used in the 2010 

TAR. For this analysis, the 2-mode battery pack DMC is estimated at $1,100 (2010$) and the 
 Technologies Considered in the Agencies’ Analysis

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DMC of non-battery components is estimated at $2,997 (2010$). The battery pack DMC is 

considered to be applicable for the 2025MY while the non-battery pack DMC would be 

applicable for the 2012MY. The agencies consider the 2-mode battery packs to be on the 

steep portion of the learning curve during the 2017-2025 timeframe. The agencies have 

applied a high1 complexity ICM of 1.56 through 2018 then 1.35 thereafter. For 2-mode nonbattery components, the agencies consider them to be on the flat portion of the learning curve 

in the 2017-2025 timeframe and have applied a high1 complexity ICM of 1.56 through 2018 

then 1.35 thereafter. The resultant 2-mode hybrid costs are presented in Table 3-49.

Table 3-49 Costs for 2-Mode Hybrids (2010$) 

Cost 

type Vehicle Class 2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery-pack

DMC

Small MPV/Large 

MPV/Truck

$2,148 $1,718 $1,718 $1,374 $1,374 $1,374 $1,374 $1,374 $1,100

IC $688 $660 $399 $389 $389 $389 $389 $389 $380

TC $2,835 $2,378 $2,118 $1,763 $1,763 $1,763 $1,763 $1,763 $1,479

Non-battery pack components

DMC

Small MPV/Large 

MPV/Truck

$2,600 $2,548 $2,497 $2,447 $2,398 $2,350 $2,303 $2,257 $2,212

IC $1,664 $1,660 $1,019 $1,018 $1,016 $1,015 $1,013 $1,012 $1,010

TC $4,264 $4,208 $3,517 $3,465 $3,415 $3,365 $3,317 $3,269 $3,222

Battery-pack and non-battery pack components

TC Small MPV/Large 

MPV/Truck $7,099 $6,586 $5,634 $5,228 $5,178 $5,128 $5,080 $5,032 $4,702

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.3.6.3 P2 Hybrid 

A P2 hybrid is hybrid technology that uses a transmission-integrated electric motor 

placed between the engine and a gearbox or CVT and coupled to the engine crankshaft via a 

clutch. The engine and the drive motor are mechanically independent of each other, allowing 

the engine or motor to power the vehicle separately or combined. Disengaging the engine 

clutch allows all-electric operation and more efficient brake-energy recovery. The P2 HEV 

system is similar to the Honda IMA HEV architecture with the exception of the added clutch, 

and larger batteries and motors. Examples of this include the Hyundai Sonata HEV and 

Infiniti M35h. The agencies believe that the P2 is an example of a “strong” hybrid technology 

that is typical of what will be prevalent in the timeframe of this rule. The agencies could have 

equally chosen the power-split architecture as the representative HEV architecture. These two 

HEV’s have similar average effectiveness values (combined city and highway fuel economy), 

though the P2 systems may have lower cost due to having only a single, smaller 

motor/generator. 

For purposes of this rulemaking analysis, the agencies are assuming that P2 hybrids 

will become the dominant technology in the MYs 2017-2025 timeframe, replacing costlier 

power-split or 2-mode architectures while providing substantially similar efficiency 

improvement. At the present time, P2 hybrids are relatively new to the market and the 

agencies have not attempted to quantify any measurable performance differential between 

these technologies. As mentioned, the 2011 Hyundai Sonata, 2011 Volkswagen Touareg 
 Technologies Considered in the Agencies’ Analysis

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Hybrid, the 2011 Porsche S Hybrid, and the 2012 Infiniti M35 Hybrid are examples of P2 

hybrids currently in production and available to consumers. The agencies are aware of some 

articles in trade journals, newspapers and other reviews that some first generation P2 hybrid 

vehicles with automatic transmissions have trade-offs in NVH and drivability – though these 

reviews do not cover all of the P2 systems available today, and a number of reviews are very 

positive with respect to NVH and drivability. The agencies recognize that manufacturers will 

have several years to test, develop and improve P2 technology in the years before 2017. We 

expect that manufacturers will address any perceived integration issues in early production 

models. However, we believe it is important to continue to monitor development of P2 

hybrids and market acceptance of this technology. We will continue to gather information on 

these issues and consider them as part of the mid-term evaluation. 

The agencies requested comment regarding the potential of P2 hybrids to overcome 

these issues or others and we specifically sought comment from automakers developing and 

considering P2 technology on whether they believe these to be significant impediments to 

deployment and how they may be addressed. There were no comments submitted. 

The effectiveness used for vehicle packages with the P2-hybrid configuration within 

this analysis reflects a conservative estimate of system performance. Vehicle simulation 

modeling of technology packages using the P-2 hybrid has recently been completed under a 

contract with Ricardo Engineering. The agencies have updated the effectiveness of hybrid 

electric vehicle packages using the new Ricardo vehicle simulation modeling runs for this 

analysis. 

Due to the lower cost and comparative effectiveness of P2 hybrid in relative to other 

strong hybrid technologies, such as power-split hybrid and 2-mode hybrid, the agencies 

assume P2 hybrid application for all vehicle sub-classes in this FRM analysis, consistent with 

the proposal, and increased HEV effectiveness by approximately 2% comparing to 2012-2016 

light duty GHG/CAFE final rule based on published data for new HEVs that have entered into 

production, such as 2011 Hyundai Sonata hybrid, 2010 Hyundai Elantra LPI HEV (Korean 

market only), 2011 Infiniti G35 Hybrid and 2011 Volkswagen Touareg Hybrid). In addition, 

for the Large Car, Minivan and Small Truck subclasses, the agencies further increased HEV 

effectiveness by assuming that towing capacity could be reduced from their current ratingrr to 

approximately 1,500 pounds for some vehicles in these subclasses without significantly 

impacting consumers’ need for utility in these vehicles.ss The agencies believe that 

consumers for these vehicles who require higher towing capacity could acquire it by 

purchasing a vehicle with a more capable non-hybrid powertrain (as they do today).tt 

 

rr Current small SUVs and Minivans have an approximate average towing capacity of 2000 pounds (without a 

towing package), but range from no towing capacity to 3500 pounds. 

ss We note that there are some gasoline vehicles in the large car/minivan/small truck segments sold today which 

do not have any towing rating. 

tt The agencies recognize that assuming that certain consumers will choose to purchase non-hybrid vehicles in 

order to obtain their desired towing capacity could lead to some increase in fuel consumption and CO2 emissions 

as compared to assuming that towing capacity is maintained for hybrid vehicles across the board. However, the 
 Technologies Considered in the Agencies’ Analysis

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Moreover, it is likely that some fraction of consumers who purchase the larger engine option 

do so for purposes of hauling and acceleration performance, not just maximum towing. 

A reduction in towing capacity allows greater engine downsizing, which increases 

estimated overall HEV system incremental effectiveness by 5 to 10 percent for Large Cars, 

Minivans, and Small Trucks, similar to the HEV effectiveness value assumed for Small Cars 

and Compact Cars.uu

 

Based on the recent Ricardo study, the effectiveness for P2 hybrid used in this FRM, 

consistent with the proposal, is 46.2 percent for subcompact and compact passenger cars, 48.6 

percent for midsize passenger car, 49.4 percent for large passenger car, 46.1 percent for small 

light truck, 45.7 percent for midsize SUV, truck and minivan and 45.1 percent for large 

pickup truck. 

The process for battery sizing for the P2 hybrids is explained in Section 3.4.3.8. The 

battery sizing is different for the 2008 and 2010 baseline vehicle fleets, because vehicle mass 

for each subclass is slightly different between the two baseline fleets, thus requiring a slightly 

different battery size to maintain equivalent performance. The battery sizes with no applied 

mass reduction are listed in Table 3-50. 

Table 3-50 NHTSA Battery Sizes for P2 Hybrid Applied in Volpe Model without Mass Reduction (kWh) 

Baseline 

Fleet

Subcompact 

PC/ Perf PC

Compact PC/ 

Perf PC

Midsize 

PC/Perf PC

Large PC/Perf 

PC

Midsize LT 

Minivan Small LT Large LT

2008 0.81 1.00 1.16 1.28 1.04 1.49

2010 0.84 1.02 1.20 1.27 1.06 1.56

The agencies have applied a high complexity ICM to both the battery and non-battery 

component costs for P2 hybrid. But for battery for P2 hybrid, the ICM switches from short 

term value of 1.56 to long term value of 1.35 at 2024 while for the non-battery component the 

switch happens at 2018. 

The costs for P2 hybrids without mass reduction as used in the Volpe model are listed 

in Table 3-51. The battery costs are calculated using the battery sizes for both the 2008 and 

2010 baseline fleets. NHTSA accounts the cost impact from the interaction between mass 

reduction and sizing of the electrification system (battery and non-battery system) as a cost 

 

agencies think it likely that the net improvement in fuel consumption and CO2 emissions due to the increased 

numbers of hybrids available for consumers to choose will offset any potential increase in fuel consumption and 

CO2 emissions resulting from consumers selecting the higher-performance non-hybrid powertrain vehicles. 

uu The effectiveness of HEVs for heavier vehicles which require conventional towing capabilities is markedly 

less because the rated power of the IC engine must be similar to its non-hybrid brethren. As such, there is less 

opportunity for downsizing with these vehicles. 
 Technologies Considered in the Agencies’ Analysis

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synergy as described in section 3.4.3.9. Estimated costs for P2 HEVs with mass reduction as 

used in the OMEGA model are presented in Sections 3.4.3.9 and 3.4.3.10 below. 

Table 3-51 NHTSA Costs for P2 Hybrid Applied in Volpe Model without Mass Reduction (2010$) 

Tech. Cost 

Type

NHTSA 

Vehicle 

Class

Baseline 

Fleet 2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery DMC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $733 $711 $689 $669 $648 $629 $610 $592 $574 

2008 $726 $704 $683 $662 $642 $623 $604 $586 $569 

Battery DMC Midsize 

PC/Perf PC

2010 $818 $793 $769 $746 $724 $702 $681 $661 $641 

2008 $809 $784 $761 $738 $716 $694 $674 $653 $634 

Battery DMC Large 

PC/Perf PC

2010 $959 $931 $903 $876 $849 $824 $799 $775 $752 

2008 $946 $918 $890 $864 $838 $813 $788 $765 $742 

Battery DMC Midsize LT 

Minivan

2010 $887 $860 $834 $809 $785 $761 $739 $716 $695 

2008 $885 $858 $832 $807 $783 $760 $737 $715 $693 

Battery DMC Small LT 2010 $796 $773 $749 $727 $705 $684 $663 $643 $624 

2008 $787 $763 $740 $718 $697 $676 $655 $636 $617 

Battery DMC Large LT 2010 $1,029 $998 $968 $939 $911 $884 $857 $831 $807 

2008 $1,020 $989 $960 $931 $903 $876 $850 $824 $799 

NonbatteryDMC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $1,474 $1,445 $1,416 $1,388 $1,360 $1,333 $1,306 $1,280 $1,254 

2008 $1,468 $1,438 $1,410 $1,381 $1,354 $1,327 $1,300 $1,274 $1,249 

NonbatteryDMC Midsize 

PC/Perf PC

2010 $1,645 $1,612 $1,580 $1,549 $1,518 $1,487 $1,457 $1,428 $1,400 

2008 $1,627 $1,595 $1,563 $1,531 $1,501 $1,471 $1,441 $1,413 $1,384 

NonbatteryDMC Large 

PC/Perf PC

2010 $1,949 $1,910 $1,872 $1,834 $1,798 $1,762 $1,727 $1,692 $1,658 

2008 $1,906 $1,868 $1,830 $1,794 $1,758 $1,723 $1,688 $1,655 $1,621 

NonbatteryDMC Midsize LT 

Minivan

2010 $1,817 $1,780 $1,745 $1,710 $1,676 $1,642 $1,609 $1,577 $1,546 

2008 $1,798 $1,762 $1,727 $1,693 $1,659 $1,626 $1,593 $1,561 $1,530 

NonbatteryDMC Small LT 2010 $1,587 $1,555 $1,524 $1,493 $1,464 $1,434 $1,406 $1,378 $1,350 

2008 $1,557 $1,526 $1,496 $1,466 $1,436 $1,408 $1,380 $1,352 $1,325 

NonbatteryDMC Large LT 2010 $1,918 $1,879 $1,842 $1,805 $1,769 $1,733 $1,699 $1,665 $1,631 

2008 $1,901 $1,863 $1,825 $1,789 $1,753 $1,718 $1,684 $1,650 $1,617 

Battery IC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $413 $411 $410 $409 $407 $406 $405 $404 $248 

2008 $409 $408 $406 $405 $404 $402 $401 $400 $246 

Battery IC Midsize 2010 $461 $459 $458 $456 $455 $453 $452 $451 $277 
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PC/Perf PC 2008 $456 $454 $453 $451 $450 $448 $447 $446 $274 

Battery IC Large 

PC/Perf PC

2010 $541 $539 $537 $535 $534 $532 $530 $529 $325 

2008 $533 $532 $530 $528 $526 $525 $523 $522 $320 

Battery IC Midsize LT 

Minivan

2010 $500 $498 $496 $495 $493 $492 $490 $489 $300 

2008 $499 $497 $495 $494 $492 $490 $489 $488 $299 

Battery IC Small LT 2010 $449 $447 $446 $444 $443 $442 $440 $439 $270 

2008 $443 $442 $440 $439 $438 $436 $435 $434 $266 

Battery IC Large LT 2010 $580 $578 $576 $574 $572 $571 $569 $567 $348 

2008 $575 $573 $571 $569 $567 $566 $564 $562 $345 

NonbatteryIC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $943 $941 $578 $577 $576 $575 $574 $574 $573 

2008 $939 $937 $575 $574 $574 $573 $572 $571 $570 

NonbatteryIC Midsize 

PC/Perf PC

2010 $1,053 $1,050 $645 $644 $643 $642 $641 $640 $639 

2008 $1,041 $1,039 $638 $637 $636 $635 $634 $633 $632 

NonbatteryIC Large 

PC/Perf PC

2010 $1,247 $1,244 $764 $763 $762 $761 $759 $758 $757 

2008 $1,219 $1,217 $747 $746 $745 $744 $743 $741 $740 

NonbatteryIC Midsize LT 

Minivan

2010 $1,162 $1,160 $712 $711 $710 $709 $708 $707 $706 

2008 $1,150 $1,148 $705 $704 $703 $702 $701 $700 $699 

NonbatteryIC Small LT 2010 $1,015 $1,013 $622 $621 $620 $619 $618 $617 $616 

2008 $996 $994 $610 $610 $609 $608 $607 $606 $605 

NonbatteryIC Large LT 2010 $1,227 $1,224 $752 $751 $749 $748 $747 $746 $745 

2008 $1,216 $1,213 $745 $744 $743 $742 $741 $739 $738 

Battery TC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $1,145 $1,122 $1,099 $1,077 $1,056 $1,035 $1,015 $996 $822 

2008 $1,135 $1,111 $1,089 $1,067 $1,046 $1,025 $1,006 $986 $814 

Battery TC Midsize 

PC/Perf PC

2010 $1,278 $1,252 $1,227 $1,202 $1,179 $1,155 $1,133 $1,111 $918 

2008 $1,264 $1,239 $1,213 $1,189 $1,166 $1,143 $1,121 $1,099 $907 

Battery TC Large 

PC/Perf PC

2010 $1,500 $1,469 $1,440 $1,411 $1,383 $1,356 $1,330 $1,304 $1,077 

2008 $1,480 $1,449 $1,420 $1,392 $1,364 $1,337 $1,311 $1,286 $1,062 

Battery TC Midsize LT 

Minivan

2010 $1,386 $1,358 $1,331 $1,304 $1,278 $1,253 $1,229 $1,205 $995 

2008 $1,383 $1,355 $1,327 $1,301 $1,275 $1,250 $1,226 $1,202 $993 

Battery TC Small LT 2010 $1,245 $1,220 $1,195 $1,171 $1,148 $1,125 $1,104 $1,082 $894 

2008 $1,230 $1,205 $1,181 $1,157 $1,134 $1,112 $1,090 $1,069 $883 

Battery TC Large LT 2010 $1,609 $1,576 $1,544 $1,513 $1,483 $1,454 $1,426 $1,399 $1,155 

2008 $1,595 $1,562 $1,531 $1,500 $1,470 $1,442 $1,414 $1,386 $1,145 

NonbatteryTC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $2,418 $2,386 $1,994 $1,965 $1,936 $1,908 $1,881 $1,854 $1,827 

2008 $2,407 $2,375 $1,985 $1,956 $1,927 $1,899 $1,872 $1,845 $1,819 
 Technologies Considered in the Agencies’ Analysis

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NonbatteryTC Midsize 

PC/Perf PC

2010 $2,698 $2,663 $2,225 $2,192 $2,160 $2,129 $2,098 $2,068 $2,039 

2008 $2,668 $2,633 $2,201 $2,168 $2,137 $2,106 $2,075 $2,046 $2,016 

NonbatteryTC Large 

PC/Perf PC

2010 $3,196 $3,155 $2,636 $2,597 $2,559 $2,522 $2,486 $2,450 $2,415 

2008 $3,125 $3,085 $2,577 $2,540 $2,503 $2,466 $2,431 $2,396 $2,362 

NonbatteryTC Midsize LT 

Minivan

2010 $2,979 $2,940 $2,457 $2,421 $2,386 $2,351 $2,317 $2,284 $2,252 

2008 $2,949 $2,910 $2,432 $2,396 $2,361 $2,327 $2,294 $2,261 $2,229 

NonbatteryTC Small LT 2010 $2,602 $2,568 $2,146 $2,115 $2,084 $2,054 $2,024 $1,995 $1,966 

2008 $2,554 $2,520 $2,106 $2,075 $2,045 $2,015 $1,986 $1,958 $1,930 

NonbatteryTC Large LT 2010 $3,144 $3,103 $2,593 $2,555 $2,518 $2,482 $2,446 $2,411 $2,376 

2008 $3,116 $3,076 $2,570 $2,533 $2,496 $2,460 $2,424 $2,389 $2,355 

3.4.3.6.4 Plug-In Hybrid 

Plug-In Hybrid Electric Vehicles (PHEVs) are very similar to Hybrid Electric 

Vehicles, but with three significant functional differences. The first is the addition of a means 

to charge the battery pack from an outside source of electricity (e.g., the electric grid). 

Second, a PHEV would have a larger battery pack with more energy storage, and a greater 

capability to be discharged. Finally, a PHEV would have a control system that allows the 

battery pack to be significantly depleted during normal operation. 

Table 3-52 below, illustrates how PHEVs compare functionally to both hybrid electric 

vehicles (HEV) and electric vehicles (EV). These characteristics can change significantly 

within each vehicle class/subclass, so this is simply meant as an illustration of the general 

characteristics. In reality, the design options are so varied that all these vehicles exist on a 

continuum with HEVs on one end and EVs on the other. 

Table 3-52 Conventional, HEVs, PHEVs, and EVs Compared 

 Increasing Electrification

Attribute Conventional HEV PHEV EV

Drive Power Engine Blended 

Engine/Electric

Blended 

Engine/Electric Electric

Engine Size Full Size Full Size or Smaller Smaller or Much 

Smaller No Engine

Electric Range None None to Very Short Short to Medium Medium to Long

Battery Charging None On-Board Grid/On-Board Grid Only

Deriving some of their propulsion energy from the electric grid provides several 

advantages for PHEVs. PHEVs offer a significant opportunity to replace petroleum used for 

transportation energy with domestically-produced electricity. The reduction in petroleum 

usage does, of course, depend on the amount of electric drive the vehicle is capable of under 

its duty cycle. PHEVs also provide electric utilities the possibility to increase electric 

generation during “off-peak” periods overnight when there is excess generation capacity and 
 Technologies Considered in the Agencies’ Analysis

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electricity prices are lower. Utilities like to increase this “base load” because it increases 

overall system efficiency and lowers average costs. PHEVs can lower localized emissions of 

criteria pollutants and air toxics especially in urban areas by operating on electric power. The 

emissions from the power generation occur outside the urban area at the power generation 

plant which provides health benefits for residents of the more densely populated urban areas 

by moving emissions of ozone precursors out of the urban air shed. Unlike most other 

alternative fuel technologies, PHEVs can initially use an existing infrastructure for refueling 

(charging and liquid refueling) so investments in infrastructure may be reduced. 

In analyzing the impacts of grid-connected vehicles like PHEVs and EVs, the 

emissions from the electrical generation can be accounted for if a full upstream and 

downstream analysis is desired. While this issue is being studied on an on-going basis, 

upstream CO2 emissions are not unique to grid-connected technologies and so are not 

included in this analysis. The respective agencies’ RIAs and NHTSA’s EIS have more 

information on upstream emissions. 

PHEVs will be considerably more costly than conventional vehicles and some other 

advanced technologies due to the fact that PHEVs require both conventional internal 

combustion engine and electrical driving system and the larger expensive battery pack. To 

take full advantage of their capability, consumers would have to be willing to charge the 

vehicles during electricity off-peak hours during the night, and would need access to electric 

power where they park their vehicles. For many urban dwellers who may park on the street, 

or in private or public lots or garages, charging may not be practical. Charging may be 

possible at an owner’s place of work, but that would increase grid loading during peak hours 

which would eliminate some of the benefits to utilities of off-peak charging versus on-peak. 

Oil savings will still be the same in this case assuming the vehicle can be charged fully. 

The effectiveness potential of PHEVs depends on many factors, the most important 

being the energy storage capacity designed into the battery pack. To estimate the fuel 

consumption and tailpipe CO2 reduction potential of PHEVs, EPA has developed an in-house 

vehicle energy model (PEREGRIN) to estimate the fuel consumption/CO2 emissions 

reductions of PHEVs. This model is based on the PERE (Physical Emission Rate Estimator) 

physics-based model used as a fuel consumption input for EPA’s MOVES mobile source 

emissions model. 

How EPA Estimates PHEV Effectiveness

The PHEV small car, large car, minivan and small trucks were modeled using 

parameters from a midsize car similar to today’s hybrids and scaled to each vehicle’s weight. 

The large truck PHEV was modeled separately assuming no engine downsizing. PHEVs can 

have a wide variation in the All Electric Range (AER) that they offer. Some PHEVs are of 

the “blended” type where the engine is on during most of the vehicle operation, but the 

proportion of electric energy that is used to propel the vehicle is significantly higher than that 

used in a PSHEV or 2MHEV. Each PHEV was modeled with enough battery capacity for a 

20-mile-equivalent AER and a power requirement to provide similar performance to a hybrid 

vehicle. 20 miles was selected because it offers a good compromise for vehicle performance, 
 Technologies Considered in the Agencies’ Analysis

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weight, battery packaging and cost. Given expected near-term battery capability, a 20 mile 

range represents the likely capability that will be seen in PHEVs in the near-to-mid term. 

To calculate the total energy use of a PHEV, the PHEV can be thought of as operating 

in two distinct modes, electric (EV) mode, and hybrid (HEV) mode. At the tailpipe, the CO2

emissions during EV operation are zero. The EV mode fuel economy can then be combined 

with the HEV mode fuel economy using the Utility Factor calculation in SAE J1711 to 

determine a total MPG value for the vehicle. (See Table 3-53) 

Table 3-53 Sample Calculation of PHEV Gasoline-Equivalent CO2 Reduction 

Midsize Car Large Truck

EV energy comb (0.55 city / 0.45 hwy) 0.252 kwh/mi 0.429 kwh/mi

EV range (from PEREGRIN) 20 miles 20 miles

SAE J1711 utility factor 0.30 0.30

HEV mode comb FE (0.55 city / 0.45 hwy) 49.1 mpg 25.6 mpg

Total UF-adjusted FE (UF*FCEV + (1-UF)*FCHEV) 70.1 mpg 36.6 mpg

Baseline FE 29.3 mpg 19.2 mpg

Percent FE gain 139% 90%

Percent CO2reduction -58% -47%

Calculating a total fuel consumption and tailpipe CO2 reduction based on model 

outputs and the Utility Factor calculations results in a 58 percent reduction for small cars, 

large cars, minivans, and small trucks. For large trucks, the result is a 47 percent reduction. 

The lower improvement is due to less engine downsizing in the large truck class. 

How NHTSA Estimates PHEV Effectiveness

For purposes of CAFE analysis, we assume that all future PHEVs during the 

rulemaking timeframe will meet the range requirements to qualify as a dual fuel vehicle. 

When calculating the fuel economy of a dual-fuel PHEV, NHTSA uses a petroleum 

equivalency factor for electricity consumption as stated in 49 U.S.C. 32904 and 32905. 

When deciding PHEV and EV effectiveness, NHTSA referenced the fuel economy of 

3 pairs of vehicles for which NHTSA has fuel economy data in the CAFE database. These 

three vehicles pairs are MiniE electric vehicle versus gasoline powered Mini with automatic 

transmission, Tesla Roadster electric vehicle versus gasoline powered rear-wheel-drive Lotus 

Elise Sedan with a 6-speed manual transmission, and Nissan Leaf electric vehicle versus 

gasoline powered Nissan Sentra with automatic transmission. The fuel economy and fuel 

consumption for the first two pairs are shown in Table 3-54. Nissan Leaf information is used 

but not shown in the table because it is confidential information. Because technologies are 

applied in the CAFE model in an incremental manner, the effectiveness for each technology is 

incremental to the previous technology. In the electrification decision tree of the CAFE 

model, the order of technology selection starts from gasoline only powertrain, then moves to 

strong hybrid, to plug-in hybrid electric vehicle, and finally to electric vehicle. So the 

incremental effectiveness for each step has to be defined. 
 Technologies Considered in the Agencies’ Analysis

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Table 3-54 EV Fuel Economy and Fuel Consumption 

104 mile range (Mini website) Fuel economy (mpg) Fuel consumption (gpm)

MiniE (mpg) 342.4 0.0029206

Mini Gas ATX (mpg) 38.6 0.0259067

227 mile range (EPA)

Tesla Roadster 346.8 0.0028835

Lotus Elise sedan M6 RWD 30.6 0.0326797

In order to calculate the effectiveness of PHEV for purposes of a CAFE standard, fuel 

economy for strong hybrid electric vehicle (SHEV) is calculated first using the incremental 

effectiveness of strong hybrid from LPM model which is around 46 percent. For example, the 

derived fuel economy for SHEV based on Mini Gas ATX is 71.7 mpg. Then the fuel economy 

from gasoline source for PHEV is assumed to be the same as SHEV fuel economy, i.e. 71.7 

mpg in the case of Mini E. The petroleum equivalent fuel economy from the electricity source 

is set to be the same as the EV fuel economy, e.g., 342.4 mpg in the case of Mini E. The 

combined fuel economy for PHEV is calculated using the 50-50 weighting factor as follows. 

Consistent with 49 U.S.C. 32904 and 32905, NHTSA is using a 50-50 weighting 

factor in the calculation above for CAFE model analysis of PHEV through 2019. After 2019, 

NHTSA will use the utility factor method defined by SAE standard J1711 for calculating 

CAFE fuel economy of PHEV. NHTSA expects that a PHEV with a 30 mile charge depleting 

range may reasonably represent the PHEVs that manufacturers may produce in MYs 2017 to 

2025. According to SAE standard J2841, a vehicle with 30 mile charge depleting range has a 

0.668 city specific utility factor and a 0.337 highway specific utility factor, which together 

give a 0.52 combined utility factor (55% city/45% highway split). Therefore NHTSA selected 

a PHEV with a 30 mile range for the CAFE model analysis, and the selection of a PHEV with 

a 30 mile range maintains continuity between pre-2020 and post-2020 PHEV fuel economy 

calculations. NHTSA assumes a 0.50 utility factor for MY2020 and beyond. In the FRM 

analysis, consistent with the proposal, EPA models a 20-mile range and a 40-mile range 

PHEV. 

The incremental fuel consumption reduction for PHEV is then calculated in relative to 

strong HEV. Using the example of Mini E, the incremental fuel consumption reduction for 

PHEV relative to SHEV is 39.5 percent as shown below. 
 Technologies Considered in the Agencies’ Analysis

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Table 3-55 lists the incremental effectiveness calculation for two pairs of vehicles, 

MiniE and Tesla Roaster. Incremental fuel consumption calculation for PHEV based on 

Nissan Leaf is not shown in Table 3-55 due to confidentiality of the fuel economy rating. The 

derived incremental effectiveness for Nissan Leaf is 40.6%. The average incremental 

effectiveness of these three pairs of vehicles is 40.65 percent which is used in CAFE 

modeling. 

Table 3-55 Incremental Effectiveness Calculation for purposes of CAFE modeling 

Mini E

Gasoline SHEV2 PHEV1 EV1

Combined Fuel Economy [mpg] 38.6 71.7 118.6 342.4

Gasoline Fuel Economy [mpg] 71.7 71.7

Electric Petroleum Equivalent Fuel 

Economy [mpg] 342.4

Combined Fuel Consumption[gpm] 

0.0139414 

 

0.0084310 

 

0.0029206 

Gasoline Fuel Consumption [gpm] 

0.0139414 

 

0.0139414 

Incremental Combined Fuel Consumption 

[%] 39.5% 65.4%

Gasoline Weighing Factor[%] 50% 0%

Electricity Weighing Factor [%] 50% 100%

Tesla

Gasoline SHEV2 PHEV1 EV1

Combined Fuel Economy [mpg] 30.6 56.7 97.4 346.8

Gasoline Fuel Economy [mpg] 56.7 56.7

Electric Petroleum Equivalent Fuel 

Economy [mpg] 346.8

Combined Fuel Consumption[gpm] 

0.017647 

 

0.0102653 

 

0.0028835 

Gasoline Fuel Consumption [gpm] 

0.017647 

 

0.0176471 

Incremental Combined Fuel Consumption 

[%] 41.8% 71.9%

Gasoline Weighing Factor[%] 50% 0%
 Technologies Considered in the Agencies’ Analysis

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Electricity Weighing Factor [%] 50% 100%

Once the fuel economy of the PHEV is calculated, the effectiveness of PHEV 

incremental to EV can be calculated similarly using the formula below. 

The average effectiveness for the three pairs of vehicles of 68.54% is used in CAFE 

modeling. 

The cost of PHEV consists of three parts, the cost for battery, the cost for non-battery 

systems and the cost for charger and the labor to install it. The battery sizing is calculated as 

in Section 3.4.3.8 and listed in Table 3-56. Costs for PHEVs without mass reduction as used 

in the Volpe model are listed in Table 3-57 to Table 3-61. NHTSA accounts the cost impact 

from the interaction between mass reduction and sizing of the electrification system (battery 

and non-battery system) as a cost synergy as described in section 3.4.3.9. Sections 3.4.3.9

and 3.4.3.10 contain the cost for PHEVs with mass reduction as used in EPA’s OMEGA 

model. PHEV20 and PHEV40 are sized by EPA with the methodologies discussed in section 

3.4.3.8. 

Table 3-56 NHTSA Battery Sizes for PHEV30 Hybrid Applied in Volpe Model without Mass Reduction 

(kWh) 

Baseline 

Fleet

Subcompact 

PC/ Perf PC

Compact PC/ 

Perf PC

Midsize 

PC/Perf PC

Large PC/Perf 

PC

Midsize LT 

Minivan Small LT Large LT

2008 10.42 12.82 15.21 17.09 13.48 19.73

2010 10.81 13.13 15.79 16.94 13.69 20.27

The battery pack DMCs for PHEV20 and PHEV40 are calculated using ANL’s 

BatPaC model. NHTSA modeled a PHEV 30 for this final rule, for which NHTSA averaged 

the costs of PHEV20s and PHEV40s. 

The agencies have applied a high complexity ICM to non-battery component cost for 

PHEV and PHEV charger, which switch from short term value of 1.56 to long term value of 

1.35 at 2018. The agencies applied a higher ICM factor to the battery of PHEV due to the fact 

that it a more complex technology. The ICM for PHEV battery switches from short term 

value of 1.77 to long term value of 1.50 at 2024. 

Table 3-57 NHTSA Costs Applied in Volpe Model for PHEV30 with No Mass Reduction (2010$) 
 Technologies Considered in the Agencies’ Analysis

3-133 

Tech. Cost 

Type

NHTSA 

Vehicle 

Class

Baseline 

Fleet

2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery DMC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $6,208 $8,259 $8,259 $6,607 $6,607 $6,607 $6,607 $6,607 $5,286 

2008 $6,095 $8,097 $8,097 $6,477 $6,477 $6,477 $6,477 $6,477 $5,182 

Battery DMC Midsize 

PC/Perf PC

2010 $7,415 $5,932 $5,932 $4,746 $4,746 $4,746 $4,746 $4,746 $3,797 

2008 $7,251 $5,801 $5,801 $4,640 $4,640 $4,640 $4,640 $4,640 $3,712 

Battery DMC Large 

PC/Perf PC

2010 $9,835 $7,868 $7,868 $6,294 $6,294 $6,294 $6,294 $6,294 $5,035 

2008 $9,610 $7,688 $7,688 $6,150 $6,150 $6,150 $6,150 $6,150 $4,920 

NonbatteryDMC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $2,586 $2,535 $2,484 $2,434 $2,386 $2,338 $2,291 $2,245 $2,200 

2008 $2,522 $2,472 $2,422 $2,374 $2,326 $2,280 $2,234 $2,190 $2,146 

NonbatteryDMC Midsize 

PC/Perf PC

2010 $3,252 $3,187 $3,124 $3,061 $3,000 $2,940 $2,881 $2,824 $2,767 

2008 $3,132 $3,070 $3,008 $2,948 $2,889 $2,831 $2,775 $2,719 $2,665 

NonbatteryDMC Large 

PC/Perf PC

2010 $4,685 $4,591 $4,499 $4,409 $4,321 $4,235 $4,150 $4,067 $3,986 

2008 $4,494 $4,405 $4,316 $4,230 $4,145 $4,063 $3,981 $3,902 $3,824 

Charger DMC All 2008/2010 $210 $168 $168 $134 $134 $134 $134 $134 $108 

Charger 

Labor

DMC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 

Battery IC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $2,671 $2,579 $2,579 $2,506 $2,506 $2,506 $2,506 $2,506 $1,578 

2008 $2,622 $2,532 $2,532 $2,460 $2,460 $2,460 $2,460 $2,460 $1,550 

Battery IC Midsize 

PC/Perf PC

2010 $3,190 $3,081 $3,081 $2,993 $2,993 $2,993 $2,993 $2,993 $1,885 

2008 $3,119 $3,012 $3,012 $2,927 $2,927 $2,927 $2,927 $2,927 $1,844 

Battery IC Large 

PC/Perf PC

2010 $4,231 $4,086 $4,086 $3,970 $3,970 $3,970 $3,970 $3,970 $2,501 

2008 $4,134 $3,993 $3,993 $3,879 $3,879 $3,879 $3,879 $3,879 $2,444 

NonbatteryDMC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $1,655 $1,651 $1,014 $1,012 $1,011 $1,009 $1,008 $1,006 $1,005 

2008 $1,614 $1,610 $989 $987 $986 $984 $983 $981 $980 

NonbatteryDMC Midsize 

PC/Perf PC

2010 $2,081 $2,077 $1,275 $1,273 $1,271 $1,269 $1,267 $1,265 $1,264 

2008 $2,004 $2,000 $1,228 $1,226 $1,224 $1,222 $1,220 $1,219 $1,217 

NonbatteryDMC Large 

PC/Perf PC

2010 $2,997 $2,991 $1,836 $1,834 $1,831 $1,828 $1,825 $1,823 $1,820 

2008 $2,875 $2,869 $1,762 $1,759 $1,756 $1,754 $1,751 $1,749 $1,746 

Charger IC All 2008/2010 $67 $65 $65 $62 $62 $62 $62 $62 $37 

Charger 

Labor

IC All 2008/2010 $0 $0 $0 $0 $0 $0 $0 $0 $0 
 Technologies Considered in the Agencies’ Analysis

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Battery TC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $8,878 $7,545 $7,545 $6,479 $6,479 $6,479 $6,479 $6,479 $4,757 

2008 $8,717 $7,408 $7,408 $6,361 $6,361 $6,361 $6,361 $6,361 $4,670 

Battery TC Midsize 

PC/Perf PC

2010 $10,605 $9,013 $9,013 $7,739 $7,739 $7,739 $7,739 $7,739 $5,682 

2008 $10,370 $8,813 $8,813 $7,567 $7,567 $7,567 $7,567 $7,567 $5,556 

Battery TC Large 

PC/Perf PC

2010 $14,066 $11,954 $11,954 $10,264 $10,264 $10,264 $10,264 $10,264 $7,536 

2008 $13,744 $11,681 $11,681 $10,030 $10,030 $10,030 $10,030 $10,030 $7,364 

NonbatteryTC Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $4,241 $4,186 $3,498 $3,446 $3,396 $3,347 $3,299 $3,251 $3,205 

2008 $4,136 $4,082 $3,411 $3,361 $3,312 $3,264 $3,217 $3,171 $3,126 

NonbatteryTC Midsize 

PC/Perf PC

2010 $5,333 $5,264 $4,399 $4,334 $4,271 $4,209 $4,148 $4,089 $4,031 

2008 $5,136 $5,069 $4,236 $4,174 $4,113 $4,054 $3,995 $3,938 $3,882 

NonbatteryTC Large 

PC/Perf PC

2010 $7,682 $7,582 $6,336 $6,243 $6,152 $6,063 $5,975 $5,890 $5,806 

2008 $277 $233 $233 $197 $197 $197 $197 $197 $145 

Charger TC All 2008/2010 $277 $233 $233 $197 $197 $197 $197 $197 $145 

Charger 

Labor

TC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 

3.4.3.6.5 Electric vehicles 

Electric vehicles (EV) – are vehicles with all-electric drive and with vehicle systems 

powered by energy-optimized batteries charged primarily from grid electricity. While the 

2016 FRM did not anticipate a significant penetration of EVs, in this analysis, EVs with 

several ranges have been included. The GHG effectiveness is unchanged from estimates used 

for 2016 model year vehicles in the 2012-2016 final rule which is 100 percent GHG 

reduction. Per 49 U.S.C. 32904, NHTSA uses the Petroleum Equivalency Factor (PEF) in 

calculating the effectiveness for EVs as stated in the section above for PHEV. The PEF is 

determined by the U.S. Department of Energy as specified in 10 CFR Part 474. The PEF 

accounts for U.S. average fossil-fuel electricity generation and transmission efficiencies, 

petroleum refining and distribution efficiency, the energy content of gasoline, and includes a 

0.15 divisor to incentivize the use of electricity in vehicles. The current PEF for electricity is 

82.049 kWh per gallon of gasoline. 

Once the fuel economy of the PHEV is calculated as shown in the previous section, 

the effectiveness of PHEV incremental to EV can be calculated similarly using the formula 

below. 
 Technologies Considered in the Agencies’ Analysis

3-135 

The average effectiveness for the three pairs of vehicles of 68.54% is used in CAFE 

modeling. 

Battery costs assume that battery packs for EV applications will be designed to last for 

the full useful life of the vehicle at a useable state of charge equivalent to 80% of the nominal 

battery pack capacity. NHTSA included two levels of EVs, a 75-mile range EV and a 150-

mile range EV in this FRM analysis, consistent with the proposal. As this technology is 

entering the market, it is expected that the OEMs will try to keep the cost low at the beginning 

so that there will be more penetration. Due to the high cost of the battery packs at this early 

stage of EVs, OEM will try to limit the battery pack size to reduce cost. Also the early 

adopters for this technology are normally urban drivers and range anxiety is not believed to be 

a big concern to them. Therefore NHTSA applied a 75-mile range EV for early adoption of 

this technology in the market, up to 5% penetration. As the technology develops and as the 

market penetration increases beyond 5%, NHTSA expects that OEMs would provide longer 

driving range to help the consumers overcome range anxiety. NHTSA applied 150-mile EV 

for this broad market adoption of this technology. 

The cost of an EV consists of three parts, cost of battery pack, cost of non-battery systems, 

and cost of charger and charger installation labor. An algorithm was used to select battery 

sizes. The algorithm is described in Section 3.4.3.8 and the battery sizes applied in the Volpe 

model for each type of EV and vehicle subclass are listed in Table 3-63. 

Table 3-58 NHTSA Battery Sizes for EVs Applied in Volpe Model with No Mass Reduction (kWh) 

Baseline 

Fleet

Subcompact 

PC/ Perf PC

Compact PC/ 

Perf PC

Midsize 

PC/Perf PC

Large 

PC/Perf PC

Midsize LT 

Minivan Small LT Large LT

EV75

2008 22.79 28.03 33.28 n/a 29.48 n/a

2010 23.65 28.72 34.54 n/a 29.95 n/a

EV100

2008 30.39 37.38 44.37 n/a 39.30 n/a

2010 31.54 38.30 46.05 n/a 39.94 n/a

EV150

2008 45.58 56.07 66.55 n/a 58.96 n/a

2010 47.31 57.45 69.08 n/a 59.90 n/a
 Technologies Considered in the Agencies’ Analysis

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The agencies have applied a high complexity ICM to non-battery component cost for 

EVs and EV chargers, which switch from short term value of 1.56 to long term value of 1.35 

at 2018. The agencies applied a higher ICM factor to the battery of EVs due to the fact that it 

a more complex technology. The ICM for EV battery switches from short term value of 1.77 

to long term value of 1.50 at 2024. The agencies present costs of EVs in Sections 3.4.3.9 and 

3.4.3.10. The costs of EVs without mass reduction as applied in Volpe model are listed in 

Table 3-58 to Table 3-60. NHTSA accounts the cost impact from the interaction between 

mass reduction and sizing of electrification system (battery and non-battery system) as cost 

synergy as described in section 3.4.3.9.

Table 3-59 NHTSA Costs for EV75 Applied in Volpe Model with No Mass Reduction (2010$) 

Tech. Cost 

Type

NHTSA 

Vehicle 

Class

Baseline 

Fleet 2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $10,324 $8,259 $8,259 $6,607 $6,607 $6,607 $6,607 $6,607 $5,286

2008 $10,121 $8,097 $8,097 $6,477 $6,477 $6,477 $6,477 $6,477 $5,182

Battery DMC Midsize 

PC/Perf PC

2010 $12,140 $9,712 $9,712 $7,769 $7,769 $7,769 $7,769 $7,769 $6,215

2008 $11,881 $9,505 $9,505 $7,604 $7,604 $7,604 $7,604 $7,604 $6,083

Battery DMC Large 

PC/Perf PC

2010 $15,634 $12,507 $12,507 $10,006 $10,006 $10,006 $10,006 $10,006 $8,005

2008 $15,238 $12,190 $12,190 $9,752 $9,752 $9,752 $9,752 $9,752 $7,802

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $410 $398 $386 $375 $363 $352 $345 $338 $332

2008 $354 $343 $333 $323 $313 $304 $298 $292 $286

Nonbattery DMC Midsize 

PC/Perf PC

2010 $1,267 $1,229 $1,193 $1,157 $1,122 $1,088 $1,067 $1,045 $1,024

2008 $1,156 $1,122 $1,088 $1,055 $1,024 $993 $973 $954 $935

Nonbattery DMC Large 

PC/Perf PC

2010 $2,236 $2,169 $2,104 $2,041 $1,980 $1,920 $1,882 $1,844 $1,808

2008 $2,080 $2,018 $1,957 $1,899 $1,842 $1,786 $1,751 $1,716 $1,681

Charger DMC All 2008/2010 $395 $316 $316 $253 $253 $253 $253 $253 $202

Charger 

Labor DMC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010

Battery IC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $4,441 $4,289 $4,289 $4,167 $4,167 $4,167 $4,167 $4,167 $2,625

2008 $4,354 $4,205 $4,205 $4,086 $4,086 $4,086 $4,086 $4,086 $2,573

Battery IC Midsize 

PC/Perf PC

2010 $5,222 $5,044 $5,044 $4,901 $4,901 $4,901 $4,901 $4,901 $3,087

2008 $5,111 $4,936 $4,936 $4,796 $4,796 $4,796 $4,796 $4,796 $3,021

Battery IC Large 

PC/Perf PC

2010 $6,725 $1,717 $1,712 $1,708 $1,703 $1,699 $1,696 $1,693 $1,090

2008 $6,555 $6,331 $6,331 $6,151 $6,151 $6,151 $6,151 $6,151 $3,874
 Technologies Considered in the Agencies’ Analysis

3-137 

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $316 $315 $314 $313 $313 $312 $311 $311 $200

2008 $272 $272 $271 $270 $269 $269 $268 $268 $172

Nonbattery DMC Midsize 

PC/Perf PC

2010 $976 $973 $970 $968 $965 $963 $961 $960 $618

2008 $890 $888 $885 $883 $881 $878 $877 $876 $563

Nonbattery DMC Large 

PC/Perf PC

2010 $1,722 $18 $18 $18 $18 $18 $18 $18 $10

2008 $1,602 $1,597 $1,593 $1,588 $1,584 $1,580 $1,578 $1,575 $1,014

Charger IC All 2008/2010 $126 $121 $121 $117 $117 $117 $117 $117 $70

Charger 

Labor IC All 2008/2010 $0 $0 $0 $0 $0 $0 $0 $0 $0

Battery TC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $14,765 $12,548 $12,548 $10,775 $10,775 $10,775 $10,775 $10,775 $7,911

2008 $14,475 $12,302 $12,302 $10,563 $10,563 $10,563 $10,563 $10,563 $7,755

Battery TC Midsize 

PC/Perf PC

2010 $17,362 $14,755 $14,755 $12,670 $12,670 $12,670 $12,670 $12,670 $9,302

2008 $16,992 $14,441 $14,441 $12,400 $12,400 $12,400 $12,400 $12,400 $9,104

Battery TC Large 

PC/Perf PC

2010 $22,359 $19,002 $19,002 $16,317 $16,317 $16,317 $16,317 $16,317 $11,980

2008 $21,793 $18,521 $18,521 $15,903 $15,903 $15,903 $15,903 $15,903 $11,676

Nonbattery TC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $726 $713 $700 $688 $676 $664 $657 $649 $532

2008 $626 $615 $603 $593 $582 $572 $566 $559 $458

Nonbattery TC Midsize 

PC/Perf PC

2010 $2,243 $2,203 $2,163 $2,125 $2,087 $2,051 $2,028 $2,005 $1,642

2008 $2,047 $2,010 $1,973 $1,938 $1,904 $1,871 $1,850 $1,829 $1,498

Nonbattery TC Large 

PC/Perf PC

2010 $3,959 $3,887 $3,817 $3,749 $3,683 $3,619 $3,578 $3,538 $2,897

2008 $3,682 $3,615 $3,550 $3,487 $3,426 $3,367 $3,328 $3,291 $2,695

Charger IC All 2008/2010 $521 $437 $437 $370 $370 $370 $370 $370 $272

Charger 

Labor IC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010

Table 3-60 NHTSA Costs for EV100 Applied in Volpe Model with No Mass Reduction (2010$) 

Tech. Cost 

Type

NHTSA 

Vehicle 

Class

Baseline 

Fleet 2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery DMC

Subcompact 

PC/Perf PC

Compact 

PC/Perf PC

2010 $12,341 $9,873 $9,873 $7,898 $7,898 $7,898 $7,898 $7,898 $6,319 

2008 $12,063 $9,651 $9,651 $7,720 $7,720 $7,720 $7,720 $7,720 $6,176 

Battery DMC Midsize 

PC/Perf PC

2010 $14,159 $11,327 $11,327 $9,062 $9,062 $9,062 $9,062 $9,062 $7,250 

2008 $13,919 $11,135 $11,135 $8,908 $8,908 $8,908 $8,908 $8,908 $7,127 
 Technologies Considered in the Agencies’ Analysis

3-138 

Battery DMC Large 

PC/Perf PC

2010 $17,482 $13,985 $13,985 $11,188 $11,188 $11,188 $11,188 $11,188 $8,951 

2008 $17,025 $13,620 $13,620 $10,896 $10,896 $10,896 $10,896 $10,896 $8,717 

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $410 $398 $386 $375 $363 $352 $345 $338 $332 

2008 $354 $343 $333 $323 $313 $304 $298 $292 $286 

Nonbattery DMC Midsize 

PC/Perf PC

2010 $1,267 $1,229 $1,193 $1,157 $1,122 $1,088 $1,067 $1,045 $1,024 

2008 $1,156 $1,122 $1,088 $1,055 $1,024 $993 $973 $954 $935 

Nonbattery DMC Large 

PC/Perf PC

2010 $2,236 $2,169 $2,104 $2,041 $1,980 $1,920 $1,882 $1,844 $1,808 

2008 $2,080 $2,018 $1,957 $1,899 $1,842 $1,786 $1,751 $1,716 $1,681 

Charger DMC All 2008/2010 $395 $316 $316 $253 $253 $253 $253 $253 $202 

Charger 

Labor DMC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 

Battery IC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $5,309 $5,127 $5,127 $4,982 $4,982 $4,982 $4,982 $4,982 $3,138 

2008 $5,189 $5,012 $5,012 $4,870 $4,870 $4,870 $4,870 $4,870 $3,067 

Battery IC Midsize 

PC/Perf PC

2010 $6,091 $5,883 $5,883 $5,716 $5,716 $5,716 $5,716 $5,716 $3,600 

2008 $5,988 $5,783 $5,783 $5,619 $5,619 $5,619 $5,619 $5,619 $3,539 

Battery IC Large 

PC/Perf PC

2010 $7,520 $7,263 $7,263 $7,057 $7,057 $7,057 $7,057 $7,057 $4,445 

2008 $7,324 $7,073 $7,073 $6,873 $6,873 $6,873 $6,873 $6,873 $4,329 

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $316 $315 $314 $313 $313 $312 $311 $311 $200 

2008 $272 $272 $271 $270 $269 $269 $268 $268 $172 

Nonbattery DMC Midsize 

PC/Perf PC

2010 $976 $973 $970 $968 $965 $963 $961 $960 $618 

2008 $890 $888 $885 $883 $881 $878 $877 $876 $563 

Nonbattery DMC Large 

PC/Perf PC

2010 $1,722 $1,717 $1,712 $1,708 $1,703 $1,699 $1,696 $1,693 $1,090 

2008 $1,602 $1,597 $1,593 $1,588 $1,584 $1,580 $1,578 $1,575 $1,014 

Charger IC All 2008/2010 $126 $121 $121 $117 $117 $117 $117 $117 $70 

Charger 

Labor IC All 2008/2010 $0 $0 $0 $0 $0 $0 $0 $0 $0 

Battery TC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $17,650 $15,000 $15,000 $12,880 $12,880 $12,880 $12,880 $12,880 $9,457 

2008 $17,253 $14,662 $14,662 $12,590 $12,590 $12,590 $12,590 $12,590 $9,244 

Battery TC Midsize 

PC/Perf PC

2010 $20,250 $17,210 $17,210 $14,778 $14,778 $14,778 $14,778 $14,778 $10,850

2008 $19,907 $16,918 $16,918 $14,527 $14,527 $14,527 $14,527 $14,527 $10,666

Battery TC Large 

PC/Perf PC

2010 $25,002 $21,248 $21,248 $18,245 $18,245 $18,245 $18,245 $18,245 $13,396

2008 $24,349 $20,693 $20,693 $17,769 $17,769 $17,769 $17,769 $17,769 $13,046

Nonbattery TC

Subcompact 

PC/Perf PC 

Compact 

2010 $726 $713 $700 $688 $676 $664 $657 $649 $532 
 Technologies Considered in the Agencies’ Analysis

3-139 

PC/Perf PC

2008 $626 $615 $603 $593 $582 $572 $566 $559 $458 

Nonbattery TC Midsize 

PC/Perf PC

2010 $2,243 $2,203 $2,163 $2,125 $2,087 $2,051 $2,028 $2,005 $1,642 

2008 $2,047 $2,010 $1,973 $1,938 $1,904 $1,871 $1,850 $1,829 $1,498 

Nonbattery TC Large 

PC/Perf PC

2010 $3,959 $3,887 $3,817 $3,749 $3,683 $3,619 $3,578 $3,538 $2,897 

2008 $3,682 $3,615 $3,550 $3,487 $3,426 $3,367 $3,328 $3,291 $2,695 

Charger IC All 2008/2010 $521 $437 $437 $370 $370 $370 $370 $370 $272 

Charger 

Labor IC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 

Table 3-61 NHTSA Costs for EV150 Applied in Volpe Model with No Mass Reduction (2010$) 

Tech. Cost 

Type

NHTSA 

Vehicle 

Class

Baseline 

Fleet 2017 2018 2019 2020 2021 2022 2023 2024 2025

Battery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $16,369 $13,095 $13,095 $10,476 $10,476 $10,476 $10,476 $10,476 $8,381

2008 $15,939 $12,751 $12,751 $10,201 $10,201 $10,201 $10,201 $10,201 $8,161

Battery DMC Midsize 

PC/Perf PC

2010 $19,585 $15,668 $15,668 $12,534 $12,534 $12,534 $12,534 $12,534 $10,028

2008 $19,240 $15,392 $15,392 $12,313 $12,313 $12,313 $12,313 $12,313 $9,851

Battery DMC Large 

PC/Perf PC

2010 $22,552 $18,042 $18,042 $14,433 $14,433 $14,433 $14,433 $14,433 $11,547

2008 $21,936 $17,549 $17,549 $14,039 $14,039 $14,039 $14,039 $14,039 $11,231

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $410 $398 $386 $375 $363 $352 $345 $338 $332

2008 $355 $344 $334 $324 $314 $305 $299 $293 $287

Nonbattery DMC Midsize 

PC/Perf PC

2010 $1,267 $1,229 $1,193 $1,157 $1,122 $1,088 $1,067 $1,045 $1,024

2008 $1,157 $1,123 $1,089 $1,056 $1,025 $994 $974 $954 $935

Nonbattery DMC Large 

PC/Perf PC

2010 $2,236 $2,169 $2,104 $2,041 $1,980 $1,920 $1,882 $1,844 $1,808

2008 $2,082 $2,019 $1,959 $1,900 $1,843 $1,788 $1,752 $1,717 $1,682

Charger DMC All 2008/2010 $395 $316 $316 $253 $253 $253 $253 $253 $202

Charger 

Labor DMC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010

Battery IC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $7,042 $6,801 $6,801 $6,608 $6,608 $6,608 $6,608 $6,608 $4,162

2008 $6,857 $6,622 $6,622 $6,434 $6,434 $6,434 $6,434 $6,434 $4,053

Battery IC Midsize 

PC/Perf PC

2010 $8,425 $8,137 $8,137 $7,906 $7,906 $7,906 $7,906 $7,906 $4,980

2008 $8,277 $7,993 $7,993 $7,767 $7,767 $7,767 $7,767 $7,767 $4,892

Battery IC Large 2010 $9,702 $9,370 $9,370 $9,104 $9,104 $9,104 $9,104 $9,104 $5,734
 Technologies Considered in the Agencies’ Analysis

3-140 

PC/Perf PC 2008 $9,437 $9,114 $9,114 $8,855 $8,855 $8,855 $8,855 $8,855 $5,578

Nonbattery DMC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $316 $315 $314 $313 $313 $312 $311 $311 $200

2008 $273 $273 $272 $271 $270 $270 $269 $269 $173

Nonbattery DMC Midsize 

PC/Perf PC

2010 $976 $973 $970 $968 $965 $963 $961 $960 $618

2008 $891 $889 $886 $884 $881 $879 $878 $876 $564

Nonbattery DMC Large 

PC/Perf PC

2010 $1,722 $1,717 $1,712 $1,708 $1,703 $1,699 $1,696 $1,693 $1,090

2008 $1,603 $1,598 $1,594 $1,590 $1,585 $1,581 $1,579 $1,576 $1,014

Charger IC All 2008/2010 $126 $121 $121 $117 $117 $117 $117 $117 $70

Charger 

Labor IC All 2008/2010 $0 $0 $0 $0 $0 $0 $0 $0 $0

Battery TC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $23,411 $19,896 $19,896 $17,084 $17,084 $17,084 $17,084 $17,084 $12,543

2008 $22,796 $19,373 $19,373 $16,635 $16,635 $16,635 $16,635 $16,635 $12,214

Battery TC Midsize 

PC/Perf PC

2010 $28,010 $23,805 $23,805 $20,441 $20,441 $20,441 $20,441 $20,441 $15,007

2008 $27,517 $23,385 $23,385 $20,080 $20,080 $20,080 $20,080 $20,080 $14,743

Battery TC Large 

PC/Perf PC

2010 $32,254 $27,411 $27,411 $23,537 $23,537 $23,537 $23,537 $23,537 $17,281

2008 $31,372 $26,662 $26,662 $22,894 $22,894 $22,894 $22,894 $22,894 $16,809

Nonbattery TC

Subcompact 

PC/Perf PC 

Compact 

PC/Perf PC

2010 $726 $713 $700 $688 $676 $664 $657 $649 $532

2008 $628 $617 $606 $595 $585 $574 $568 $561 $460

Nonbattery TC Midsize 

PC/Perf PC

2010 $2,243 $2,203 $2,163 $2,125 $2,087 $2,051 $2,028 $2,005 $1,642

2008 $2,048 $2,011 $1,975 $1,940 $1,906 $1,873 $1,852 $1,831 $1,499

Nonbattery TC Large 

PC/Perf PC

2010 $3,959 $3,887 $3,817 $3,749 $3,683 $3,619 $3,578 $3,538 $2,897

2008 $3,685 $3,618 $3,552 $3,489 $3,428 $3,369 $3,330 $3,293 $2,697

Charger IC All 2008/2010 $521 $437 $437 $370 $370 $370 $370 $370 $272

Charger 

Labor IC All 2008/2010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010 $1,010

3.4.3.6.6 Fuel cell electric vehicles 

Fuel cell electric vehicles (FCEVs) – utilize a full electric drive platform but consume 

electricity generated by an on-board fuel cell and hydrogen fuel. Fuel cells are electrochemical devices that directly convert reactants (hydrogen and oxygen via air) into electricity, 

with the potential of achieving more than twice the efficiency of conventional internal 

combustion engines. High pressure gaseous hydrogen storage tanks are used by most 

automakers for FCEVs that are currently under development. The high pressure tanks are 

similar to those used for compressed gas storage in more than 10 million CNG vehicles 
 Technologies Considered in the Agencies’ Analysis

3-141 

worldwide, except that they are designed to operate at a higher pressure (350 bar or 700 bar 

vs. 250 bar for CNG). Due to the uncertainty of the future availability for this technology, 

FCEVs were not included in any OMEGA or Volpe model runs. 

3.4.3.7 Batteries for Mild HEV, HEV, PHEV and EV Applications 

The design of battery secondary cells can vary considerably between Stop/Start, Mild 

HEV (ISG), HEV, PHEV and EV applications. 

MHEV batteries: Due to their lower voltage (12-42 VDC) and reduced power and 

energy requirements, MHEV systems may continue to use lead-acid batteries even long term 

(2017 model year and later). MHEV battery designs differ from those of current starvedelectrolyte (typical maintenance free batteries) or flooded-electrolyte (the older style lead-acid 

batteries requiring water “top-off”) batteries used for starting, lighting and ignition (SLI) in 

automotive applications. Standard SLI batteries are primarily designed to provide highcurrent for engine start-up and then recharge immediately after startup via the vehicle’s 

charging system. Deeply discharging a standard SLI battery will greatly shorten its life. 

MHEV applications are expected to use: 

Extended-cycle-life flooded (ELF) lead-acid batteries 

Absorptive glass matt, valve-regulated lead-acid (AGM/VRLA) batteries –or –

Asymmetric lead-acid battery/capacitor hybrids (e.g., flooded ultrabatteries) 

MHEV systems using electrolytic double-layer capacitors are also under development and 

may provide improved performance and reduced cost in the post-2017 timeframe. 

Mild HEV and HEV batteries: Mild HEV and HEV applications operate in a narrow, 

short-cycling, charge-sustaining state of charge (SOC). Energy capacity in Mild HEV and 

HEV applications is somewhat limited by the ability of the battery and power electronics to 

accept charge and by space and weight constraints within the vehicle design. Mild HEV and 

HEV battery designs tend to be optimized for high power density rather than high energy 

density, with thinner cathode and anode layers and more numerous current collectors and 

separators (Figure 3-20). 

EV batteries: EV batteries tend to be optimized for high energy density and are 

considerably larger and heavier than HEV batteries in order to provide sufficient energy 

capacity. EV battery cells tend to have thicker cathode and anode layers and fewer collectors 

and separators than HEV cells. This reduces the specific cost on a per-kWh basis for EV 

battery cells relative to HEV battery cells. 

PHEV batteries: PHEV battery designs are intermediate between power-optimized 

HEV and energy-optimized EV battery cell designs. PHEV batteries must provide both 

charge depleting operation similar to an EV and charge sustaining operation similar to an 

HEV. Unlike HEV applications, charge-sustaining operation with PHEVs occurs at a 

relatively low battery state of charge (SOC) which can pose a significant challenge with 
 Technologies Considered in the Agencies’ Analysis

3-142 

respect to attaining acceptable battery cycle life. In the case of the GM Volt, this limits 

charge depleting operation to a minimum SOC of approximately 30 percent.59 An alternative 

approach for PHEV applications that has potential to allow extension of charge depletion to a 

lower battery SOC is using energy-optimized lithium-ion batteries for charge depleting 

operation in combination with the use of supercapacitors or power-optimized batteries for 

charge sustaining operation.60

Figure 3-20: Schematic representation of power and energy optimized 

prismatic-layered battery cells 

Power-split hybrid vehicles from Toyota, Ford and Nissan, integrated motor assist 

hybrid vehicles from Honda and the GM 2-mode hybrid vehicles currently use nickel-metal 

hydride (NiMH) batteries. Lithium-ion (Li-ion) batteries offer the potential to approximately 

double both the energy and power density relative to current NiMH batteries, enabling much 

more electrification of automotive drive applications such as PHEVs and EVs. 

Li-ion batteries for high-volume automotive applications differ substantially from 

those used in consumer electronics applications with respect to cathode chemistry, 

construction and cell size. Li-ion battery designs currently in production by CPI (LG-Chem) 

for the GM Volt PHEV and by AESC and GS-Yuasa (respectively) for the Nissan Leaf and 

Mitsubishi iMiEV use large-format, layered-prismatic cells assembled into battery modules. 

The modules are then combined into battery packs. 

Two families of cathode chemistries are used in large-format, automotive Li-ion 

batteries currently in production – LiMn2O4-spinel (CPI, GS-Yuasa, AESC) and LiFePO4 

(A123 Systems). Current production batteries typically use graphite anodes. Automotive LiCollector (-)

Cathode (-)

Separator

Anode (+)

Collector (+)

HEV Power-optimized Battery Cell

EV Energy-optimized Battery Cell
 Technologies Considered in the Agencies’ Analysis

3-143 

ion batteries using lithium nickel manganese cobalt (NMC) oxide cathodes with graphite 

anodes are in advanced stages of development for PHEV and EV applications. The agencies 

expect large-format Li-ion batteries to completely replace NiMH batteries for post-2017 HEV 

applications. We also expect that stacked and/or folded prismatic Li-ion cell designs will 

continue to be used for PHEV and EV applications and that NMC/graphite Li-ion batteries 

will be a mature technology for 2017-2025 light-duty vehicle applications. 

3.4.3.8 HEV, PHEV and EV System Sizing Methodology 

Battery packs are (and will continue to be) one of the most expensive components for 

EVs, PHEVs and HEVs. To obtain reasonable cost estimates for electrified vehicles, it was 

therefore important to establish a reliable approach for determining battery attributes for each 

vehicle and class. Both battery energy content (“size”) and power rating are key inputs used 

to establish costs per ANL’s battery costing model. For EVs and PHEVs in particular, battery 

size and weight are closely related, and so battery weight must be known as well. The 

following section details the steps taken to size a battery for 

a) EVs and PHEVs (at various all-electric ranges), 

b) a more simplified separate approach for MHEVs and HEVs. 

3.4.3.8.1 Battery Pack Sizing for EVs and PHEVs 

Calculation of required battery pack energy requirements for EVs and PHEVs is not 

straightforward. Because vehicle energy consumption is strongly dependent on weight, and 

battery packs are very heavy, the weight of the battery pack itself can change the energy 

required to move the vehicle. As vehicle energy consumption increases, the battery size must 

increase for a given range (in the case of EVs and PHEVs) – as a result, vehicle weight 

increases, and per-mile energy consumption increases as well, increasing the battery size, and 

so on. 

EPA built spreadsheets to estimate the required battery size for each vehicle and class. 

Listed below are the steps EPA has taken in these spreadsheets to estimate not only battery 

size, but associated weight for EVs and PHEVs of varying ranges and designs. 

1. Establish baseline FE/energy consumption 

2. Assume nominal weight of electrified vehicle (based on weight reduction 

target) 

3. Calculate vehicle energy demand at this target weight 

4. Calculate required battery energy 

5. Calculate actual battery and vehicle weight 

6. Do vehicle weight and battery size match estimated values? 
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Steps 2-6 were iterated until each assumed weight reduction target (and nominal 

vehicle weight) reconciled with required battery size and the calculated weight of each 

vehicle. 

Baseline vehicle energy consumption is estimated based on a fitted trendline for FE 

vs. inertia weight, or ETW (from FE Trends data for 2008 MY vehicles, table M-80) and 

converting to Wh/mi. This is shown in Figure 3-21.

Figure 3-21: Average fuel economy based on inertia weight (ETW) from FE Trends data 

Then, fuel economy was converted into energy consumption (assuming 33700 Wh 

energy in 1 gallon of gasoline) and used to populate a range of test weights between 2000 and 

6000 lbs. A linear trendline was used to fit this curve and then applied to estimate generic 

energy consumption for baseline vehicles of a given ETW (shown below in Figure 3-22).

y = 0.00000180x2- 0.02194637x + 85.81284974

R2 = 0.99565332

0

1 0

2 0

3 0

4 0

5 0

6 0

0 1000 2000 3000 4000 5000 6000 7000 Combined FE (mpg)

Inertia wt (lbs)

2008 Fuel Economy vs. Inertia Weight

(source: Fuel Economy Trends Report, Table M-80)
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Figure 3-22: Equivalent energy consumption (in Wh/mi) for baseline vehicles 

To calculate battery pack size, the electrified vehicle weight must first be known; to 

calculate vehicle weight, the battery pack size must first be known. This circular reference 

required an iterative solution. EPA assumed a target vehicle glider (a rolling chassis with no 

powertrain) weight reduction and applied that to the baseline curb weight. The resulting 

nominal vehicle weight was then used to calculate the vehicle energy demand. To calculate 

the energy demand (efficiency) of an electric vehicle in Wh/mi, the following information 

was needed: 

Baseline energy consumption / mpg 

Efficiency (η) improvement of electric vehicle 

Change in road loads 

In Table 3-62 below, the following definitions apply: 

Brake eff (brake efficiency) – the % amount of chemical fuel energy converted to 

energy at the engine crankshaft (or, for batteries, the amount of stored electrical 

energy converted to shaft energy entering the transmission) 

D/L eff (driveline efficiency) – the % of the brake energy entering the transmission 

delivered through the driveline to the wheels 

Wheel eff (wheel efficiency) – the product of brake and driveline efficiency 

Cycle eff (cycle efficiency) – the % of energy delivered to the wheels used to 

overcome road loads and power the vehicle (it does not include energy lost as 

braking heat) 

Vehicle efficiency – the product of wheel and cycle efficiency 

y = 0.3135x

R2 = 0.9929

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1000 2000 3000 4000 5000 6000 7000 Energy Consumption (Wh/mi)

Inertia wt (lbs)

2008 Energy Consumption vs. Inertia Weight
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Road loads – the amount of resistant energy the vehicle must overcome during a 

city/highway test. Composed of vehicle weight (inertia), aerodynamic drag and 

rolling resistance 

Table 3-62: EV100 efficiency and energy demand calculations, 20% applied weight reduction 

Class Brake 

eff

D/L 

eff

Wheel 

eff

Cycle 

eff

Vehicle 

eff

Road 

loads

Energy 

reduction

Energy 

eff 

increase

IWbased, 

base ICE 

nominal 

mpgge

Base 

fuel 

energy 

req’d

Wh/mi

FTP 

fuel 

energy 

req’d

Wh/mi

Onroad 

fuel 

energy 

req’d

Wh/mi

Baseline 

gas ICE 24% 81% 20% 77% 15% 100%

Small 

car

85% 93% 79% 97% 77% 88% 83% 478% 37 912 158 225

Std car 85% 93% 79% 97% 77% 88% 83% 478% 30 1122 194 277

Large 

car

85% 93% 79% 97% 77% 88% 83% 478% 25 1332 230 329

Small 

MPV 85% 93% 79% 97% 77% 89% 83% 475% 29 1180 205 293

Large 

MPV 85% 93% 79% 97% 77% 89% 83% 475% 23 1497 260 372

Truck 85% 93% 79% 97% 77% 88% 83% 482% 20 1727 297 424

The energy efficiency of a baseline vehicle (around 15%), as indicated in the table 

above, was estimated using efficiency terms derived from EPA’s lumped parameter model 

(engine/battery brake efficiency, driveline efficiency, cycle efficiency and road load ratio to 

baseline). To calculate the energy consumption of an EV (or PHEV in charge-depleting 

mode), the following assumptions were made: 

“Brake” efficiency (for an EV, the efficiency of converting battery energy to 

tractive energy at the transmission input shaft) was estimated at 85% - assuming, 

roughly a 95% efficiency for the battery, motor, and power electronics, 

respectively. 

The driveline efficiency (including the transmission) was comparable to the value 

calculated by the lumped parameter model for an advanced 6-speed dual-clutch 

transmission at 93%. 

The cycle efficiency assumes regenerative braking where 97% recoverable braking 

energy is recaptured. As a result, most of the energy delivered to the wheels is 

used to overcome road loads. 

The road loads were based on the weight reduction of the vehicle. In the case of a 

100 mile EV with a 20% weight reduction, road loads (as calculated by the LP 

model) are reduced to 88-89% of the baseline vehiclevv.

 

vv Included in this example road load calculation is a 10% reduction in rolling resistance and aerodynamic drag.
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The energy consumption of the EV includes ratio of the roadloads of the EV to the 

baseline vehicle, and the ratio of the efficiency of the EV compared to the baseline 

vehicle. It is expressed mathematically as shown below in Equation 3-1: EV 

energy consumption: 

Equation 3-1: EV energy consumption 

In Table 3-63, the baseline energy required (in Wh/mi) is in the column labeled “Base 

fuel energy reqd”. The energy required for each vehicle class EV over the FTP is in the 

column “FTP fuel energy reqd Wh/mi” and incorporates the equation above. This energy rate 

refers to the laboratory or unadjusted test cycle value, as opposed to a real-world “onroad” 

value. EPA assumes a 30% fuel economy shortfall, based loosely on the 5-cycle Fuel 

Economy Labeling Rule (year) which is directionally correct for electrified vehicles. This 

corresponds to an increase in fuel consumption of 43%. Applying this 43% increase gives the 

onroad energy consumption values for EVs as shown in the far right column of the previous 

table. From this value, one can determine an appropriate battery pack size for the vehicle. 

The required battery energy for EVs equals the onroad energy consumption, 

multiplied by the desired range, divided by the useful state-of-charge window of the battery. 

It is calculated as follows in Equation 3-2

Equation 3-2: Required battery pack energy (size) for EVs 

Assumed usable SOC (battery state-of-charge) windows were 80% for EVs (10-90%) 

and 70% for PHEVs (15%-85%). The battery pack sizes are listed in orange in Table 3-63 for 

the 100-mile EV case and show both the onroad energy consumption (“EV adj Wh/mi” 

column) and the nominal battery energy content or “battery pack size”. 
 Technologies Considered in the Agencies’ Analysis

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Table 3-63: Battery pack sizes for EV100 based on inertia weight, 20% applied weight reduction 

Class Baseline curb wt

(lb)

Inertia wt

(lb)

EV unadj

(Wh/mi)

EV adj

(Wh/mi)

100 mile batt pack size 

(kWh)

2008 Baseline

Small car 2633 2933 158 225 28.2

Std car 3306 3606 194 277 34.7

Large car 3897 4197 230 329 41.1

Small MPV 3474 3774 205 293 36.7

Large MPV 4351 4651 260 372 46.5

Truck 5108 5408 297 424 53.0

2010 Baseline

Small car 2753 3053 164 234 29.2

Std car 3387 3687 200 286 35.7

Large car 4035 4335 241 344 43.0

Small MPV 3528 3828 209 298 37.3

Large MPV 4313 4613 257 367 45.8

Truck 5346 5646 307 439 54.8

EPA used the following formula to determine weight of an EV (Equation 3-3):

Equation 3-3: EV weight calculation 

Any weight reduction technology was applied only to the glider (baseline vehicle 

absent powertrain) as defined in Equation 3-4.

Equation 3-4: Weight reduction of the glider 

In the case of a PHEV, it was assumed that the base ICE powertrain remains so it is 

not deducted; the proper equation for PHEVs is shown in Equation 3-5.

Equation 3-5: Weight calculation for PHEV 

Listed in Table 3-64 are the assumed baseline ICE-powertrain weights, by vehicle 

class: 
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Table 3-64: Baseline ICE-powertrain weight assumptions, by class 

Class Engine Trans

(diff not 

included)

Fuel sys

(50% 

fill)

Engine mounts/

NVH 

treatments

Exhaust 12V 

batt

Total ICE powertrain 

weight

Small car 250 125 50 25 20 25 495

Std car 300 150 60 25 25 30 590

Large car 375 175 70 25 30 35 710

Small 

MPV

300 150 60 25 25 30 590

Large 

MPV

400 200 80 25 30 40 775

Truck 550 200 100 25 40 50 965

EPA then estimated the weight of the electric drive subsystem using the energy 

content of the battery pack as an input. EPA scaled the weight by applying a specific energy 

for the electric drive subsystem–including the battery pack, drive motor, wiring, power 

electronics, etc.–of 120 Wh/kg (or 18.33 lb/kWh). This specific energy value is based on 

adding components to an assumed battery pack specific energy of 150 Wh/kgww. Then, the 

gearbox (the only subsystem excluded from the electric drive scaling) was added to the 

weight of the electric drive subsystem; this total was included into the electric vehicle weight 

calculation as Welectric_drive

xx . A summary table of electric drive weights for 100-mile EVs is 

shown as Table 3-65. 

 

ww 150 Wh/kg is a conservative estimate for year 2017 and beyond: outputs from ANL’s battery cost model, 

which shows specific energy values of 160- 180 Wh/kg for a similar timeframe. 

xx Applies only to the EV. Because the baseline ICE powertrain weight (which includes gearbox weight) was not 

deducted from the PHEV, it is not added back in for the PHEV. 
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Table 3-65: Total electric drive weights for 100-mile EVs 

Class Batt pack size 

(kWh)

2020 electric content

(lbs)

Gearbox 

(power-split or other )

2020 EV 

powertrain total

2008 Baseline

Small car 28.2 517 50 567

Std car 34.7 635 60 695

Large car 41.1 754 70 824

Small MPV 36.7 672 60 732

Large MPV 46.5 853 80 933

Truck 53.0 972 100 1072

2010 Baseline

Small car 29.2 536 50 586

Std car 35.7 655 60 715

Large car 43.0 788 70 858

Small MPV 37.3 683 60 743

Large MPV 45.8 840 80 920

Truck 54.8 1005 100 1105

The difference between the actual weight and the predicted or nominal weight should 

be zero. However, if not then a revised weight reduction was used for another iteration of 

steps 2-6 until the two vehicle weights match. Spreadsheet tools such as “solver” in MS 

Excel were used for automating this iterative process. 

Table 3-66 shows example results for 100-mile range EVs; in this case a 20% applied 

glider weight reduction for a variety of vehicle classes. 

Table 3-66: Sample calculation sheet for 100-mile EVs for the 2008 Baseline 

Class

Base 

curb 

wt

(lb)

Base

power/wt 

ratio

Powertrain 

weight

(lb)

Base 

glider 

wt

(lb)

WR 

of 

glider

New EV 

wt

(nominal 

lb)

Energy 

cons 

adjusted 

(Wh/mi)

Batt 

pack 

size

(kWh)

Electric 

drive wt

(lb)

New 

EV 

weight

(lb)

Error

% WR 

from 

curb

% RL 

vs 

base

Small 

car

2633 0.0486 495 2138 428 2205 225 28.2 567 2277 0 13.5% 88%

Std 

car

3306 0.0575 590 2716 543 2763 277 34.7 695 2868 0 13.2% 88%

Large 

car

3897 0.0872 710 3187 637 3260 329 41.1 824 3374 0 13.4% 88%

Small 

MPV 3474 0.0463 590 2884 577 2897 293 36.7 732 3039 0 12.5% 89%

Large 

MPV 4351 0.0565 775 3576 715 3636 372 46.5 933 3794 0 12.8% 89%

Truck 5108 0.0617 965 4143 829 4279 424 53.0 1072 4387 0 14.1% 88%

Table 3-67 shows the effect on net electric vehicle weight reduction after 20% glider 

weight reduction was applied to EVs and PHEVs. As battery pack size increases for largerrange EVs and PHEVs, the overall realized vehicle weight reduction decreases (because it 

requires more energy to carry the extra battery weight). In this example, EVs with a 150 mile 

range require almost 20% weight reduction to the glider to make up for the additional weight 

of the electric drive and battery pack compared to a conventional ICE-based powertrain. 
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Table 3-67: Actual weight reduction percentages for EVs and PHEVs with 20% weight reduction applied 

to glider 

75 Mile EV

Actual %WR 

vs.

base vehicle

100 Mile EV

Actual %WR 

vs.

base vehicle

150 Mile EV

Actual %WR 

vs.

base vehicle

20 Mile PHEV

Actual %WR 

vs.

base vehicle

40 Mile PHEV

Actual %WR 

vs.

base vehicle

2008 Baseline

Small car 19% 14% 2% 12% 7%

Standard car 18% 13% 2% 12% 7%

Large car 19% 13% 2% 12% 7%

Small MPV 18% 13% 1% 12% 7%

Large MPV 18% 13% 1% 12% 7%

Truck 19% 14% 3% 11% 6%

2010 Baseline

Small car 18% 13% 1% 12% 7%

Standard car 18% 13% 1% 12% 7%

Large car 18% 13% 1% 12% 7%

Small MPV 18% 12% 1% 12% 8%

Large MPV 18% 13% 1% 12% 7%

Truck 19% 14% 3% 11% 6%

Because there is no “all-electric range” requirement for HEVs, battery pack sizes were 

relatively consistent for a given weight class. Furthermore, because battery pack sizes are at 

least an order of magnitude smaller for HEVs than for all-electric vehicles, the sensitivity of 

HEV vehicle weight (and hence energy consumption) to battery pack size is rather 

insignificant. For these reasons, a more direct approach (rather than an iterative process) 

works for battery sizing of HEVs. 

HEV batteries were scaled similar to the 2010 Fusion Hybrid based on nominal 

battery energy per lb ETW (equivalent test weight), at 0.37 Wh/lb. 

A higher usable SOC window of 40% (compared to 30% for Fusion Hybrid) 

reduced the required Li-Ion battery size to 75% of the Fusion Hybrid’s NiMH 

battery. This resulted in a 0.28 Wh/lb ETW ratio. 

In comparing anecdotal data for HEVs, the agencies assumed a slight weight 

increase of 4-5% for HEVs compared to baseline non-hybridized vehicles. The 

added weight of the Li-ion pack, motor and other electric hardware were offset 

partially by the reduced size of the base engine. 

3.4.3.9 HEV, PHEV and EV battery pack design and cost analysis using the ANL 

BatPaC model 

The U.S. Department of Energy (DOE) has established long term industry goals and 

targets for advanced battery systems as it does for many energy efficient technologies. 

Argonne National Laboratory (ANL) was funded by DOE to provide an independent 

assessment of Li-ion battery costs because of their expertise in the field as one of the primary 
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DOE National Laboratories responsible for basic and applied battery energy storage 

technologies for future HEV, PHEV and EV applications. A basic description of the ANL Liion battery cost model and initial modeling results for PHEV applications were published in a 

peer-reviewed technical paper presented at EVS-2461. ANL has extended modeling inputs 

and pack design criteria within the battery cost model to include analysis of manufacturing 

costs for EVs and HEVs as well has PHEVs.62 In early 2011, ANL issued a draft report 

detailing the methodology, inputs and outputs of their Battery Performance and Cost (BatPaC) 

model.63 A complete independent peer-review of the BatPaC model and its inputs and results 

for HEV, PHEV and EV applications has been completed64. ANL recently provided the 

agencies with an updated report documenting the BatPaC model that fully addresses the issues 

raised within the peer review.65 Based on the feedback from peer-reviewers, ANL updated the 

model in the following areas. 

1. Battery pack cost is adjusted upward. This adjustment is based on the feedback 

from several peer-reviewers, and changes are related to limiting electrode 

thickness to 100 microns, changing allocation of overhead cost to more closely 

represent a Tier 1 auto supplier, increasing cost of tabs, changing capital cost 

of material preparation, etc; 

2. Battery management system cost is increased to represent the complete 

monitoring and control needs for proper battery operation and safety as shown 

in Table 5.3 in the report; 

3. Battery automatic and manual disconnect unit cost is added based on safety 

considerations as shown in Table 5.3 in the report; 

4. Liquid thermal management system is added. ANL stated in the report that the 

liquid-cooled closure design it uses in the model would not have sufficient 

surface area and cell spacing to be cooled by air effectively as shown in Table 

5.3 in the report. 

 Subsequently, the agencies requested that an option be added to select between liquid 

or air thermal management and that adequate surface area and cell spacing be determined 

accordingly. Also, the agencies requested a feature to allow battery packs to be configured as 

subpacks in parallel or modules in parallel, as additional options for staying within voltage 

and cell size limits for large packs. 

 ANL added these features in a version of the model distributed March 1, 2012. This 

version of the model is used for the battery cost estimates in the final rule. This model and the 

peer review report are available in the public dockets for this rulemaking.64,66

NHTSA and EPA decided to use the ANL BatPaC model for estimating large-format 

lithium-ion batteries for this final rule, consistent with the proposal, for the following reasons. 

First, the ANL model has been described and presented in the public domain and does not rely 

upon confidential business information (which would therefore not be reviewable by the 

public). The model was developed by scientists at ANL who have significant experience in 
 Technologies Considered in the Agencies’ Analysis

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this area. The model uses a bill of materials methodology which the agencies believe is the 

preferred method for developing cost estimates. The ANL model appropriately considers the 

vehicle applications power and energy requirements, which are two of the fundamental 

parameters when designing a lithium-ion battery for an HEV, PHEV, or EV. The ANL model 

can estimate high volume production costs, which the agencies believe is appropriate for the 

2025 time frame. Finally, the ANL model’s cost estimates, while generally lower than the 

estimates we received from the OEMs, is consistent with some of the supplier cost estimates 

the agencies received from large-format lithium-ion battery pack manufacturers. A portion of 

those data was received from on-site visits to vehicle manufacturers and battery suppliers 

done by the EPA in 2008. 

The ANL battery cost model is based on a bill of materials approach in addition to 

specific design criteria for the intended application of a battery pack. The costs include 

materials, manufacturing processes, the cost of capital equipment, plant area, and labor for 

each manufacturing step as well as the design criteria include a vehicle application’s power 

and energy storage capacity requirements, the battery’s cathode and anode chemistry, and the 

number of cells per module and modules per battery pack. The model assumes use of a 

laminated multi-layer prismatic cell and battery modules consisting of double-seamed rigid 

containers. The model also assumes that the battery modules are liquid-cooled. The model 

takes into consideration the cost of capital equipment, plant area and labor for each step in the 

manufacturing process for battery packs and places relevant limits on electrode coating 

thicknesses and other processes limited by existing and near-term manufacturing processes. 

The ANL model also takes into consideration annual pack production volume and economies 

of scale for high-volume production. 

Basic user inputs to BatPaC include performance goals (power and energy capacity), 

choice of battery chemistry (of five predefined chemistries), the vehicle type for which the 

battery is intended (HEV, PHEV, or EV), the desired number of cells and modules, and the 

volume of production. BatPaC then designs the cells, modules, and battery pack, and 

provides an itemized cost breakdown at the specified production volume. 

BatPaC provides default values for engineering properties and material costs that 

allow the model to operate without requiring the user to supply detailed technical or 

experimental data. In general, the default properties and costs represent what the model 

authors consider to be reasonable values representing the state of the art expected to be 

available to large battery manufacturers in the year 2020. Users are encouraged to change 

these defaults as necessary to represent their own expectations or their own proprietary data. 

In using BatPaC, it is extremely important that the user monitor certain properties of 

the cells, modules, and packs that it generates, to ensure that they stay within practical design 

guidelines, adjusting related inputs if necessary. In particular, pack voltage and individual 

cell capacity should be limited to appropriate ranges for the application. These design 

guidelines are not rigidly defined but approximate ranges are beginning to emerge in the 

industry. 

Also inherent in BatPaC are certain modeling assumptions that are still open to some 

uncertainty or debate in the industry. For some, such as the available portion of total battery 
 Technologies Considered in the Agencies’ Analysis

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energy (aka "SOC window") for a PHEV/EV/HEV, the user can easily modify a single 

parameter to represent a value other than the default. For others, such as specific unit costs 

for thermal management or battery monitoring components, changes can often be made by 

replacing the relevant components of the model outputs. 

The cost outputs used by the agencies to determine 2025 HEV, PHEV and EV battery 

costs were based on the following inputs and assumptions. 

EPA selected basic user inputs as follows. For performance goals, EPA used the 

power and energy requirements derived from the scaling analysis described in the previous 

section. Specifically, these covered each of the six classes of vehicles (Small Car, Standard 

Car, Large Car, Small MPV, Large MPV and Truck) under each of the five weight reduction 

scenarios (0%, 2%, 7.5%, 10%, and 20%). The chosen battery chemistries were NMC441-G 

(for EVs and PHEV40) and LMO-G (for P2 HEVs and PHEV20). Vehicle types were EV75, 

EV100, EV150 (using the BatPaC “EV” setting); PHEV20 and PHEV40 (using the "PHEV" 

setting), and P2 HEV (using the “HEV-HP” setting). All modules were composed of 32 cells, 

with each pack having a varying number of modules. Cost outputs were generated for annual 

production volumes of 50K, 125K, 250K, and 450K packs. The cost outputs for the 450K 

production volume are used in the FRM analysis, consistent with the proposal, as being 

applicable in MY 2017 (HEVs) and MY 2025 (EVs and PHEVs). 

For engineering properties and material costs, and for other parameters not identified 

below, EPA used the defaults provided in the model. 

For design guidelines regarding pack voltage and cell capacity, EPA chose guidelines 

based on knowledge of current practices and developing trends of battery manufacturers and 

OEMs, supplemented by discussions with the BatPaC authors. Specifically: (1) allowable 

pack voltage was targeted to approximately 120V for HEVs and approximately 350-400V for 

EVs and PHEVs (with some EV150 packs for larger vehicles allowed to about 460-600V); (2) 

allowable cell capacity was limited to less than approximately 80 A-hr. 

EPA made several modeling assumptions that differed from the default model: (1) The 

SOC window for HEVs was increased to 40% rather than the default 25%. (2) HEV packs 

were modeled as air cooled instead of liquid cooled (except for Truck and MPV with Towing, 

which are modeled as liquid-cooled). EPA replaced the model's projected costs for air cooling 

components (blower motor, ducting, and temperature feedback) with costs derived from 

FEV’s teardown studies, which may be more representative of volume production than the 

default values provided in the model. 

Additionally, EPA did not include warranty costs computed by BatPaC in the total 

battery cost because these are accounted for elsewhere by means of indirect cost multipliers 

(ICMs). 

Table 3-68 Summary of Inputs and Assumptions Used with BatPaC 

Category of BatPaC Default or Suggested Agency Inputs for FRM Analysis
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input/Assumptions Values 

Annual production volume n/a 450,000

Battery chemistry n/a for HEV, PHEV20: LMO-G

for PHEV40, EV: NMC441-G

Allowable pack voltage for HEV: 160-260 V

for PHEV, EV: 290-360 V

for HEV: ~ 120 V

for PHEV, EV: ~ 360-600 V

Allowable cell capacity < 60 A-hr < 80 A-hr

Cells per module 16-32 32

SOC window for HEVs 25% 40%

Thermal management Liquid Air, for small/medium HEVs

Liquid for all others

The cost projections produced by BatPaC are sensitive to the inputs and assumptions 

the user provides. Significant uncertainty remains regarding which will best represent 

manufacturer practice in the year 2020. The battery pack cost projection from BatPaC model 

ranges from $161/kWh for EV150 truck to $296/kWh for PHEV40 large car with NMC as 

chemistry and to $373/kWh for PHEV20 small car as shown in Table 3-69 to Table 3-74. The 

agencies note that costs used in the analysis are lower than the costs generally reported in 

stakeholder meetings, which ranged from $300/kW-hour to $400/kW-hour range for 2020 and 

$250 to $300/kW-hour range for 2025. A comparison of BatPaC modeling results to the costs 

used in the 2012-2016 final rule and to cost estimates compiled by EPA from battery suppliers 

and auto OEMs is shown in Figure 3-24. 

The agencies also reviewed publically available PHEV and EV battery cost literature 

including reports from Anderman67, Frost & Sullivan68, TIAX69, Boston Consulting Group70, 

and NRC71. Due to the uncertainties inherent in estimating battery costs through the MY 2025 

model year, a sensitivity analysis will be provided in each agency’s RIA using a range of 

costs estimated by DOE technical experts to represent a reasonable outer bounds to the results 

from the BatPaC model. In a recent report to NHTSA and EPA, DOE and ANL suggested the 

following range for the sensitivity study with 95% confidence interval after analyzing the 

confidence bound using the BatPaC model. The agencies describe their respective sensitivities 

surrounding BatPaC costs in their respective RIAs (see Chapter 3.11 of EPA’s final RIA and 

Chapter X of NHTSA’s FRIA). 

Suggested confidence bounds as percentage of the calculated 

point estimate for a graphite based Li-ion battery using the 

default inputs in BatPaC

Confidence Interval

Battery type Cathodes lower upper

HEV LMO, LFP, NCA, 

NMC -10% 10%

PHEV, EV NMC, NCA -10% 20%

PHEV, EV LMO, LFP -20% 35%
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Figure 3-23 Table from ANL Recommendation72

Figure 3-24 Comparison of direct manufacturing costs per unit of energy storage ($/kW-hr) between the 

estimates used by EPA in the 2012-2016 GHG final rule, the BatPaC model results for PHEV20, PHEV40, 

EV75, EV100 and EV150 packages compared to estimates from OEM battery suppliers (2009 dollars, 

markups not included). Multiple points shown for the BatPaC model results for PHEV 20, PHEV40, 

EV75, EV100 and EV150 reflect the range of energy-specific costs for EPA’s subcompact through largecar package categories (see Table 3-70 through Table 3-74for details). A range of OEM estimated battery 

costs from stakeholder meetings is also shown for comparison (red bars) which may or may not reflect 

additional cost markups. 

While it is expected that other Li-ion battery chemistries with higher energy density, 

higher power density and lower cost will likely be available in the 2017-2025 timeframe, the 

specific chemistries used for the cost analysis were chosen due to their known characteristics 

and to be consistent with both public available information on current and near term HEV, 

PHEV and EV product offerings from Hyundai, GM and Nissan as well as confidential 

business information on future products currently under development.73,74,75,76

 The cost 

outputs from the BatPaC model used by the agencies in this analysis are shown in Table 3-69

through Table 3-74 for different levels of applied weight reduction technology. We 

differentiate between “applied” weight reduction and “net” weight reduction in this analysis 

because to achieve the same amount of mass reduction, more mass reduction technologies 

might need to be applied to vehicles with electrification than with traditional powertrains 

because of the added weight of the electrification systems (i.e., the battery, electric motors, 

etc.). This also makes it clear that we have estimated vehicle level battery pack costs—and 

motor and other electrified vehicle specific costs—based on the net weight reduction of the 

$-

$200.00 

$400.00 

$600.00 

$800.00 

$1,000.00 

$1,200.00 

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Calendar Year

Estimated Battery Pack Costs

$U.S./kW-hr (pack basis)

2012-2016 EPA Cost Estimate

BatPac Model (PHEV20) BatPac Model (PHEV40)

BatPac Model (EV75) BatPac Model (EV150) BatPac Model (EV100)

Total Range of OEM Supplier Estimates (>5sources) collected by EPA from 2008 -2010

Range from the majority of OEM stakeholder meetings in June-August 2010
 Technologies Considered in the Agencies’ Analysis

3-157 

vehicle. For example, a typical EV150 battery back and associated motors and other EVspecific equipment increases vehicle weight roughly 18 percent. As a result, an EV150 that 

applied 20 percent mass reduction technology (see section 3.4.5.5 for a full discussion of mass 

reduction technologies and costs) would have a net weight reduction of only 2 percent. In 

such a case, the agencies would estimate mass reduction costs associated with a 20 percent 

applied mass reduction, and EV150 costs associated with only a 2 percent net mass reduction 

(lower net mass reduction results in higher battery pack and motor costs). Similarly, HEV 

battery packs increase vehicle weight by roughly 5 or 6 percent. Therefore, for an HEV with 

20 percent applied mass reduction technology—and costs associated with 20 percent applied 

mass reduction—would have HEV costs associated with a 15 percent net mass reduction. 

Furthermore, such an HEV would have an effectiveness level improvement associated with a 

15 percent net mass reduction rather than a 20 percent net reduction. 

Table 3-69 Direct Manufacturing Costs for P2 HEV battery packs at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

P2 HEV (LMO)

0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 450K/yr 

volume Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh

2008 Baseline

Small Car $726 $896 $722 $909 $712 $950 $708 $970 $700 $1,008

Standard Car $801 $804 $796 $815 $783 $849 $777 $866 $765 $901

Large Car $938 $809 $929 $817 $909 $848 $900 $862 $882 $894

Small MPV $779 $747 $775 $758 $762 $790 $757 $806 $746 $839

Large MPV $876 $682 $870 $691 $853 $718 $846 $731 $830 $760

Truck $1,010 $676 $1,003 $685 $983 $711 $974 $724 $957 $747

2010 Baseline

Small Car $732 $904 $729 $918 $718 $958 $714 $978 $705 $1,017

Standard Car $809 $813 $805 $824 $791 $858 $785 $875 $773 $909

Large Car $950 $819 $943 $830 $920 $858 $911 $873 $893 $904

Small MPV $788 $756 $784 $767 $771 $800 $765 $816 $754 $848

Large MPV $878 $683 $872 $692 $855 $720 $847 $733 $832 $762

Truck $1,019 $682 $1,012 $691 $992 $718 $983 $731 $967 $754

Table 3-70 Direct Manufacturing Costs for PHEV20 battery packs at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

PHEV20 

(LMO)

0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 450K/yr 

volume Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh

2008 Baseline

Small Car $2,531 $364 $2,517 $364 $2,469 $370 $2,447 $371 $2,431 $373

Standard Car $2,962 $347 $2,938 $348 $2,835 $345 $2,808 $346 $2,784 $347
 Technologies Considered in the Agencies’ Analysis

3-158 

Large Car $3,734 $368 $3,696 $369 $3,592 $369 $3,546 $368 $3,510 $369

Small MPV $2,835 $316 $2,813 $317 $2,754 $319 $2,730 $320 $2,703 $323

Large MPV $3,424 $300 $3,393 $301 $3,309 $302 $3,274 $303 $3,244 $303

Truck $3,874 $295 $3,834 $295 $3,732 $295 $3,681 $297 $3,671 $296

2010 Baseline

Small Car $2,572 $370 $2,554 $370 $2,507 $376 $2,487 $377 $2,468 $379

Standard Car $3,019 $353 $2,992 $354 $2,927 $357 $2,858 $352 $2,829 $353

Large Car $3,813 $376 $3,773 $376 $3,668 $376 $3,621 $376 $3,575 $376

Small MPV $2,933 $326 $2,911 $328 $2,811 $326 $2,783 $326 $2,754 $329

Large MPV $3,434 $301 $3,403 $302 $3,319 $303 $3,282 $303 $3,253 $304

Truck $3,922 $298 $3,881 $298 $3,778 $299 $3,732 $301 $3,706 $298

Table 3-71 Direct Manufacturing Costs for PHEV40 battery pack at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

PHEV40 

(NMC)

0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 450K/yr 

volume Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh

2008 Baseline

Small Car $3,644 $262 $3,619 $262 $3,542 $264 $3,542 $264 $3,542 $264

Standard Car $4,390 $257 $4,343 $257 $4,228 $258 $4,228 $258 $4,228 $258

Large Car $6,006 $296 $5,921 $295 $5,671 $291 $5,671 $291 $5,671 $291

Small MPV $4,247 $236 $4,207 $237 $4,101 $238 $4,100 $237 $4,100 $237

Large MPV $5,269 $231 $5,212 $231 $5,065 $231 $5,065 $231 $5,065 $231

Truck $6,122 $233 $6,050 $233 $5,900 $232 $5,900 $232 $5,900 $232

2010 Baseline

Small Car $3,722 $268 $3,690 $267 $3,606 $269 $3,606 $269 $3,606 $269

Standard Car $4,494 $263 $4,447 $263 $4,324 $263 $4,324 $263 $4,324 $263

Large Car $6,158 $304 $6,073 $303 $5,850 $300 $5,850 $300 $5,850 $300

Small MPV $4,351 $242 $4,309 $243 $4,198 $243 $4,198 $243 $4,198 $243

Large MPV $5,286 $232 $5,228 $232 $5,080 $232 $5,080 $232 $5,080 $232

Truck $6,215 $236 $6,142 $236 $5,980 $235 $5,980 $235 $5,980 $235

Table 3-72 Direct Manufacturing Costs for EV75 battery packs at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

EV75 (NMC)

0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 450K/yr 

volume Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh
 Technologies Considered in the Agencies’ Analysis

3-159 

2008 Baseline

Small Car $5,115 $224 $5,098 $225 $4,996 $228 $4,962 $229 $4,768 $233

Standard Car $6,021 $215 $5,965 $215 $5,818 $216 $5,755 $216 $5,509 $219

Large Car $7,724 $232 $7,635 $232 $7,397 $231 $7,295 $231 $6,907 $231

Small MPV $5,995 $203 $5,952 $204 $5,843 $206 $5,800 $207 $5,625 $211

Large MPV $7,310 $195 $7,237 $196 $7,045 $196 $6,963 $196 $6,610 $197

Truck $8,332 $193 $8,242 $193 $8,005 $193 $7,883 $194 $7,474 $194

2010 Baseline

Small Car $5,232 $221 $5,195 $222 $5,106 $225 $5,071 $226 $4,912 $231

Standard Car $6,152 $214 $6,092 $214 $5,940 $215 $5,874 $215 $5,624 $218

Large Car $7,923 $229 $7,832 $229 $7,586 $229 $7,479 $228 $7,092 $228

Small MPV $6,070 $203 $6,016 $203 $5,904 $205 $5,860 $206 $5,684 $210

Large MPV $7,312 $197 $7,238 $198 $7,046 $198 $6,962 $198 $6,605 $198

Truck $8,472 $191 $8,380 $191 $8,141 $191 $8,036 $191 $7,629 $191

Table 3-73 Direct Manufacturing Costs for EV100 battery packs at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

EV100 (NMC) 0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 450K/yr 

volume

Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh

2008 Baseline

Small Car $6,105 $201 $6,083 $201 $5,950 $204 $5,906 $205 $5,817 $206

Standard Car $7,054 $189 $7,001 $189 $6,826 $190 $6,770 $191 $6,662 $192

Large Car $8,630 $195 $8,535 $195 $8,283 $194 $8,175 $194 $7,999 $194

Small MPV $7,293 $186 $7,237 $186 $7,096 $188 $7,039 $189 $6,953 $190

Large MPV $8,641 $173 $8,571 $174 $8,392 $175 $8,321 $176 $8,215 $177

Truck $9,962 $173 $9,879 $174 $9,676 $175 $9,554 $176 $9,392 $177

2010 Baseline

Small Car $6,255 $198 $6,209 $199 $6,094 $201 $6,048 $202 $5,956 $204

Standard Car $7,173 $187 $7,118 $188 $6,980 $190 $6,884 $189 $6,802 $190

Large Car $8,863 $192 $8,765 $192 $8,504 $192 $8,393 $192 $8,251 $192

Small MPV $7,375 $185 $7,318 $185 $7,174 $187 $7,117 $188 $7,031 $189

Large MPV $8,586 $174 $8,516 $174 $8,338 $176 $8,268 $176 $8,128 $177

Truck $10,158 $172 $10,075 $172 $9,865 $174 $9,782 $174 $9,615 $175
 Technologies Considered in the Agencies’ Analysis

3-160 

Table 3-74 Direct Manufacturing Costs for EV150 battery packs at different levels of applied vehicle 

weight reduction (2010 dollars, markups not included) 

EV150 

(NMC)

0% weight 

reduction

2% weight 

reduction

7.5% weight 

reduction

10% weight 

reduction

20% weight 

reduction

@ 

450K/yr 

volume

Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh Pack $/kWh

2008 Baseline

Small 

Car $8,080 $177 $8,048 $178 $8,048 $178 $8,048 $178 $8,048 $178

Standard 

Car $9,753 $174 $9,714 $174 $9,714 $174 $9,714 $174 $9,714 $174

Large 

Car $11,120 $167 $11,073 $167 $11,073 $167 $11,073 $167 $11,073 $167

Small 

MPV $10,109 $171 $10,109 $171 $10,109 $171 $10,109 $171 $10,109 $171

Large 

MPV $12,114 $162 $12,112 $162 $12,112 $162 $12,112 $162 $12,112 $162

Truck $13,878 $161 $13,818 $161 $13,759 $161 $13,759 $161 $13,759 $161

2010 Baseline

Small 

Car $8,298 $175 $8,265 $176 $8,265 $176 $8,265 $176 $8,265 $176

Standard 

Car $9,928 $173 $9,888 $173 $9,888 $173 $9,888 $173 $9,888 $173

Large 

Car $11,432 $166 $11,384 $166 $11,384 $166 $11,384 $166 $11,384 $166

Small 

MPV $10,228 $171 $10,228 $171 $10,228 $171 $10,228 $171 $10,228 $171

Large 

MPV $12,032 $162 $11,981 $163 $11,981 $163 $11,981 $163 $11,981 $163

Truck $14,166 $160 $14,045 $160 $14,044 $160 $14,044 $160 $14,044 $160

Specifically for modeling purposes, both agencies wanted HEV/PHEV/EV battery 

pack costs based on net weight reduction rather than applied weight reduction as shown in 

Table 3-69 through Table 3-74 above. The agencies did this by first determining the average 

weight differences (applied weight reduction vs net weight reduction) for each of the 6 major 

vehicle classes (small car, standard car, large car, small MPV, large MPV & truck) and each 

of the electrification types (P2 HEV, PHEV & EV). Due to the weight increases of adding the 

electrification system and battery pack and the weight decreases by applying smaller or no 

conventional internal combustion engine, the net mass reduction for HEV, PHEV and EV 

varies for different electrification packages and vehicle classes. For example, for a 20-mile 

small car PHEV, a 5% mass reduction of the glider is offset by the additional weight of the 

electrification system. Said another way, a 5% mass reduction needs to be applied to the 

glider to achieve a net 0% overall vehicle mass reduction for a PHEV20 small car. Those 

weight reduction differences are shown in Table 3-75.

Table 3-75 EPA and NHTSA Weight Reduction Offset Associated with Electrification Technologies 

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

2008 Baseline

Small car 5% 7% 13% 0% 6% 18%

Standard car 5% 7% 12% 0% 6% 18%

Large car 5% 8% 14% -1% 5% 17%

Small MPV 5% 7% 12% 1% 7% 19%

Large MPV 5% 7% 12% 0% 6% 18%
 Technologies Considered in the Agencies’ Analysis

3-161 

Truck 4% 7% 12% 1% 7% 19%

2010 Baseline

Small car 5% 7% 12% 0% 6% 19%

Standard car 5% 7% 12% 1% 7% 19%

Large car 5% 8% 13% 0% 6% 17%

Small MPV 5% 7% 12% 1% 7% 19%

Large MPV 5% 7% 12% 1% 7% 19%

Truck 4% 7% 12% 0% 6% 19%

Notes:

For example, PHEV40-specific technologies add 12-14% to vehicle weight so that a 20% 

applied weight reduction would result in a 6-8% net weight reduction.

While an EV75 can actually reduce vehicle weight by 1-2% (i.e., battery packs and motors 

weigh less than the removed internal combustion engine and transmission), the agencies used a 

value of 0% where negative entries are shown.

The agencies then generated linear regressions of battery pack costs against percentage 

net weight reduction using the costs shown in Table 3-69 through Table 3-74 and the weight 

reduction offsets shown in Table 3-75. These results are shown in Table 3-76.

Table 3-76 EPA and NHTSA Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net 

Weight Reduction (2010$) 

Vehicle 

Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

2008 Baseline

Small car -$181x+$726 -$861x+$2,533 -$1,517x+$3,646 -$1,859x+$5,131 -$2,168x+$6,115 -$2,045x+$8,080

Standard

car

-$240x+$801 -$1,543x+$2,962 -$2,195x+$4,389 -$2,754x+$6,023 -$2,958x+$7,056 -$2,552x+$9,753

Large car -$369x+$937 -$1,881x+$3,734 -$4,700x+$6,010 -$4,356x+$7,725 -$4,647x+$8,630 -$2,840x+$11,120

Small 

MPV -$224x+$779 -$1,073x+$2,835 -$1,957x+$4,247 -$2,061x+$5,997 -$2,649x+$7,293 -$19x+$10,109

Large 

MPV -$303x+$876

Truck -$367x+$1,010

2010 Baseline

Small car -$188x+$733 -$866x+$2,572 -$1,612x+$3,722 -$1,717x+$5,233 -$2,209x+$6,256 -$2,700x+$8,298

Standard 

car

-$248x+$810 -$1,573x+$3,024 -$2,291x+$4,494 -$2,887x+$6,154 -$2,883x+$7,178 -$3,242x+$9,928

Large car -$387x+$950 -$1,957x+$3,813 -$4,217x+$6,158 -$4,543x+$7,925 -$4,744x+$8,862 -$4,250x+$11,432

Small 

MPV -$233x+$789 -$1,516x+$2,934 -$2,022x+$4,350 -$2,155x+$6,067 -$2,706x+$7,375 -$21x+$10,228

Large 

MPV -$305x+$878

Truck -$364x+$1,019

Notes:

“x” in the equations represents the net weight reduction as a percentage, so a small car P2 HEV battery pack (2008 baseline) with a 20% 

applied weight reduction and, therefore, a 15% net weight reduction would cost (-$181)x(15%)+$726=$698.

The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle classes 

would use the technologies.

For P2 HEV battery packs, the direct manufacturing costs shown in Table 3-76 are 

considered applicable to the 2017MY. The agencies consider the P2 battery packs technology 

to be on the flat portion of the learning curve during the 2017-2025 timeframe. The agencies 

have applied a high1 complexity ICM of 1.56 through 2024 then 1.35 thereafter. For PHEV 
 Technologies Considered in the Agencies’ Analysis

3-162 

and EV battery packs, the direct manufacturing costs shown in Table 3-76 are considered 

applicable to the 2025MY. For the PHEV and EV battery packs, the agencies have applied 

the learning curve discussed in Section 3.2.3. The agencies have applied a high2 complexity 

ICM of 1.77 through 2024 then 1.50 thereafter. The resultant costs for P2 HEV, PHEV20, 

PHEV40, EV75, EV100 and EV150 battery packs for the 2008 and 2010 baselines are shown 

in Table 3-77 through Table 3-87, respectively. 

Table 3-77 Costs for P2 HEV Battery Packs for the 2008 Baseline (2010$) 

Cost 

type Vehicle class Applied WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 5% $717 $695 $674 $654 $634 $615 $597 $579 $562

DMC Small car 15% 10% $707 $686 $666 $646 $626 $608 $589 $572 $554

DMC Small car 20% 15% $698 $677 $657 $637 $618 $600 $582 $564 $547

DMC Standard car 10% 5% $789 $765 $742 $720 $698 $677 $657 $637 $618

DMC Standard car 15% 10% $777 $753 $731 $709 $688 $667 $647 $628 $609

DMC Standard car 20% 15% $765 $742 $719 $698 $677 $657 $637 $618 $599

DMC Large car 10% 5% $919 $891 $864 $838 $813 $789 $765 $742 $720

DMC Large car 15% 10% $900 $873 $847 $821 $797 $773 $750 $727 $705

DMC Large car 20% 15% $882 $855 $830 $805 $781 $757 $734 $712 $691

DMC Small MPV 10% 5% $768 $745 $722 $701 $680 $659 $640 $620 $602

DMC Small MPV 15% 10% $757 $734 $712 $690 $670 $650 $630 $611 $593

DMC Small MPV 20% 15% $745 $723 $701 $680 $660 $640 $621 $602 $584

DMC Large MPV 10% 5% $861 $835 $810 $786 $762 $739 $717 $695 $675

DMC Large MPV 15% 10% $846 $820 $796 $772 $749 $726 $704 $683 $663

DMC Large MPV 20% 15% $830 $805 $781 $758 $735 $713 $692 $671 $651

DMC Truck 10% 6% $988 $958 $930 $902 $875 $848 $823 $798 $774

DMC Truck 15% 11% $970 $941 $912 $885 $858 $833 $808 $783 $760

DMC Truck 20% 16% $951 $923 $895 $868 $842 $817 $792 $769 $746

IC Small car 10% 5% $404 $402 $401 $400 $399 $397 $396 $395 $243

IC Small car 15% 10% $399 $397 $396 $395 $393 $392 $391 $390 $239

IC Small car 20% 15% $394 $392 $391 $390 $388 $387 $386 $385 $236

IC Standard car 10% 5% $444 $443 $441 $440 $439 $437 $436 $435 $267

IC Standard car 15% 10% $438 $436 $435 $433 $432 $431 $429 $428 $263

IC Standard car 20% 15% $431 $429 $428 $427 $425 $424 $423 $421 $259

IC Large car 10% 5% $518 $516 $514 $512 $511 $509 $508 $506 $311

IC Large car 15% 10% $507 $506 $504 $502 $501 $499 $498 $496 $305

IC Large car 20% 15% $497 $495 $494 $492 $490 $489 $487 $486 $298

IC Small MPV 10% 5% $433 $431 $430 $428 $427 $426 $424 $423 $260

IC Small MPV 15% 10% $426 $425 $424 $422 $421 $419 $418 $417 $256

IC Small MPV 20% 15% $420 $419 $417 $416 $415 $413 $412 $411 $252

IC Large MPV 10% 5% $485 $483 $482 $480 $479 $477 $476 $474 $291

IC Large MPV 15% 10% $477 $475 $473 $472 $470 $469 $467 $466 $286

IC Large MPV 20% 15% $468 $466 $465 $463 $462 $460 $459 $458 $281

IC Truck 10% 6% $557 $555 $553 $551 $549 $548 $546 $545 $334

IC Truck 15% 11% $546 $545 $543 $541 $539 $538 $536 $534 $328

IC Truck 20% 16% $536 $534 $533 $531 $529 $527 $526 $524 $322

TC Small car 10% 5% $1,120 $1,097 $1,075 $1,054 $1,033 $1,013 $993 $974 $804

TC Small car 15% 10% $1,106 $1,084 $1,062 $1,040 $1,020 $1,000 $980 $962 $794

TC Small car 20% 15% $1,092 $1,070 $1,048 $1,027 $1,007 $987 $968 $949 $784

TC Standard car 10% 5% $1,233 $1,208 $1,183 $1,160 $1,137 $1,114 $1,093 $1,072 $885

TC Standard car 15% 10% $1,214 $1,190 $1,165 $1,142 $1,119 $1,098 $1,076 $1,056 $872

TC Standard car 20% 15% $1,196 $1,171 $1,147 $1,125 $1,102 $1,081 $1,060 $1,039 $858

TC Large car 10% 5% $1,436 $1,407 $1,378 $1,351 $1,324 $1,298 $1,273 $1,248 $1,031

TC Large car 15% 10% $1,407 $1,379 $1,351 $1,324 $1,297 $1,272 $1,247 $1,223 $1,010

TC Large car 20% 15% $1,379 $1,350 $1,323 $1,297 $1,271 $1,246 $1,222 $1,198 $989

TC Small MPV 10% 5% $1,201 $1,176 $1,152 $1,129 $1,107 $1,085 $1,064 $1,044 $862

TC Small MPV 15% 10% $1,183 $1,159 $1,135 $1,113 $1,091 $1,069 $1,048 $1,028 $849
 Technologies Considered in the Agencies’ Analysis

3-163 

TC Small MPV 20% 15% $1,165 $1,142 $1,119 $1,096 $1,074 $1,053 $1,033 $1,013 $836

TC Large MPV 10% 5% $1,346 $1,318 $1,292 $1,266 $1,241 $1,216 $1,193 $1,170 $966

TC Large MPV 15% 10% $1,322 $1,295 $1,269 $1,243 $1,219 $1,195 $1,172 $1,149 $949

TC Large MPV 20% 15% $1,298 $1,272 $1,246 $1,221 $1,197 $1,174 $1,151 $1,129 $932

TC Truck 10% 6% $1,545 $1,513 $1,483 $1,453 $1,424 $1,396 $1,369 $1,343 $1,109

TC Truck 15% 11% $1,516 $1,485 $1,455 $1,426 $1,398 $1,370 $1,344 $1,318 $1,088

TC Truck 20% 16% $1,487 $1,457 $1,428 $1,399 $1,371 $1,344 $1,318 $1,293 $1,068

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-78 Costs for P2 HEV Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle class Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 5% $723 $701 $680 $660 $640 $621 $602 $584 $567

DMC Small car 15% 10% $714 $692 $672 $651 $632 $613 $595 $577 $559

DMC Small car 20% 15% $704 $683 $663 $643 $624 $605 $587 $569 $552

DMC Standard car 10% 5% $797 $773 $750 $727 $706 $685 $664 $644 $625

DMC Standard car 15% 10% $785 $761 $738 $716 $695 $674 $654 $634 $615

DMC Standard car 20% 15% $772 $749 $727 $705 $684 $663 $643 $624 $605

DMC Large car 10% 5% $931 $903 $876 $849 $824 $799 $775 $752 $729

DMC Large car 15% 10% $911 $884 $857 $832 $807 $782 $759 $736 $714

DMC Large car 20% 15% $892 $865 $839 $814 $790 $766 $743 $721 $699

DMC Small MPV 10% 5% $777 $754 $731 $709 $688 $667 $647 $628 $609

DMC Small MPV 15% 10% $765 $742 $720 $698 $677 $657 $637 $618 $600

DMC Small MPV 20% 15% $754 $731 $709 $688 $667 $647 $628 $609 $591

DMC Large MPV 10% 5% $863 $837 $812 $787 $764 $741 $719 $697 $676

DMC Large MPV 15% 10% $847 $822 $797 $773 $750 $728 $706 $685 $664

DMC Large MPV 20% 15% $832 $807 $783 $759 $737 $715 $693 $672 $652

DMC Truck 10% 6% $997 $967 $938 $910 $883 $856 $831 $806 $781

DMC Truck 15% 11% $979 $949 $921 $893 $867 $841 $815 $791 $767

DMC Truck 20% 16% $961 $932 $904 $877 $850 $825 $800 $776 $753

IC Small car 10% 5% $408 $406 $405 $403 $402 $401 $400 $399 $245

IC Small car 15% 10% $402 $401 $400 $398 $397 $396 $395 $393 $242

IC Small car 20% 15% $397 $396 $394 $393 $392 $391 $389 $388 $238

IC Standard car 10% 5% $449 $448 $446 $445 $443 $442 $441 $439 $270

IC Standard car 15% 10% $442 $441 $439 $438 $436 $435 $434 $433 $266

IC Standard car 20% 15% $435 $434 $432 $431 $430 $428 $427 $426 $261

IC Large car 10% 5% $524 $523 $521 $519 $518 $516 $514 $513 $315

IC Large car 15% 10% $514 $512 $510 $508 $507 $505 $504 $502 $308

IC Large car 20% 15% $503 $501 $499 $498 $496 $494 $493 $492 $302

IC Small MPV 10% 5% $438 $436 $435 $433 $432 $431 $429 $428 $263

IC Small MPV 15% 10% $431 $430 $428 $427 $426 $424 $423 $422 $259

IC Small MPV 20% 15% $425 $423 $422 $420 $419 $418 $417 $415 $255

IC Large MPV 10% 5% $486 $485 $483 $481 $480 $478 $477 $475 $292

IC Large MPV 15% 10% $478 $476 $474 $473 $471 $470 $468 $467 $287

IC Large MPV 20% 15% $469 $467 $466 $464 $463 $461 $460 $459 $282

IC Truck 10% 6% $562 $560 $558 $556 $555 $553 $551 $550 $338

IC Truck 15% 11% $552 $550 $548 $546 $544 $543 $541 $540 $331

IC Truck 20% 16% $541 $540 $538 $536 $534 $533 $531 $529 $325

TC Small car 10% 5% $1,131 $1,108 $1,085 $1,063 $1,042 $1,022 $1,002 $983 $812

TC Small car 15% 10% $1,116 $1,093 $1,071 $1,050 $1,029 $1,009 $989 $970 $801

TC Small car 20% 15% $1,101 $1,079 $1,057 $1,036 $1,015 $995 $976 $957 $790

TC Standard car 10% 5% $1,246 $1,221 $1,196 $1,172 $1,149 $1,126 $1,105 $1,083 $895

TC Standard car 15% 10% $1,227 $1,202 $1,178 $1,154 $1,131 $1,109 $1,087 $1,067 $881

TC Standard car 20% 15% $1,208 $1,183 $1,159 $1,136 $1,113 $1,091 $1,070 $1,050 $867

TC Large car 10% 5% $1,455 $1,425 $1,396 $1,369 $1,341 $1,315 $1,290 $1,265 $1,044

TC Large car 15% 10% $1,425 $1,396 $1,367 $1,340 $1,313 $1,288 $1,263 $1,238 $1,023

TC Large car 20% 15% $1,394 $1,366 $1,338 $1,312 $1,286 $1,260 $1,236 $1,212 $1,001

TC Small MPV 10% 5% $1,215 $1,190 $1,166 $1,142 $1,120 $1,098 $1,077 $1,056 $872
 Technologies Considered in the Agencies’ Analysis

3-164 

TC Small MPV 15% 10% $1,196 $1,172 $1,148 $1,125 $1,103 $1,081 $1,060 $1,040 $859

TC Small MPV 20% 15% $1,178 $1,154 $1,131 $1,108 $1,086 $1,065 $1,044 $1,024 $846

TC Large MPV 10% 5% $1,349 $1,321 $1,295 $1,269 $1,243 $1,219 $1,195 $1,172 $968

TC Large MPV 15% 10% $1,325 $1,298 $1,272 $1,246 $1,222 $1,198 $1,174 $1,152 $951

TC Large MPV 20% 15% $1,301 $1,275 $1,249 $1,224 $1,200 $1,176 $1,153 $1,131 $934

TC Truck 10% 6% $1,559 $1,527 $1,496 $1,466 $1,437 $1,409 $1,382 $1,355 $1,119

TC Truck 15% 11% $1,531 $1,499 $1,469 $1,440 $1,411 $1,383 $1,356 $1,330 $1,099

TC Truck 20% 16% $1,502 $1,471 $1,442 $1,413 $1,385 $1,358 $1,331 $1,306 $1,078

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-79 Costs for PHEV20 Battery Packs for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR Net WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 3% $4,896 $3,917 $3,917 $3,134 $3,134 $3,134 $3,134 $3,134 $2,507

DMC Small car 15% 8% $4,812 $3,850 $3,850 $3,080 $3,080 $3,080 $3,080 $3,080 $2,464

DMC Small car 20% 13% $4,728 $3,783 $3,783 $3,026 $3,026 $3,026 $3,026 $3,026 $2,421

DMC Standard 

car

10% 3% $5,696 $4,557 $4,557 $3,645 $3,645 $3,645 $3,645 $3,645 $2,916

DMC Standard 

car

15% 8% $5,545 $4,436 $4,436 $3,549 $3,549 $3,549 $3,549 $3,549 $2,839

DMC Standard 

car

20% 13% $5,394 $4,315 $4,315 $3,452 $3,452 $3,452 $3,452 $3,452 $2,762

DMC Large car 10% 2% $7,219 $5,775 $5,775 $4,620 $4,620 $4,620 $4,620 $4,620 $3,696

DMC Large car 15% 7% $7,035 $5,628 $5,628 $4,502 $4,502 $4,502 $4,502 $4,502 $3,602

DMC Large car 20% 12% $6,851 $5,481 $5,481 $4,385 $4,385 $4,385 $4,385 $4,385 $3,508

DMC Small MPV 10% 3% $5,474 $4,379 $4,379 $3,504 $3,504 $3,504 $3,504 $3,504 $2,803

DMC Small MPV 15% 8% $5,370 $4,296 $4,296 $3,436 $3,436 $3,436 $3,436 $3,436 $2,749

DMC Small MPV 20% 13% $5,265 $4,212 $4,212 $3,369 $3,369 $3,369 $3,369 $3,369 $2,696

IC Small car 10% 3% $2,106 $2,034 $2,034 $1,977 $1,977 $1,977 $1,977 $1,977 $1,245

IC Small car 15% 8% $2,070 $1,999 $1,999 $1,943 $1,943 $1,943 $1,943 $1,943 $1,224

IC Small car 20% 13% $2,034 $1,964 $1,964 $1,909 $1,909 $1,909 $1,909 $1,909 $1,202

IC Standard 

car

10% 3% $2,450 $2,366 $2,366 $2,299 $2,299 $2,299 $2,299 $2,299 $1,448

IC Standard 

car

15% 8% $2,385 $2,304 $2,304 $2,238 $2,238 $2,238 $2,238 $2,238 $1,410

IC Standard 

car

20% 13% $2,321 $2,241 $2,241 $2,178 $2,178 $2,178 $2,178 $2,178 $1,372

IC Large car 10% 2% $3,105 $2,999 $2,999 $2,914 $2,914 $2,914 $2,914 $2,914 $1,835

IC Large car 15% 7% $3,026 $2,923 $2,923 $2,840 $2,840 $2,840 $2,840 $2,840 $1,789

IC Large car 20% 12% $2,947 $2,846 $2,846 $2,766 $2,766 $2,766 $2,766 $2,766 $1,742

IC Small MPV 10% 3% $2,355 $2,274 $2,274 $2,210 $2,210 $2,210 $2,210 $2,210 $1,392

IC Small MPV 15% 8% $2,310 $2,231 $2,231 $2,168 $2,168 $2,168 $2,168 $2,168 $1,365

IC Small MPV 20% 13% $2,265 $2,187 $2,187 $2,125 $2,125 $2,125 $2,125 $2,125 $1,339

TC Small car 10% 3% $7,003 $5,951 $5,951 $5,110 $5,110 $5,110 $5,110 $5,110 $3,752

TC Small car 15% 8% $6,883 $5,849 $5,849 $5,023 $5,023 $5,023 $5,023 $5,023 $3,688

TC Small car 20% 13% $6,762 $5,747 $5,747 $4,935 $4,935 $4,935 $4,935 $4,935 $3,623

TC Standard 

car

10% 3% $8,146 $6,923 $6,923 $5,944 $5,944 $5,944 $5,944 $5,944 $4,364

TC Standard 

car

15% 8% $7,930 $6,740 $6,740 $5,787 $5,787 $5,787 $5,787 $5,787 $4,249

TC Standard 

car

20% 13% $7,715 $6,556 $6,556 $5,630 $5,630 $5,630 $5,630 $5,630 $4,133

TC Large car 10% 2% $10,324 $8,774 $8,774 $7,534 $7,534 $7,534 $7,534 $7,534 $5,531

TC Large car 15% 7% $10,061 $8,551 $8,551 $7,342 $7,342 $7,342 $7,342 $7,342 $5,391

TC Large car 20% 12% $9,799 $8,327 $8,327 $7,151 $7,151 $7,151 $7,151 $7,151 $5,250

TC Small MPV 10% 3% $7,829 $6,654 $6,654 $5,713 $5,713 $5,713 $5,713 $5,713 $4,195
 Technologies Considered in the Agencies’ Analysis

3-165 

TC Small MPV 15% 8% $7,679 $6,526 $6,526 $5,604 $5,604 $5,604 $5,604 $5,604 $4,114

TC Small MPV 20% 13% $7,530 $6,399 $6,399 $5,495 $5,495 $5,495 $5,495 $5,495 $4,034

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-80 Costs for PHEV20 Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 3% $4,973 $3,978 $3,978 $3,182 $3,182 $3,182 $3,182 $3,182 $2,546

DMC Small car 15% 8% $4,888 $3,910 $3,910 $3,128 $3,128 $3,128 $3,128 $3,128 $2,503

DMC Small car 20% 13% $4,804 $3,843 $3,843 $3,074 $3,074 $3,074 $3,074 $3,074 $2,459

DMC Standard 

car 10% 3% $5,815 $4,652 $4,652 $3,722 $3,722 $3,722 $3,722 $3,722 $2,977

DMC Standard 

car 15% 8% $5,661 $4,529 $4,529 $3,623 $3,623 $3,623 $3,623 $3,623 $2,899

DMC Standard 

car 20% 13% $5,508 $4,406 $4,406 $3,525 $3,525 $3,525 $3,525 $3,525 $2,820

DMC Large car 10% 2% $7,371 $5,897 $5,897 $4,718 $4,718 $4,718 $4,718 $4,718 $3,774

DMC Large car 15% 7% $7,180 $5,744 $5,744 $4,595 $4,595 $4,595 $4,595 $4,595 $3,676

DMC Large car 20% 12% $6,989 $5,591 $5,591 $4,473 $4,473 $4,473 $4,473 $4,473 $3,578

DMC Small MPV 10% 3% $5,643 $4,514 $4,514 $3,611 $3,611 $3,611 $3,611 $3,611 $2,889

DMC Small MPV 15% 8% $5,494 $4,396 $4,396 $3,516 $3,516 $3,516 $3,516 $3,516 $2,813

DMC Small MPV 20% 13% $5,346 $4,277 $4,277 $3,422 $3,422 $3,422 $3,422 $3,422 $2,737

IC Small car 10% 3% $2,139 $2,066 $2,066 $2,007 $2,007 $2,007 $2,007 $2,007 $1,264

IC Small car 15% 8% $2,103 $2,031 $2,031 $1,973 $1,973 $1,973 $1,973 $1,973 $1,243

IC Small car 20% 13% $2,066 $1,996 $1,996 $1,939 $1,939 $1,939 $1,939 $1,939 $1,221

IC Standard 

car 10% 3% $2,501 $2,416 $2,416 $2,347 $2,347 $2,347 $2,347 $2,347 $1,479

IC Standard 

car 15% 8% $2,435 $2,352 $2,352 $2,285 $2,285 $2,285 $2,285 $2,285 $1,439

IC Standard 

car 20% 13% $2,369 $2,288 $2,288 $2,223 $2,223 $2,223 $2,223 $2,223 $1,400

IC Large car 10% 2% $3,171 $3,063 $3,063 $2,976 $2,976 $2,976 $2,976 $2,976 $1,874

IC Large car 15% 7% $3,089 $2,983 $2,983 $2,899 $2,899 $2,899 $2,899 $2,899 $1,826

IC Large car 20% 12% $3,007 $2,904 $2,904 $2,821 $2,821 $2,821 $2,821 $2,821 $1,777

IC Small MPV 10% 3% $2,427 $2,344 $2,344 $2,278 $2,278 $2,278 $2,278 $2,278 $1,435

IC Small MPV 15% 8% $2,364 $2,283 $2,283 $2,218 $2,218 $2,218 $2,218 $2,218 $1,397

IC Small MPV 20% 13% $2,300 $2,221 $2,221 $2,158 $2,158 $2,158 $2,158 $2,158 $1,359

TC Small car 10% 3% $7,112 $6,044 $6,044 $5,190 $5,190 $5,190 $5,190 $5,190 $3,810

TC Small car 15% 8% $6,991 $5,941 $5,941 $5,102 $5,102 $5,102 $5,102 $5,102 $3,746

TC Small car 20% 13% $6,870 $5,838 $5,838 $5,013 $5,013 $5,013 $5,013 $5,013 $3,681

TC Standard 

car 10% 3% $8,316 $7,068 $7,068 $6,069 $6,069 $6,069 $6,069 $6,069 $4,456

TC Standard 

car 15% 8% $8,097 $6,881 $6,881 $5,908 $5,908 $5,908 $5,908 $5,908 $4,338

TC Standard 

car 20% 13% $7,877 $6,694 $6,694 $5,748 $5,748 $5,748 $5,748 $5,748 $4,220

TC Large car 10% 2% $10,542 $8,960 $8,960 $7,693 $7,693 $7,693 $7,693 $7,693 $5,648

TC Large car 15% 7% $10,269 $8,727 $8,727 $7,494 $7,494 $7,494 $7,494 $7,494 $5,502

TC Large car 20% 12% $9,996 $8,495 $8,495 $7,294 $7,294 $7,294 $7,294 $7,294 $5,356

TC Small MPV 10% 3% $8,070 $6,858 $6,858 $5,889 $5,889 $5,889 $5,889 $5,889 $4,324

TC Small MPV 15% 8% $7,858 $6,678 $6,678 $5,734 $5,734 $5,734 $5,734 $5,734 $4,210

TC Small MPV 20% 13% $7,646 $6,498 $6,498 $5,580 $5,580 $5,580 $5,580 $5,580 $4,097

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost
 Technologies Considered in the Agencies’ Analysis

3-166 

Table 3-81 Costs for PHEV40 Battery Packs for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 15% 2% $7,063 $5,650 $5,650 $4,520 $4,520 $4,520 $4,520 $4,520 $3,616

DMC Small car 20% 7% $6,915 $5,532 $5,532 $4,425 $4,425 $4,425 $4,425 $4,425 $3,540

DMC Standard 

car

15% 3% $8,443 $6,754 $6,754 $5,404 $5,404 $5,404 $5,404 $5,404 $4,323

DMC Standard 

car

20% 8% $8,229 $6,583 $6,583 $5,266 $5,266 $5,266 $5,266 $5,266 $4,213

DMC Large car 15% 1% $11,646 $9,317 $9,317 $7,453 $7,453 $7,453 $7,453 $7,453 $5,963

DMC Large car 20% 6% $11,187 $8,950 $8,950 $7,160 $7,160 $7,160 $7,160 $7,160 $5,728

DMC Small 

MPV 15% 3% $8,179 $6,544 $6,544 $5,235 $5,235 $5,235 $5,235 $5,235 $4,188

DMC Small 

MPV 20% 8% $7,988 $6,391 $6,391 $5,113 $5,113 $5,113 $5,113 $5,113 $4,090

IC Small car 15% 2% $3,038 $2,934 $2,934 $2,851 $2,851 $2,851 $2,851 $2,851 $1,796

IC Small car 20% 7% $2,975 $2,873 $2,873 $2,791 $2,791 $2,791 $2,791 $2,791 $1,758

IC Standard 

car

15% 3% $3,632 $3,508 $3,508 $3,408 $3,408 $3,408 $3,408 $3,408 $2,147

IC Standard 

car

20% 8% $3,540 $3,419 $3,419 $3,322 $3,322 $3,322 $3,322 $3,322 $2,092

IC Large car 15% 1% $5,010 $4,838 $4,838 $4,701 $4,701 $4,701 $4,701 $4,701 $2,961

IC Large car 20% 6% $4,813 $4,648 $4,648 $4,516 $4,516 $4,516 $4,516 $4,516 $2,844

IC Small 

MPV 15% 3% $3,519 $3,398 $3,398 $3,302 $3,302 $3,302 $3,302 $3,302 $2,080

IC Small 

MPV 20% 8% $3,436 $3,319 $3,319 $3,225 $3,225 $3,225 $3,225 $3,225 $2,031

TC Small car 15% 2% $10,101 $8,584 $8,584 $7,371 $7,371 $7,371 $7,371 $7,371 $5,412

TC Small car 20% 7% $9,889 $8,404 $8,404 $7,217 $7,217 $7,217 $7,217 $7,217 $5,298

TC Standard 

car

15% 3% $12,075 $10,262 $10,262 $8,812 $8,812 $8,812 $8,812 $8,812 $6,470

TC Standard 

car

20% 8% $11,769 $10,002 $10,002 $8,588 $8,588 $8,588 $8,588 $8,588 $6,305

TC Large car 15% 1% $16,656 $14,155 $14,155 $12,155 $12,155 $12,155 $12,155 $12,155 $8,924

TC Large car 20% 6% $16,000 $13,597 $13,597 $11,676 $11,676 $11,676 $11,676 $11,676 $8,572

TC Small 

MPV 15% 3% $11,698 $9,942 $9,942 $8,537 $8,537 $8,537 $8,537 $8,537 $6,268

TC Small 

MPV 20% 8% $11,425 $9,709 $9,709 $8,337 $8,337 $8,337 $8,337 $8,337 $6,121

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-82 Costs for PHEV40 Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 15% 3% $7,175 $5,740 $5,740 $4,592 $4,592 $4,592 $4,592 $4,592 $3,674

DMC Small car 20% 8% $7,018 $5,614 $5,614 $4,491 $4,491 $4,491 $4,491 $4,491 $3,593

DMC Standard 

car

15% 3% $8,642 $6,914 $6,914 $5,531 $5,531 $5,531 $5,531 $5,531 $4,425

DMC Standard 

car

20% 8% $8,418 $6,735 $6,735 $5,388 $5,388 $5,388 $5,388 $5,388 $4,310

DMC Large car 15% 2% $11,862 $9,490 $9,490 $7,592 $7,592 $7,592 $7,592 $7,592 $6,073
 Technologies Considered in the Agencies’ Analysis

3-167 

DMC Large car 20% 7% $11,450 $9,160 $9,160 $7,328 $7,328 $7,328 $7,328 $7,328 $5,863

DMC Small 

MPV 15% 3% $8,378 $6,702 $6,702 $5,362 $5,362 $5,362 $5,362 $5,362 $4,290

DMC Small 

MPV 20% 8% $8,180 $6,544 $6,544 $5,235 $5,235 $5,235 $5,235 $5,235 $4,188

IC Small car 15% 3% $3,087 $2,981 $2,981 $2,896 $2,896 $2,896 $2,896 $2,896 $1,824

IC Small car 20% 8% $3,019 $2,916 $2,916 $2,833 $2,833 $2,833 $2,833 $2,833 $1,784

IC Standard 

car

15% 3% $3,718 $3,591 $3,591 $3,489 $3,489 $3,489 $3,489 $3,489 $2,197

IC Standard 

car

20% 8% $3,622 $3,498 $3,498 $3,398 $3,398 $3,398 $3,398 $3,398 $2,141

IC Large car 15% 2% $5,103 $4,928 $4,928 $4,789 $4,789 $4,789 $4,789 $4,789 $3,016

IC Large car 20% 7% $4,926 $4,757 $4,757 $4,622 $4,622 $4,622 $4,622 $4,622 $2,911

IC Small 

MPV 15% 3% $3,604 $3,481 $3,481 $3,382 $3,382 $3,382 $3,382 $3,382 $2,130

IC Small 

MPV 20% 8% $3,519 $3,399 $3,399 $3,302 $3,302 $3,302 $3,302 $3,302 $2,080

TC Small car 15% 3% $10,262 $8,721 $8,721 $7,488 $7,488 $7,488 $7,488 $7,488 $5,498

TC Small car 20% 8% $10,037 $8,530 $8,530 $7,324 $7,324 $7,324 $7,324 $7,324 $5,377

TC Standard 

car

15% 3% $12,360 $10,504 $10,504 $9,020 $9,020 $9,020 $9,020 $9,020 $6,622

TC Standard 

car

20% 8% $12,040 $10,232 $10,232 $8,786 $8,786 $8,786 $8,786 $8,786 $6,451

TC Large car 15% 2% $16,965 $14,418 $14,418 $12,380 $12,380 $12,380 $12,380 $12,380 $9,090

TC Large car 20% 7% $16,376 $13,918 $13,918 $11,951 $11,951 $11,951 $11,951 $11,951 $8,774

TC Small 

MPV 15% 3% $11,982 $10,183 $10,183 $8,744 $8,744 $8,744 $8,744 $8,744 $6,420

TC Small 

MPV 20% 8% $11,700 $9,943 $9,943 $8,538 $8,538 $8,538 $8,538 $8,538 $6,268

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-83 Costs for EV75 Battery Packs for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 10% $9,658 $7,726 $7,726 $6,181 $6,181 $6,181 $6,181 $6,181 $4,945

DMC Small car 15% 15% $9,476 $7,581 $7,581 $6,065 $6,065 $6,065 $6,065 $6,065 $4,852

DMC Small car 20% 20% $9,294 $7,436 $7,436 $5,948 $5,948 $5,948 $5,948 $5,948 $4,759

DMC Standard 

car

10% 10% $11,226 $8,980 $8,980 $7,184 $7,184 $7,184 $7,184 $7,184 $5,747

DMC Standard 

car

15% 15% $10,957 $8,765 $8,765 $7,012 $7,012 $7,012 $7,012 $7,012 $5,610

DMC Standard 

car

20% 20% $10,688 $8,550 $8,550 $6,840 $6,840 $6,840 $6,840 $6,840 $5,472

DMC Large car 10% 10% $14,236 $11,389 $11,389 $9,111 $9,111 $9,111 $9,111 $9,111 $7,289

DMC Large car 15% 15% $13,811 $11,049 $11,049 $8,839 $8,839 $8,839 $8,839 $8,839 $7,071

DMC Large car 20% 20% $13,385 $10,708 $10,708 $8,567 $8,567 $8,567 $8,567 $8,567 $6,853

DMC Small 

MPV 10% 9% $11,350 $9,080 $9,080 $7,264 $7,264 $7,264 $7,264 $7,264 $5,811

DMC Small 

MPV 15% 14% $11,149 $8,919 $8,919 $7,135 $7,135 $7,135 $7,135 $7,135 $5,708

DMC Small 

MPV 20% 19% $10,947 $8,758 $8,758 $7,006 $7,006 $7,006 $7,006 $7,006 $5,605

IC Small car 10% 10% $4,155 $4,012 $4,012 $3,899 $3,899 $3,899 $3,899 $3,899 $2,456

IC Small car 15% 15% $4,076 $3,937 $3,937 $3,825 $3,825 $3,825 $3,825 $3,825 $2,409
 Technologies Considered in the Agencies’ Analysis

3-168 

IC Small car 20% 20% $3,998 $3,861 $3,861 $3,752 $3,752 $3,752 $3,752 $3,752 $2,363

IC Standard 

car

10% 10% $4,829 $4,664 $4,664 $4,532 $4,532 $4,532 $4,532 $4,532 $2,854

IC Standard 

car

15% 15% $4,713 $4,552 $4,552 $4,423 $4,423 $4,423 $4,423 $4,423 $2,786

IC Standard 

car

20% 20% $4,598 $4,440 $4,440 $4,314 $4,314 $4,314 $4,314 $4,314 $2,717

IC Large car 10% 10% $6,124 $5,915 $5,915 $5,747 $5,747 $5,747 $5,747 $5,747 $3,620

IC Large car 15% 15% $5,941 $5,738 $5,738 $5,575 $5,575 $5,575 $5,575 $5,575 $3,512

IC Large car 20% 20% $5,758 $5,561 $5,561 $5,403 $5,403 $5,403 $5,403 $5,403 $3,403

IC Small 

MPV 10% 9% $4,883 $4,715 $4,715 $4,582 $4,582 $4,582 $4,582 $4,582 $2,886

IC Small 

MPV 15% 14% $4,796 $4,632 $4,632 $4,501 $4,501 $4,501 $4,501 $4,501 $2,835

IC Small 

MPV 20% 19% $4,709 $4,548 $4,548 $4,419 $4,419 $4,419 $4,419 $4,419 $2,784

TC Small car 10% 10% $13,812 $11,738 $11,738 $10,079 $10,079 $10,079 $10,079 $10,079 $7,400

TC Small car 15% 15% $13,552 $11,518 $11,518 $9,890 $9,890 $9,890 $9,890 $9,890 $7,261

TC Small car 20% 20% $13,293 $11,297 $11,297 $9,700 $9,700 $9,700 $9,700 $9,700 $7,122

TC Standard 

car

10% 10% $16,055 $13,644 $13,644 $11,716 $11,716 $11,716 $11,716 $11,716 $8,602

TC Standard 

car

15% 15% $15,670 $13,317 $13,317 $11,435 $11,435 $11,435 $11,435 $11,435 $8,396

TC Standard 

car

20% 20% $15,285 $12,990 $12,990 $11,154 $11,154 $11,154 $11,154 $11,154 $8,190

TC Large car 10% 10% $20,360 $17,303 $17,303 $14,858 $14,858 $14,858 $14,858 $14,858 $10,909

TC Large car 15% 15% $19,752 $16,786 $16,786 $14,414 $14,414 $14,414 $14,414 $14,414 $10,583

TC Large car 20% 20% $19,144 $16,269 $16,269 $13,970 $13,970 $13,970 $13,970 $13,970 $10,257

TC Small 

MPV 10% 9% $16,232 $13,795 $13,795 $11,846 $11,846 $11,846 $11,846 $11,846 $8,697

TC Small 

MPV 15% 14% $15,945 $13,551 $13,551 $11,636 $11,636 $11,636 $11,636 $11,636 $8,543

TC Small 

MPV 20% 19% $15,657 $13,306 $13,306 $11,426 $11,426 $11,426 $11,426 $11,426 $8,389

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-84 Costs for EV75 Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 10% $9,886 $7,909 $7,909 $6,327 $6,327 $6,327 $6,327 $6,327 $5,062

DMC Small car 15% 15% $9,718 $7,775 $7,775 $6,220 $6,220 $6,220 $6,220 $6,220 $4,976

DMC Small car 20% 20% $9,551 $7,640 $7,640 $6,112 $6,112 $6,112 $6,112 $6,112 $4,890

DMC Standard 

car

10% 10% $11,456 $9,164 $9,164 $7,332 $7,332 $7,332 $7,332 $7,332 $5,865

DMC Standard 

car

15% 15% $11,174 $8,939 $8,939 $7,151 $7,151 $7,151 $7,151 $7,151 $5,721

DMC Standard 

car

20% 20% $10,892 $8,713 $8,713 $6,971 $6,971 $6,971 $6,971 $6,971 $5,577

DMC Large car 10% 10% $14,592 $11,673 $11,673 $9,339 $9,339 $9,339 $9,339 $9,339 $7,471

DMC Large car 15% 15% $14,148 $11,318 $11,318 $9,055 $9,055 $9,055 $9,055 $9,055 $7,244

DMC Large car 20% 20% $13,704 $10,964 $10,964 $8,771 $8,771 $8,771 $8,771 $8,771 $7,017

DMC Small 

MPV 10% 9% $11,470 $9,176 $9,176 $7,341 $7,341 $7,341 $7,341 $7,341 $5,873

DMC Small 

MPV 15% 14% $11,260 $9,008 $9,008 $7,206 $7,206 $7,206 $7,206 $7,206 $5,765

DMC Small 

MPV 20% 19% $11,049 $8,840 $8,840 $7,072 $7,072 $7,072 $7,072 $7,072 $5,657
 Technologies Considered in the Agencies’ Analysis

3-169 

IC Small car 10% 10% $4,253 $4,107 $4,107 $3,991 $3,991 $3,991 $3,991 $3,991 $2,514

IC Small car 15% 15% $4,181 $4,038 $4,038 $3,923 $3,923 $3,923 $3,923 $3,923 $2,471

IC Small car 20% 20% $4,109 $3,968 $3,968 $3,855 $3,855 $3,855 $3,855 $3,855 $2,428

IC Standard 

car

10% 10% $4,928 $4,759 $4,759 $4,624 $4,624 $4,624 $4,624 $4,624 $2,913

IC Standard 

car

15% 15% $4,807 $4,642 $4,642 $4,511 $4,511 $4,511 $4,511 $4,511 $2,841

IC Standard 

car

20% 20% $4,685 $4,525 $4,525 $4,397 $4,397 $4,397 $4,397 $4,397 $2,769

IC Large car 10% 10% $6,277 $6,062 $6,062 $5,890 $5,890 $5,890 $5,890 $5,890 $3,710

IC Large car 15% 15% $6,086 $5,878 $5,878 $5,711 $5,711 $5,711 $5,711 $5,711 $3,597

IC Large car 20% 20% $5,895 $5,694 $5,694 $5,532 $5,532 $5,532 $5,532 $5,532 $3,485

IC Small 

MPV 10% 9% $4,934 $4,765 $4,765 $4,630 $4,630 $4,630 $4,630 $4,630 $2,917

IC Small 

MPV 15% 14% $4,844 $4,678 $4,678 $4,545 $4,545 $4,545 $4,545 $4,545 $2,863

IC Small 

MPV 20% 19% $4,753 $4,591 $4,591 $4,460 $4,460 $4,460 $4,460 $4,460 $2,809

TC Small car 10% 10% $14,139 $12,016 $12,016 $10,318 $10,318 $10,318 $10,318 $10,318 $7,575

TC Small car 15% 15% $13,899 $11,812 $11,812 $10,143 $10,143 $10,143 $10,143 $10,143 $7,447

TC Small car 20% 20% $13,659 $11,608 $11,608 $9,968 $9,968 $9,968 $9,968 $9,968 $7,318

TC Standard 

car

10% 10% $16,384 $13,924 $13,924 $11,956 $11,956 $11,956 $11,956 $11,956 $8,778

TC Standard 

car

15% 15% $15,980 $13,581 $13,581 $11,662 $11,662 $11,662 $11,662 $11,662 $8,562

TC Standard 

car

20% 20% $15,577 $13,238 $13,238 $11,367 $11,367 $11,367 $11,367 $11,367 $8,346

TC Large car 10% 10% $20,869 $17,736 $17,736 $15,229 $15,229 $15,229 $15,229 $15,229 $11,181

TC Large car 15% 15% $20,234 $17,196 $17,196 $14,766 $14,766 $14,766 $14,766 $14,766 $10,841

TC Large car 20% 20% $19,600 $16,657 $16,657 $14,303 $14,303 $14,303 $14,303 $14,303 $10,501

TC Small 

MPV 10% 9% $16,405 $13,942 $13,942 $11,971 $11,971 $11,971 $11,971 $11,971 $8,789

TC Small 

MPV 15% 14% $16,104 $13,686 $13,686 $11,752 $11,752 $11,752 $11,752 $11,752 $8,628

TC Small 

MPV 20% 19% $15,803 $13,430 $13,430 $11,532 $11,532 $11,532 $11,532 $11,532 $8,467

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-85 Costs for EV100 Battery Packs for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 4% $11,774 $9,420 $9,420 $7,536 $7,536 $7,536 $7,536 $7,536 $6,028

DMC Small car 15% 9% $11,563 $9,250 $9,250 $7,400 $7,400 $7,400 $7,400 $7,400 $5,920

DMC Small car 20% 14% $11,351 $9,081 $9,081 $7,265 $7,265 $7,265 $7,265 $7,265 $5,812

DMC Standard 

car

10% 4% $13,550 $10,840 $10,840 $8,672 $8,672 $8,672 $8,672 $8,672 $6,938

DMC Standard 

car

15% 9% $13,261 $10,609 $10,609 $8,487 $8,487 $8,487 $8,487 $8,487 $6,790

DMC Standard 

car

20% 14% $12,973 $10,378 $10,378 $8,302 $8,302 $8,302 $8,302 $8,302 $6,642

DMC Large car 10% 5% $16,403 $13,122 $13,122 $10,498 $10,498 $10,498 $10,498 $10,498 $8,398

DMC Large car 15% 10% $15,949 $12,759 $12,759 $10,207 $10,207 $10,207 $10,207 $10,207 $8,166

DMC Large car 20% 15% $15,495 $12,396 $12,396 $9,917 $9,917 $9,917 $9,917 $9,917 $7,933

DMC Small MPV 10% 3% $14,089 $11,271 $11,271 $9,017 $9,017 $9,017 $9,017 $9,017 $7,214
 Technologies Considered in the Agencies’ Analysis

3-170 

DMC Small MPV 15% 8% $13,830 $11,064 $11,064 $8,851 $8,851 $8,851 $8,851 $8,851 $7,081

DMC Small MPV 20% 13% $13,572 $10,857 $10,857 $8,686 $8,686 $8,686 $8,686 $8,686 $6,949

IC Small car 10% 4% $5,065 $4,892 $4,892 $4,753 $4,753 $4,753 $4,753 $4,753 $2,994

IC Small car 15% 9% $4,974 $4,804 $4,804 $4,668 $4,668 $4,668 $4,668 $4,668 $2,940

IC Small car 20% 14% $4,883 $4,716 $4,716 $4,582 $4,582 $4,582 $4,582 $4,582 $2,886

IC Standard 

car

10% 4% $5,829 $5,630 $5,630 $5,470 $5,470 $5,470 $5,470 $5,470 $3,445

IC Standard 

car

15% 9% $5,705 $5,510 $5,510 $5,353 $5,353 $5,353 $5,353 $5,353 $3,372

IC Standard 

car

20% 14% $5,581 $5,390 $5,390 $5,237 $5,237 $5,237 $5,237 $5,237 $3,298

IC Large car 10% 5% $7,056 $6,815 $6,815 $6,621 $6,621 $6,621 $6,621 $6,621 $4,171

IC Large car 15% 10% $6,861 $6,626 $6,626 $6,438 $6,438 $6,438 $6,438 $6,438 $4,055

IC Large car 20% 15% $6,666 $6,438 $6,438 $6,255 $6,255 $6,255 $6,255 $6,255 $3,940

IC Small MPV 10% 3% $6,061 $5,853 $5,853 $5,687 $5,687 $5,687 $5,687 $5,687 $3,582

IC Small MPV 15% 8% $5,950 $5,746 $5,746 $5,583 $5,583 $5,583 $5,583 $5,583 $3,517

IC Small MPV 20% 13% $5,838 $5,638 $5,638 $5,479 $5,479 $5,479 $5,479 $5,479 $3,451

TC Small car 10% 4% $16,840 $14,311 $14,311 $12,289 $12,289 $12,289 $12,289 $12,289 $9,022

TC Small car 15% 9% $16,537 $14,054 $14,054 $12,068 $12,068 $12,068 $12,068 $12,068 $8,860

TC Small car 20% 14% $16,234 $13,797 $13,797 $11,847 $11,847 $11,847 $11,847 $11,847 $8,698

TC Standard 

car

10% 4% $19,380 $16,470 $16,470 $14,142 $14,142 $14,142 $14,142 $14,142 $10,383

TC Standard

car

15% 9% $18,966 $16,119 $16,119 $13,841 $13,841 $13,841 $13,841 $13,841 $10,162

TC Standard 

car

20% 14% $18,553 $15,768 $15,768 $13,539 $13,539 $13,539 $13,539 $13,539 $9,940

TC Large car 10% 5% $23,459 $19,937 $19,937 $17,119 $17,119 $17,119 $17,119 $17,119 $12,569

TC Large car 15% 10% $22,810 $19,385 $19,385 $16,645 $16,645 $16,645 $16,645 $16,645 $12,221

TC Large car 20% 15% $22,161 $18,833 $18,833 $16,172 $16,172 $16,172 $16,172 $16,172 $11,873

TC Small MPV 10% 3% $20,150 $17,125 $17,125 $14,705 $14,705 $14,705 $14,705 $14,705 $10,796

TC Small MPV 15% 8% $19,780 $16,810 $16,810 $14,434 $14,434 $14,434 $14,434 $14,434 $10,598

TC Small MPV 20% 13% $19,410 $16,496 $16,496 $14,164 $14,164 $14,164 $14,164 $14,164 $10,399

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-86 Costs for EV100 Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 4% $12,046 $9,637 $9,637 $7,710 $7,710 $7,710 $7,710 $7,710 $6,168

DMC Small car 15% 9% $11,831 $9,465 $9,465 $7,572 $7,572 $7,572 $7,572 $7,572 $6,057

DMC Small car 20% 14% $11,615 $9,292 $9,292 $7,434 $7,434 $7,434 $7,434 $7,434 $5,947

DMC Standard 

car 10% 4% $13,794 $11,035 $11,035 $8,828 $8,828 $8,828 $8,828 $8,828 $7,062

DMC Standard 

car 15% 9% $13,512 $10,810 $10,810 $8,648 $8,648 $8,648 $8,648 $8,648 $6,918

DMC Standard 

car 20% 14% $13,231 $10,585 $10,585 $8,468 $8,468 $8,468 $8,468 $8,468 $6,774

DMC Large car 10% 5% $16,845 $13,476 $13,476 $10,781 $10,781 $10,781 $10,781 $10,781 $8,625

DMC Large car 15% 10% $16,382 $13,106 $13,106 $10,484 $10,484 $10,484 $10,484 $10,484 $8,388

DMC Large car 20% 15% $15,919 $12,735 $12,735 $10,188 $10,188 $10,188 $10,188 $10,188 $8,150

DMC Small MPV 10% 3% $14,246 $11,397 $11,397 $9,117 $9,117 $9,117 $9,117 $9,117 $7,294

DMC Small MPV 15% 8% $13,982 $11,185 $11,185 $8,948 $8,948 $8,948 $8,948 $8,948 $7,159

DMC Small MPV 20% 13% $13,718 $10,974 $10,974 $8,779 $8,779 $8,779 $8,779 $8,779 $7,023

IC Small car 10% 4% $5,182 $5,005 $5,005 $4,863 $4,863 $4,863 $4,863 $4,863 $3,063

IC Small car 15% 9% $5,089 $4,915 $4,915 $4,776 $4,776 $4,776 $4,776 $4,776 $3,008
 Technologies Considered in the Agencies’ Analysis

3-171 

IC Small car 20% 14% $4,997 $4,826 $4,826 $4,689 $4,689 $4,689 $4,689 $4,689 $2,953

IC Standard 

car 10% 4% $5,934 $5,731 $5,731 $5,568 $5,568 $5,568 $5,568 $5,568 $3,507

IC Standard 

car 15% 9% $5,813 $5,614 $5,614 $5,455 $5,455 $5,455 $5,455 $5,455 $3,436

IC Standard 

car 20% 14% $5,692 $5,497 $5,497 $5,341 $5,341 $5,341 $5,341 $5,341 $3,364

IC Large car 10% 5% $7,247 $6,999 $6,999 $6,800 $6,800 $6,800 $6,800 $6,800 $4,283

IC Large car 15% 10% $7,047 $6,806 $6,806 $6,613 $6,613 $6,613 $6,613 $6,613 $4,165

IC Large car 20% 15% $6,848 $6,614 $6,614 $6,426 $6,426 $6,426 $6,426 $6,426 $4,048

IC Small MPV 10% 3% $6,128 $5,919 $5,919 $5,751 $5,751 $5,751 $5,751 $5,751 $3,622

IC Small MPV 15% 8% $6,015 $5,809 $5,809 $5,644 $5,644 $5,644 $5,644 $5,644 $3,555

IC Small MPV 20% 13% $5,901 $5,699 $5,699 $5,538 $5,538 $5,538 $5,538 $5,538 $3,488

TC Small car 10% 4% $17,229 $14,642 $14,642 $12,573 $12,573 $12,573 $12,573 $12,573 $9,231

TC Small car 15% 9% $16,920 $14,380 $14,380 $12,348 $12,348 $12,348 $12,348 $12,348 $9,066

TC Small car 20% 14% $16,612 $14,118 $14,118 $12,122 $12,122 $12,122 $12,122 $12,122 $8,900

TC Standard 

car 10% 4% $19,728 $16,766 $16,766 $14,396 $14,396 $14,396 $14,396 $14,396 $10,570

TC Standard 

car 15% 9% $19,325 $16,424 $16,424 $14,103 $14,103 $14,103 $14,103 $14,103 $10,354

TC Standard 

car 20% 14% $18,923 $16,082 $16,082 $13,809 $13,809 $13,809 $13,809 $13,809 $10,138

TC Large car 10% 5% $24,092 $20,475 $20,475 $17,581 $17,581 $17,581 $17,581 $17,581 $12,908

TC Large car 15% 10% $23,429 $19,912 $19,912 $17,097 $17,097 $17,097 $17,097 $17,097 $12,553

TC Large car 20% 15% $22,767 $19,348 $19,348 $16,614 $16,614 $16,614 $16,614 $16,614 $12,198

TC Small MPV 10% 3% $20,375 $17,316 $17,316 $14,868 $14,868 $14,868 $14,868 $14,868 $10,916

TC Small MPV 15% 8% $19,997 $16,994 $16,994 $14,593 $14,593 $14,593 $14,593 $14,593 $10,714

TC Small MPV 20% 13% $19,619 $16,673 $16,673 $14,317 $14,317 $14,317 $14,317 $14,317 $10,511

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-87 Costs for EV150 Battery Packs for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 20% 2% $15,701 $12,561 $12,561 $10,049 $10,049 $10,049 $10,049 $10,049 $8,039

DMC Standard 

car

20% 2% $18,950 $15,160 $15,160 $12,128 $12,128 $12,128 $12,128 $12,128 $9,702

DMC Large car 20% 3% $21,552 $17,242 $17,242 $13,793 $13,793 $13,793 $13,793 $13,793 $11,035

DMC Small MPV 20% 1% $19,744 $15,795 $15,795 $12,636 $12,636 $12,636 $12,636 $12,636 $10,109

IC Small car 20% 2% $6,755 $6,523 $6,523 $6,338 $6,338 $6,338 $6,338 $6,338 $3,992

IC Standard 

car

20% 2% $8,152 $7,873 $7,873 $7,650 $7,650 $7,650 $7,650 $7,650 $4,818

IC Large car 20% 3% $9,272 $8,954 $8,954 $8,700 $8,700 $8,700 $8,700 $8,700 $5,480

IC Small MPV 20% 1% $8,493 $8,203 $8,203 $7,970 $7,970 $7,970 $7,970 $7,970 $5,020

TC Small car 20% 2% $22,456 $19,084 $19,084 $16,387 $16,387 $16,387 $16,387 $16,387 $12,031

TC Standard 

car

20% 2% $27,102 $23,033 $23,033 $19,777 $19,777 $19,777 $19,777 $19,777 $14,520

TC Large car 20% 3% $30,824 $26,196 $26,196 $22,494 $22,494 $22,494 $22,494 $22,494 $16,515

TC Small MPV 20% 1% $28,237 $23,998 $23,998 $20,606 $20,606 $20,606 $20,606 $20,606 $15,129

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost
 Technologies Considered in the Agencies’ Analysis

3-172 

Table 3-88 Costs for EV150 Battery Packs for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 20% 2% $16,102 $12,881 $12,881 $10,305 $10,305 $10,305 $10,305 $10,305 $8,244

DMC Standard 

car 20% 2% $19,265 $15,412 $15,412 $12,329 $12,329 $12,329 $12,329 $12,329 $9,863

DMC Large car 20% 3% $22,080 $17,664 $17,664 $14,131 $14,131 $14,131 $14,131 $14,131 $11,305

DMC Small MPV 20% 1% $19,976 $15,981 $15,981 $12,784 $12,784 $12,784 $12,784 $12,784 $10,228

IC Small car 20% 2% $6,927 $6,690 $6,690 $6,500 $6,500 $6,500 $6,500 $6,500 $4,094

IC Standard 

car 20% 2% $8,287 $8,004 $8,004 $7,777 $7,777 $7,777 $7,777 $7,777 $4,898

IC Large car 20% 3% $9,498 $9,173 $9,173 $8,913 $8,913 $8,913 $8,913 $8,913 $5,614

IC Small MPV 20% 1% $8,593 $8,299 $8,299 $8,064 $8,064 $8,064 $8,064 $8,064 $5,079

TC Small car 20% 2% $23,028 $19,571 $19,571 $16,805 $16,805 $16,805 $16,805 $16,805 $12,338

TC Standard 

car 20% 2% $27,552 $23,415 $23,415 $20,106 $20,106 $20,106 $20,106 $20,106 $14,762

TC Large car 20% 3% $31,578 $26,837 $26,837 $23,044 $23,044 $23,044 $23,044 $23,044 $16,919

TC Small MPV 20% 1% $28,569 $24,280 $24,280 $20,848 $20,848 $20,848 $20,848 $20,848 $15,307

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

For Mild HEV batteries, the agencies used a similar approach to estimating the cost of 

the battery pack but used a different approach to determining its size. Our Mild HEV system 

used in the analyses is based, largely, on the Buick eAssist system.yy

 According to the press 

releases, it includes a 15 kW motor and a 15 kW/0.5kWh/115 Volt two-module battery. For 

the agencies’ analyses, a 15kW/0.25kWh/110 Volt single-module battery was selected for 

several reasons. First, the Buick system uses a 20% state-of-charge (SOC) swing for the 

battery. We believe that, in the 2017-2025 timeframe, a 40% SOC swing is reasonable. As 

such, the energy capacity of the battery can be halved (from 0.5 to 0.25 kWh).zz The 110V 

system used in the analysis is essentially the same as Buick’s 115V system. The voltage 

change is due to our use of a 28 cell single-module battery pack rather than the 32 cell doublemodule battery pack which is used in the eAssist system. Such changes are consistent with 

our expectation that cells will increase in size allowing for fewer cells and fewer modules. 

Further, for the Mild HEV technology, the agencies are using the same system regardless of 

vehicle class or subclass. In other words, the Mild HEV system is a stand-alone technology 

that can be applied to any subclass without unique modifications for each class or subclass. 

As such, it adds more weight as a percentage to a smaller vehicle than to a larger vehicle but it 

provides more effectiveness to a smaller vehicle than to a larger vehicle. Since the same 

system is used regardless of vehicle class or subclass, the costs are identical regardless of 

vehicle class or subclass. Using the ANL BatPaC model, the Mild HEV battery DMC was 

calculated as $553 and is considered applicable to the MY 2017. The agencies derived the 

Mild HEV battery pack cost using the same methodology that was used for the P2 HEV 

 

yy “eAssist” is a Buick (or General Motors) term and is not a generic term for this technology, hence our use of 

the term mild hybrid. 

zz

 Note that projected battery cost is relatively insensitive to kWh capacity at the high power-to-energy ratio of 

these batteries. A 0.5 kWh battery could alternatively be specified at a similar cost. 
 Technologies Considered in the Agencies’ Analysis

3-173 

battery pack, and consider cost to be on the flat portion of the learning curve during the 2017-

2025 timeframe. The agencies have applied a high1 complexity ICM of 1.56 through 2024 

then 1.35 thereafter. The resultant Mild HEV battery pack costs are as shown in Table 3-89. 

The associated weight penalties are as shown in Table 3-90. 

Table 3-89 Costs for Mild Hybrid (MHEV) Battery Packs for both the 2008 and 2010 Baselines (2010$) 

Cost type Vehicle class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC All $553 $536 $520 $505 $490 $475 $461 $447 $433

IC All $312 $311 $310 $309 $308 $307 $306 $305 $187

TC All $865 $847 $830 $813 $797 $782 $766 $752 $621

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-90 EPA and NHTSA Weight Reduction Offset Associated with MHEV for both the 2008 and 2010 

Baselines 

Vehicle class Weight penalty

Small car 3.5%

Standard car 3.0%

Large car 2.5%

Small MPV 2.5%

Large MPV 2.5%

Truck 2.0%

The CAFE model does not use pre-built packages and it applies technologies 

incrementally as necessary to meet the fuel consumption reduction requirement, so the cost 

interaction between any particular technology and other technologies (cost synergies) must be 

defined. This allows flexibility so that when a technology is picked, the model will 

automatically look through the cost synergy defined in a table and apply cost adjustments 

accordingly. The total cost for mass reduction and electrification is composed of the following 

four parts: 

(1) Cost of net mass reduction; 

(2) Cost of electrification with zero mass reduction; 

(3) Mass reduction cost synergy for increased or decreased amount of mass 

reduction due to switching from conventional powertrain to electrification 

systems as defined in Figure 3-25. For an example, if a midsize passenger car 

needs both 10 percent net mass reduction and P2 hybrid to meet the CAFE 

target, the model will need to find the cost of additional 5 percent of mass 

reduction to consider the vehicle weight increase due to switching from 

conventional powertrain system to P2 electrification packages. This additional 

5 percent of mass reduction is calculated starting from 10 percent mass 

reduction, not zero as shown in Figure 3-25 because mass reduction cost versus 

mass reduction percent is not a linear function. The cost increases faster as the 
 Technologies Considered in the Agencies’ Analysis

3-174 

amount of mass reduction becomes higher. 

(4) Electrification system cost synergies (battery and non-battery components) due 

to mass reduction as defined in Table 3-76 and Table 3-103: Continuing the 

example in the steps above, if a midsize passenger car needs both10 percent 

net mass reduction and P2 hybrid to meet the CAFE target, after calculating 

the costs above, the model will need to find the cost of electrification systems, 

including battery system and non-battery system, with the required net amount 

of mass reduction using the equations in Table 3-76 and Table 3-103. Then the 

delta cost between this cost and the cost calculated in step 2, i.e. electrification 

system cost with zero applied mass reduction is calculated and treated as a cost 

synergy. These cost deltas are normally negative, i.e., a cost reduction, due to 

the downsizing of the electrification system resulting from mass reduction 

The sum of item (3) and (4) in the above list are calculated as cost synergies and 

stored in the cost synergy table as defined in NHTSA’s RIA. 

Figure 3-25 Mass Reduction Cost Example for Applied and Net Mass Reduction 

The agencies have also carefully reconsidered the power and energy requirements for 

each electrified vehicle type, which has a significant impact on the cost estimates for HEVs, 

PHEVs, and EVs as compared to the estimates used in the 2012-2016 rulemaking. 

The agencies note that, for this analysis, the agencies have assumed batteries will be 

capable of lasting the lifetime of the vehicle, which is consistent with the expected customer 

demands from this technology (as manufacturers have confirmed). Lastly, the agencies have 

focused attention on an emerging HEV technology known as a P2-hybrid, a technology not 

considered in the 2012-2016 light-duty rule. 
 Technologies Considered in the Agencies’ Analysis

3-175 

The agencies have also considered, for this analysis, the costs associated with in-home 

chargers expected to be necessary for PHEVs and EVs. Further details on in-home chargers 

and their estimated costs are presented in Section 3.4.4. 

3.4.3.10 Non-battery costs for MHEVs, HEVs, PHEVs, EVs and FCEVs 

This section addresses the costs of non-battery components which are required for 

electric drive vehicles. Some of these components are not found in every electric-drive 

vehicle (e.g. an HEV does not have an on-board battery charger as found in a PHEV or EV). 

Others are found in all electric drive vehicles and/or must be scaled to the vehicle type or 

class to properly represent the cost. The agencies derived the costs of these components from 

the FEV teardown study and the 2010 TAR. Where appropriate, costs were scaled to vehicle 

class and in the case of the motor and inverter, the sizing methodology used for battery sizing 

was applied. 

The electric drive motor and inverter provide the motive power for any electric-drive 

vehicle converting electrical energy from the battery into kinetic energy for propulsion. In an 

electric-drive vehicle, energy stored in the battery is routed to the inverter which converts it to 

a voltage and wave form that can be used by the motor. 

In many cases, such as HEVs, the combined cost of the motor and inverter exceed the 

battery cost. As batteries become larger in PHEVs and EVs, the battery cost grows faster than 

motor and inverter cost. For this analysis, the agencies used the vehicle power requirement 

calculation discussed in 3.4.3.8 to calculate the required motor and inverter size for each 

vehicle class at each weight reduction point. Then, for the HEVs and PHEVs, a regression 

was created from the FEV teardown data for motors and inverters and this regression was 

used to calculate the motor and inverter cost for each combination of vehicle class and weight 

reduction. This regression for use with the 2008 baseline was $13.78x(motor size in 

kW)+$781.50 (values in 2010$), and for use with the 2010 baseline was $14.13x(motor size 

in kW)+$771.21 (values in 2010$). The results are shown as the “Motor assembly” line item 

in Table 3-91 through Table 3-96, which show our scaled DMC for P2 HEV, PHEV20 and 

PHEV40, respectively, for both the 2008 and 2010 baselines. 

For EVs, the agencies used the motor and inverter cost regression from the 2010 TAR 

(see 2010 TAR at page B-21) and we used that regression for both the 2008 and 2010 

baselines. Since the FEV teardown was conducted on an HEV Ford Fusion, the agencies 

believe the technology for an EV is different enough to warrant using the TAR regression. 

The regression presented in the TAR showed the DMC being equal to $8.45x(motor size in 

kW)+$185.05 (values in 2010$). The results are presented as separate line items for “Motor 

inverter” and “Motor assembly” in Table 3-97 through Table 3-102, which show our scaled 

DMC for EV75, EV100 and EV150, respectively, for both the 2008 and 2010 baselines. 

In addition to electric drive motors and inverters, there are several other components 

in electric drive vehicles that are required. These components include the following: 

• Body Modifications which are required on HEVs and PHEVs include changes 

to sheet metal to accommodate electric drive components and the addition of fasteners to 

secure components such as electric cables. These costs come from the FEV teardown and are 

scaled by vehicle class. For EVs, these costs are assumed to be included in the base vehicle 

because they are less likely to be adapted from conventional vehicles. 
 Technologies Considered in the Agencies’ Analysis

3-176 

• Brake System changes include the addition of a braking system that can control 

the vehicle’s regenerative braking system—a key enabler of electric drive vehicle efficiency. 

The brake system costs are from the FEV teardown and are scaled to vehicle class. 

• Climate Control System includes components such as an electric air 

conditioning compressor that enables operation while the engine is off for HEVs and PHEVs 

as well as for an EV which has no engine. Climate control system costs come from the FEV 

teardown and are scaled to vehicle class. 

• Conventional vehicle battery and alternator are deleted in these vehicles, for a 

cost savings, replaced by the DC-DC converter which converts the high-voltage traction 

battery to a nominal 12V DC to operate the vehicle’s accessories. This credit comes from the 

FEV teardown study and is scaled to vehicle class. 

• DC-DC converter converts the high-voltage battery voltage to a nominal 12V 

battery voltage to run vehicle accessories such as the radio, lights and wipers. This cost 

comes from the FEV teardown study and is scaled to vehicle class. 

• Power distribution and Control consists of those components which route 

electricity to the motor, inverter and contains the controllers to operate and monitor the 

electric drive system. This cost applies to HEVs and PHEVs and comes from the FEV 

teardown study. It is scaled to vehicle class. 

• On-Vehicle Charger consists of the components necessary to charge a PHEV 

or EV from an outlet. It includes the charging port, wiring and electronics necessary to 

convert a 120V or 240V AC input to the high-voltage DC power necessary to charge the 

battery. Because the FEV teardown study subject vehicle did not have an on-vehicle charger, 

the costs from the TAR were used for this item. It is not scaled to vehicle class, however the 

EV charger is assumed to cost twice the amount of the PHEV charger to account for a higher 

current capacity. This cost does not include off-vehicle charger components which are 

discussed in Section 3.4.4, below.

• Supplemental heating is required for passenger comfort on PHEVs and EVs 

which may operate for long periods with no engine heat available. This cost comes from the 

FEV teardown study and is scaled to vehicle class. The supplemental heater on the EV is 

assumed to be three times more costly than the PHEV because the entire cabin comfort is 

dependent on the supplemental heater. In a PHEV, it is assumed that in extreme conditions, 

the internal combustion engine will start to provide additional cabin heat and defrost 

functions. 

• High Voltage Wiring is an item used on EVs only. It includes the high voltage 

cabling from the battery to the inverter and motor as well as control components. It is 

equivalent to the power distribution and control used on HEVs and PHEVs and comes from 

the FEV teardown study. It is scaled to vehicle class. 

• Delete Internal Combustion Engine and Transmission For EVs, the engine and 

transmission are deleted and a credit is applied. These credits come from work done in 

support of the 2010 TAR and are scaled to vehicle class. 

• Battery Discharge System For HEVs, PHEVs and EVs, it is expected that 

manufacturers will provide the means to safely discharge battery packs following a vehicle 
 Technologies Considered in the Agencies’ Analysis

3-177 

crash. The agencies have assumed that this would include dedicated DC terminals, an access 

panel for the terminals, and a diagnostics port. The estimated cost of this capability is the 

same for all vehicle classes, but is different for HEVs than for PHEVs and EVs. 

The results of the scaling exercise applied to non-battery components are presented in 

Table 3-91 through Table 3-102 for P2 HEVs, PHEV20, PHEV40, EV75, EV100 and EV150, 

for the 2008 and 2010 baselines, respectively. 

Table 3-91 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for P2 HEV for the 2008 

Baseline (2010$) 

System Small 

car

Standard 

car

Large 

car

Small 

MPV

Large 

MPV Truck

0% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $221 $228 $231 $225 $233 $240

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $162 $152 $152 $177

Power Distr & control $196 $201 $204 $200 $206 $220

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,045 $1,172 $1,480 $1,112 $1,287 $1,429

Total $1,675 $1,857 $2,175 $1,777 $2,052 $2,169

2% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $221 $228 $231 $225 $233 $240

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $162 $152 $152 $177

Power Distr & control $196 $201 $204 $200 $206 $220

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,039 $1,164 $1,467 $1,106 $1,277 $1,416

Total $1,670 $1,849 $2,161 $1,771 $2,042 $2,156

7.5% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $221 $228 $231 $225 $233 $240

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $162 $152 $152 $177

Power Distr & control $196 $201 $204 $200 $206 $220

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,025 $1,143 $1,428 $1,088 $1,249 $1,381

Total $1,655 $1,828 $2,123 $1,752 $2,014 $2,121

10% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $221 $228 $231 $225 $233 $240

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $162 $152 $152 $177

Power Distr & control $196 $201 $204 $200 $206 $220

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,018 $1,133 $1,411 $1,079 $1,237 $1,364
 Technologies Considered in the Agencies’ Analysis

3-178 

Total $1,649 $1,818 $2,105 $1,744 $2,002 $2,104

20% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $221 $228 $231 $225 $233 $240

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $162 $152 $152 $177

Power Distr & control $196 $201 $204 $200 $206 $220

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,007 $1,115 $1,377 $1,064 $1,212 $1,337

Total $1,637 $1,800 $2,071 $1,729 $1,977 $2,077

Table 3-92 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for P2 HEV for the 2010 

Baseline (2010$) 

System Small car Standard car Large car Small MPV Large MPV Truck

0% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $223 $229 $232 $225 $232 $242

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $177 $162 $162 $177

Power Distr & control $197 $202 $205 $201 $206 $221

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,051 $1,191 $1,512 $1,134 $1,299 $1,445

Total $1,683 $1,878 $2,224 $1,811 $2,073 $2,188

2% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $223 $229 $232 $225 $232 $242

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $177 $162 $162 $177

Power Distr & control $197 $202 $205 $201 $206 $221

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,045 $1,183 $1,497 $1,127 $1,288 $1,432

Total $1,677 $1,869 $2,210 $1,804 $2,063 $2,175

7.5% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $223 $229 $232 $225 $232 $242

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $177 $162 $162 $177

Power Distr & control $197 $202 $205 $201 $206 $221

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,030 $1,159 $1,457 $1,107 $1,259 $1,395

Total $1,662 $1,846 $2,169 $1,784 $2,034 $2,138

10% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $223 $229 $232 $225 $232 $242

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $177 $162 $162 $177

Power Distr & control $197 $202 $205 $201 $206 $221
 Technologies Considered in the Agencies’ Analysis

3-179 

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,023 $1,149 $1,438 $1,098 $1,246 $1,378

Total $1,655 $1,836 $2,150 $1,775 $2,021 $2,121

20% WR

Body system $6 $6 $6 $6 $6 $6

Brake system $223 $229 $232 $225 $232 $242

Climate controls $140 $157 $168 $164 $250 $186

Delete electrical -$60 -$65 -$82 -$86 -$86 -$94

DC-DC converter $121 $152 $177 $162 $162 $177

Power Distr & control $197 $202 $205 $201 $206 $221

Battery discharge system $6 $6 $6 $6 $6 $6

Motor assembly $1,010 $1,129 $1,402 $1,081 $1,220 $1,350

Total $1,642 $1,816 $2,114 $1,757 $1,994 $2,093

Table 3-93 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for PHEV20 for the 2008 

Baseline (2010$) 

System Small car Standard car Large car Small MPV

0% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,097 $2,735 $4,276 $2,436

Battery discharge system $13 $13 $13 $13

Total $2,878 $3,575 $5,129 $3,258

2% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,071 $2,695 $4,207 $2,403

Battery discharge system $13 $13 $13 $13

Total $2,852 $3,536 $5,059 $3,225

7.5% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $1,999 $2,588 $4,014 $2,312

Battery discharge system $13 $13 $13 $13
 Technologies Considered in the Agencies’ Analysis

3-180 

Total $2,780 $3,428 $4,867 $3,134

10% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $1,966 $2,539 $3,927 $2,271

Battery discharge system $13 $13 $13 $13

Total $2,747 $3,379 $4,780 $3,093

20% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $1,943 $2,500 $3,861 $2,235

Battery discharge system $13 $13 $13 $13

Total $2,724 $3,341 $4,714 $3,057

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-94 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for PHEV20 for the 2010 

Baseline (2010$) 

System Small car Standard car Large car Small MPV

0% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,169 $2,870 $4,476 $2,586

Battery discharge system $13 $13 $13 $13

Total $2,951 $3,712 $5,347 $3,419

2% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,141 $2,828 $4,402 $2,549
 Technologies Considered in the Agencies’ Analysis

3-181 

Battery discharge system $13 $13 $13 $13

Total $2,924 $3,670 $5,272 $3,383

7.5% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,064 $2,712 $4,198 $2,450

Battery discharge system $13 $13 $13 $13

Total $2,847 $3,554 $5,069 $3,283

10% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,029 $2,660 $4,106 $2,404

Battery discharge system $13 $13 $13 $13

Total $2,812 $3,502 $4,976 $3,238

20% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,002 $2,616 $4,031 $2,364

Battery discharge system $13 $13 $13 $13

Total $2,785 $3,458 $4,901 $3,197

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-95 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for PHEV40 for the 2008 

Baseline (2010$) 

System Small car Standard car Large car Small MPV

0% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105
 Technologies Considered in the Agencies’ Analysis

3-182 

Supplemental heater $38 $43 $45 $44

Motor assembly $2,097 $2,735 $4,276 $2,436

Battery discharge system $13 $13 $13 $13

Total $2,878 $3,575 $5,129 $3,258

2% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,071 $2,695 $4,207 $2,403

Battery discharge system $13 $13 $13 $13

Total $2,852 $3,536 $5,059 $3,225

7.5% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,007 $2,591 $4,025 $2,313

Battery discharge system $13 $13 $13 $13

Total $2,788 $3,432 $4,878 $3,135

10% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,007 $2,591 $4,025 $2,312

Battery discharge system $13 $13 $13 $13

Total $2,788 $3,432 $4,878 $3,134

20% WR

Body system $6 $6 $6 $6

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

Power Distr & control $196 $201 $204 $200

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,007 $2,591 $4,025 $2,312

Battery discharge system $13 $13 $13 $13

Total $2,788 $3,432 $4,878 $3,134

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.
 Technologies Considered in the Agencies’ Analysis

3-183 

Table 3-96 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for PHEV40 for the 2010 

Baseline (2010$) a

System Small car Standard car Large car Small MPV

0% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,169 $2,870 $4,476 $2,586

Battery discharge system $13 $13 $13 $13

Total $2,951 $3,712 $5,347 $3,419

2% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,141 $2,828 $4,402 $2,549

Battery discharge system $13 $13 $13 $13

Total $2,924 $3,670 $5,272 $3,383

7.5% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,068 $2,714 $4,206 $2,450

Battery discharge system $13 $13 $13 $13

Total $2,851 $3,556 $5,076 $3,283

10% WR

Body system $6 $6 $6 $6

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,068 $2,714 $4,206 $2,449

Battery discharge system $13 $13 $13 $13

Total $2,851 $3,556 $5,076 $3,283

20% WR

Body system $6 $6 $6 $6
 Technologies Considered in the Agencies’ Analysis

3-184 

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

Power Distr & control $197 $202 $205 $201

On vehicle charger $105 $105 $105 $105

Supplemental heater $38 $43 $45 $44

Motor assembly $2,068 $2,714 $4,206 $2,449

Battery discharge system $13 $13 $13 $13

Total $2,851 $3,556 $5,076 $3,283

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-97 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV75 for the 2008 Baseline 

(2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $703 $1,044 $1,868 $885

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $992 $1,383 $2,329 $1,200

Battery discharge system $13 $13 $13 $13

Total $350 $1,145 $2,060 -$12

2% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $689 $1,023 $1,831 $867

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $976 $1,359 $2,286 $1,180

Battery discharge system $13 $13 $13 $13

Total $320 $1,100 $1,979 -$50

7.5% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89
 Technologies Considered in the Agencies’ Analysis

3-185 

On vehicle charger $316 $316 $316 $316

Motor inverter $650 $966 $1,728 $818

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $932 $1,293 $2,168 $1,124

Battery discharge system $13 $13 $13 $13

Total $237 $977 $1,759 -$154

10% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $633 $939 $1,681 $796

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $911 $1,263 $2,114 $1,099

Battery discharge system $13 $13 $13 $13

Total $199 $921 $1,659 -$202

20% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $571 $851 $1,519 $727

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $840 $1,162 $1,928 $1,020

Battery discharge system $13 $13 $13 $13

Total $65 $731 $1,309 -$350

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-98 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV75 for the 2010 Baseline 

(2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $729 $1,094 $1,932 $946
 Technologies Considered in the Agencies’ Analysis

3-186 

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,021 $1,441 $2,402 $1,271

Battery discharge system $13 $13 $13 $13

Total $406 $1,255 $2,214 $132

2% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $714 $1,072 $1,893 $927

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,004 $1,416 $2,358 $1,249

Battery discharge system $13 $13 $13 $13

Total $375 $1,208 $2,131 $92

7.5% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $674 $1,012 $1,787 $875

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $958 $1,347 $2,236 $1,189

Battery discharge system $13 $13 $13 $13

Total $289 $1,079 $1,903 -$20

10% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $656 $985 $1,739 $851

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $938 $1,315 $2,180 $1,162

Battery discharge system $13 $13 $13 $13

Total $250 $1,020 $1,799 -$71

20% WR

Brake system $223 $229 $232 $225
 Technologies Considered in the Agencies’ Analysis

3-187 

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $595 $895 $1,580 $780

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $867 $1,212 $1,998 $1,080

Battery discharge system $13 $13 $13 $13

Total $118 $828 $1,458 -$225

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-99 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV100 for the 2008 

Baseline (2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $703 $1,044 $1,868 $885

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $992 $1,383 $2,329 $1,200

Battery discharge system $13 $13 $13 $13

Total $350 $1,145 $2,060 -$12

2% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $689 $1,023 $1,831 $867

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $976 $1,359 $2,286 $1,180

Battery discharge system $13 $13 $13 $13

Total $320 $1,100 $1,979 -$50

7.5% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86
 Technologies Considered in the Agencies’ Analysis

3-188 

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $650 $966 $1,728 $818

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $932 $1,293 $2,168 $1,124

Battery discharge system $13 $13 $13 $13

Total $237 $977 $1,759 -$154

10% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $633 $939 $1,681 $796

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $911 $1,263 $2,114 $1,099

Battery discharge system $13 $13 $13 $13

Total $199 $921 $1,659 -$202

20% WR

Brake system $221 $228 $231 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $608 $906 $1,617 $774

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $883 $1,224 $2,041 $1,073

Battery discharge system $13 $13 $13 $13

Total $146 $848 $1,521 -$249

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-100 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV100 for the 2010 

Baseline (2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201
 Technologies Considered in the Agencies’ Analysis

3-189 

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $729 $1,094 $1,932 $946

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,021 $1,441 $2,402 $1,271

Battery discharge system $13 $13 $13 $13

Total $406 $1,255 $2,214 $132

2% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $714 $1,072 $1,893 $927

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,004 $1,416 $2,358 $1,249

Battery discharge system $13 $13 $13 $13

Total $375 $1,208 $2,131 $92

7.5% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $674 $1,012 $1,787 $875

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $958 $1,347 $2,236 $1,189

Battery discharge system $13 $13 $13 $13

Total $289 $1,079 $1,903 -$20

10% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $656 $985 $1,739 $851

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $938 $1,315 $2,180 $1,162

Battery discharge system $13 $13 $13 $13
 Technologies Considered in the Agencies’ Analysis

3-190 

Total $250 $1,020 $1,799 -$71

20% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $633 $954 $1,684 $829

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $912 $1,280 $2,118 $1,137

Battery discharge system $13 $13 $13 $13

Total $201 $954 $1,682 -$118

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-101 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV150 for the 2008 

Baseline (2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $703 $1,044 $1,868 $885

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $992 $1,383 $2,329 $1,200

Battery discharge system $13 $13 $13 $13

Total $351 $1,146 $2,061 -$11

2% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $692 $1,028 $1,837 $878

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $979 $1,364 $2,293 $1,193

Battery discharge system $13 $13 $13 $13

Total $328 $1,111 $1,995 -$26

7.5% WR
 Technologies Considered in the Agencies’ Analysis

3-191 

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $692 $1,028 $1,837 $878

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $979 $1,364 $2,293 $1,193

Battery discharge system $13 $13 $13 $13

Total $328 $1,111 $1,995 -$26

10% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $692 $1,028 $1,837 $878

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $979 $1,364 $2,293 $1,193

Battery discharge system $13 $13 $13 $13

Total $328 $1,111 $1,995 -$26

20% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $162 $152

High voltage wiring $196 $201 $204 $200

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $692 $1,028 $1,837 $878

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $979 $1,364 $2,293 $1,193

Battery discharge system $13 $13 $13 $13

Total $328 $1,111 $1,995 -$26

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Table 3-102 Scaled Non-battery DMC by Applied Vehicle Weight Reduction for EV150 for the 2010 

Baseline (2010$) a

System Small car Standard car Large car Small MPV

0% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164
 Technologies Considered in the Agencies’ Analysis

3-192 

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $729 $1,094 $1,932 $946

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,021 $1,441 $2,402 $1,271

Battery discharge system $13 $13 $13 $13

Total $406 $1,255 $2,214 $132

2% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $720 $1,081 $1,910 $941

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,011 $1,425 $2,377 $1,265

Battery discharge system $13 $13 $13 $13

Total $387 $1,226 $2,167 $121

7.5% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $720 $1,081 $1,910 $941

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,011 $1,425 $2,377 $1,265

Battery discharge system $13 $13 $13 $13

Total $387 $1,226 $2,167 $121

10% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $720 $1,081 $1,910 $941

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394
 Technologies Considered in the Agencies’ Analysis

3-193 

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,011 $1,425 $2,377 $1,265

Battery discharge system $13 $13 $13 $13

Total $387 $1,226 $2,167 $121

20% WR

Brake system $223 $229 $232 $225

Climate controls $140 $157 $168 $164

Delete electrical -$60 -$65 -$82 -$86

DC-DC converter $121 $152 $177 $162

High voltage wiring $197 $202 $205 $201

Supplemental heater $76 $85 $91 $89

On vehicle charger $316 $316 $316 $316

Motor inverter $720 $1,081 $1,910 $941

Controls $121 $121 $121 $121

Delete IC engine -$1,596 -$1,596 -$2,466 -$2,394

Delete transmission -$894 -$894 -$894 -$894

Motor assembly $1,011 $1,425 $2,377 $1,265

Battery discharge system $13 $13 $13 $13

Total $387 $1,226 $2,167 $121

a The agencies have not estimated PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle 

classes would use the technologies.

Similar to the approach taken for battery pack costs, the agencies generated linear 

regressions of non-battery system costs against percent of net mass reduction and the results 

are shown in Table 3-103. This was done using the same weight reduction offsets as used for 

battery packs as presented in Table 3-75. The agencies separated battery pack costs from the 

remainder of the systems for each type of electrified vehicle. The advantage of separating the 

battery pack costs from other system costs is that it allows each to carry unique indirect cost 

multipliers and learning effects which are important given that battery technology is an 

emerging technology, while electric motors and inverters are more stable technologies. 

Table 3-103 Linear Regressions of Non-Battery System Direct Manufacturing Costs vs Net Mass 

reduction (2010$) 

Vehicle

Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

2008 Baseline

Small car -$263x+$1,675 -$1,316x+$2,878 -$1,316x+$2,878 -$1,510x+$350 -$1,510x+$350 -$1,510x+$351

Standard 

car

-$391x+$1,857 -$1,953x+$3,575 -$1,953x+$3,575 -$2,242x+$1,145 -$2,242x+$1,145 -$2,242x+$1,146

Large car -$699x+$2,175 -$3,495x+$5,129 -$3,495x+$5,129 -$4,012x+$2,060 -$4,012x+$2,060 -$4,012x+$2,061

Small 

MPV -$331x+$1,777 -$1,655x+$3,258 -$1,655x+$3,258 -$1,900x+-$12 -$1,900x+-$12 -$1,900x+-$11

Large 

MPV -$506x+$2,052

Truck -$648x+$2,169

2010 Baseline

Small car -$279x+$1,683 -$1,397x+$2,951 -$1,397x+$2,951 -$1,565x+$406 -$1,565x+$406 -$1,565x+$406

Standard 

car

-$420x+$1,878 -$2,099x+$3,712 -$2,099x+$3,712 -$2,350x+$1,255 -$2,350x+$1,255 -$2,350x+$1,255

Large car -$741x+$2,224 -$3,705x+$5,347 -$3,705x+$5,347 -$4,149x+$2,214 -$4,149x+$2,214 -$4,149x+$2,214

Small 

MPV -$363x+$1,811 -$1,814x+$3,419 -$1,814x+$3,419 -$2,032x+$132 -$2,032x+$132 -$2,032x+$132

Large 

MPV -$528x+$2,073

Truck -$674x+$2,188

Notes:
 Technologies Considered in the Agencies’ Analysis

3-194 

“x” in the equations represents the net weight reduction as a percentage, so the non-battery components for a small car P2 HEV (2008 

baseline) with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-$263)x(15%)+$1,675=$1,635.The 

agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not believe these vehicle classes would 

use the technologies.

For P2 HEV and PHEV non-battery components, the direct manufacturing costs 

shown in Table 3-103 are considered applicable to the 2012MY. The agencies consider the 

P2 and PHEV non-battery component technologies to be on the flat portion of the learning 

curve during the 2017-2025 timeframe. The agencies have applied a high1 complexity ICM 

of 1.56 through 2018 then 1.35 thereafter. For EV non-battery components, the direct 

manufacturing costs shown in Table 3-103 are considered applicable to the 2017MY. The 

agencies consider the EV non-battery component technologies to be on the flat portion of the 

learning curve during the 2017-2025 timeframe. The agencies have applied a high2 

complexity ICM of 1.77 through 2024 then 1.50 thereafter. The resultant costs for P2 HEV, 

PHEV20, PHEV40, EV75, EV100 and EV150 non-battery components for the 2008 and 2010 

baselines are shown in Table 3-104 through Table 3-115, respectively.aaa

Table 3-104 Costs for P2 HEV Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type Vehicle class Applied WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 5% $1,442 $1,413 $1,385 $1,357 $1,330 $1,303 $1,277 $1,252 $1,227

DMC Small car 15% 10% $1,430 $1,402 $1,374 $1,346 $1,319 $1,293 $1,267 $1,242 $1,217

DMC Small car 20% 15% $1,419 $1,391 $1,363 $1,335 $1,309 $1,283 $1,257 $1,232 $1,207

DMC Standard car 10% 5% $1,594 $1,562 $1,531 $1,500 $1,470 $1,441 $1,412 $1,384 $1,356

DMC Standard car 15% 10% $1,577 $1,546 $1,515 $1,484 $1,455 $1,426 $1,397 $1,369 $1,342

DMC Standard car 20% 15% $1,560 $1,529 $1,498 $1,468 $1,439 $1,410 $1,382 $1,354 $1,327

DMC Large car 10% 5% $1,857 $1,820 $1,783 $1,747 $1,713 $1,678 $1,645 $1,612 $1,580

DMC Large car 15% 10% $1,826 $1,790 $1,754 $1,719 $1,685 $1,651 $1,618 $1,585 $1,554

DMC Large car 20% 15% $1,796 $1,760 $1,725 $1,690 $1,657 $1,623 $1,591 $1,559 $1,528

DMC Small MPV 10% 5% $1,528 $1,497 $1,467 $1,438 $1,409 $1,381 $1,353 $1,326 $1,300

DMC Small MPV 15% 10% $1,513 $1,483 $1,453 $1,424 $1,396 $1,368 $1,340 $1,314 $1,287

DMC Small MPV 20% 15% $1,499 $1,469 $1,440 $1,411 $1,383 $1,355 $1,328 $1,301 $1,275

DMC Large MPV 10% 5% $1,759 $1,723 $1,689 $1,655 $1,622 $1,590 $1,558 $1,527 $1,496

DMC Large MPV 15% 10% $1,737 $1,702 $1,668 $1,634 $1,602 $1,570 $1,538 $1,508 $1,477

DMC Large MPV 20% 15% $1,715 $1,680 $1,647 $1,614 $1,582 $1,550 $1,519 $1,489 $1,459

DMC Truck 10% 6% $1,848 $1,811 $1,775 $1,739 $1,705 $1,670 $1,637 $1,604 $1,572

DMC Truck 15% 11% $1,820 $1,784 $1,748 $1,713 $1,679 $1,645 $1,612 $1,580 $1,548

DMC Truck 20% 16% $1,792 $1,756 $1,721 $1,686 $1,653 $1,620 $1,587 $1,556 $1,524

IC Small car 10% 5% $922 $920 $565 $564 $563 $563 $562 $561 $560

 

aaa Note that, in the draft Joint TSD, we inadvertently stated the following with respect to the years in which costs 

were considered valid and the years for which near term and long term ICMs were applied: “For P2 HEV nonbattery components, the direct manufacturing costs shown in Table 3-103 are considered applicable to the 

2017MY. The agencies consider the P2 non-battery component technologies to be on the flat portion of the 

learning curve during the 2017-2025 timeframe. The agencies have applied a high1 complexity ICM of 1.56 

through 2018 then 1.35 thereafter. For PHEV and EV non-battery components, the direct manufacturing costs 

shown in Table 3-103 are considered applicable to the 2025MY. The agencies consider the PHEV and EV nonbattery component technologies to be on the flat portion of the learning curve during the 2017-2025 timeframe. 

The agencies have applied a high2 complexity ICM of 1.77 through 2024 then 1.50 thereafter.” Importantly, the 

costs then (and now) were calculated according to the corrected text shown in this final Joint TSD. 
 Technologies Considered in the Agencies’ Analysis

3-195 

IC Small car 15% 10% $915 $913 $561 $560 $559 $558 $557 $556 $556

IC Small car 20% 15% $908 $906 $556 $555 $554 $554 $553 $552 $551

IC Standard car 10% 5% $1,020 $1,018 $625 $624 $623 $622 $621 $620 $619

IC Standard car 15% 10% $1,009 $1,007 $618 $617 $616 $615 $614 $614 $613

IC Standard car 20% 15% $998 $996 $612 $611 $610 $609 $608 $607 $606

IC Large car 10% 5% $1,188 $1,185 $728 $727 $726 $724 $723 $722 $721

IC Large car 15% 10% $1,168 $1,166 $716 $715 $714 $713 $712 $711 $710

IC Large car 20% 15% $1,149 $1,147 $704 $703 $702 $701 $700 $699 $698

IC Small MPV 10% 5% $977 $975 $599 $598 $597 $596 $595 $594 $593

IC Small MPV 15% 10% $968 $966 $593 $592 $591 $590 $590 $589 $588

IC Small MPV 20% 15% $959 $957 $588 $587 $586 $585 $584 $583 $582

IC Large MPV 10% 5% $1,125 $1,123 $689 $688 $687 $686 $685 $684 $683

IC Large MPV 15% 10% $1,111 $1,109 $681 $680 $679 $678 $677 $676 $675

IC Large MPV 20% 15% $1,097 $1,095 $672 $671 $670 $669 $668 $667 $666

IC Truck 10% 6% $1,182 $1,180 $724 $723 $722 $721 $720 $719 $718

IC Truck 15% 11% $1,164 $1,162 $713 $712 $711 $710 $709 $708 $707

IC Truck 20% 16% $1,146 $1,144 $702 $701 $700 $699 $698 $697 $696

TC Small car 10% 5% $2,364 $2,333 $1,950 $1,921 $1,893 $1,866 $1,839 $1,813 $1,787

TC Small car 15% 10% $2,345 $2,315 $1,934 $1,906 $1,878 $1,851 $1,824 $1,798 $1,773

TC Small car 20% 15% $2,327 $2,296 $1,919 $1,891 $1,863 $1,836 $1,810 $1,784 $1,758

TC Standard car 10% 5% $2,614 $2,580 $2,156 $2,124 $2,093 $2,063 $2,033 $2,004 $1,975

TC Standard car 15% 10% $2,586 $2,552 $2,133 $2,102 $2,071 $2,041 $2,012 $1,983 $1,954

TC Standard car 20% 15% $2,558 $2,525 $2,110 $2,079 $2,049 $2,019 $1,990 $1,961 $1,933

TC Large car 10% 5% $3,044 $3,005 $2,511 $2,474 $2,438 $2,403 $2,368 $2,334 $2,301

TC Large car 15% 10% $2,995 $2,956 $2,470 $2,434 $2,398 $2,364 $2,329 $2,296 $2,263

TC Large car 20% 15% $2,945 $2,907 $2,429 $2,393 $2,358 $2,324 $2,291 $2,258 $2,226

TC Small MPV 10% 5% $2,505 $2,472 $2,066 $2,036 $2,006 $1,977 $1,948 $1,920 $1,893

TC Small MPV 15% 10% $2,481 $2,449 $2,046 $2,016 $1,987 $1,958 $1,930 $1,902 $1,875

TC Small MPV 20% 15% $2,458 $2,426 $2,027 $1,997 $1,968 $1,940 $1,912 $1,884 $1,857

TC Large MPV 10% 5% $2,884 $2,846 $2,378 $2,343 $2,309 $2,276 $2,243 $2,211 $2,179

TC Large MPV 15% 10% $2,848 $2,811 $2,349 $2,314 $2,280 $2,247 $2,215 $2,183 $2,152

TC Large MPV 20% 15% $2,812 $2,775 $2,319 $2,285 $2,252 $2,219 $2,187 $2,156 $2,125

TC Truck 10% 6% $3,030 $2,991 $2,499 $2,463 $2,427 $2,392 $2,357 $2,323 $2,290

TC Truck 15% 11% $2,984 $2,946 $2,461 $2,425 $2,390 $2,355 $2,321 $2,288 $2,255

TC Truck 20% 16% $2,938 $2,900 $2,423 $2,388 $2,353 $2,319 $2,285 $2,253 $2,221

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-105 Costs for P2 HEV Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

Vehicle class Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 5% $1,448 $1,419 $1,390 $1,363 $1,335 $1,309 $1,282 $1,257 $1,232

DMC Small car 15% 10% $1,436 $1,407 $1,379 $1,351 $1,324 $1,298 $1,272 $1,246 $1,221

DMC Small car 20% 15% $1,423 $1,395 $1,367 $1,340 $1,313 $1,287 $1,261 $1,236 $1,211

DMC Standard car 10% 5% $1,611 $1,579 $1,547 $1,516 $1,486 $1,456 $1,427 $1,398 $1,370

DMC Standard car 15% 10% $1,593 $1,561 $1,529 $1,499 $1,469 $1,440 $1,411 $1,383 $1,355

DMC Standard car 20% 15% $1,574 $1,543 $1,512 $1,482 $1,452 $1,423 $1,395 $1,367 $1,339

DMC Large car 10% 5% $1,898 $1,860 $1,823 $1,786 $1,750 $1,715 $1,681 $1,647 $1,614

DMC Large car 15% 10% $1,866 $1,828 $1,792 $1,756 $1,721 $1,686 $1,653 $1,620 $1,587

DMC Large car 20% 15% $1,833 $1,797 $1,761 $1,726 $1,691 $1,657 $1,624 $1,592 $1,560

DMC Small MPV 10% 5% $1,555 $1,524 $1,494 $1,464 $1,435 $1,406 $1,378 $1,350 $1,323

DMC Small MPV 15% 10% $1,540 $1,509 $1,479 $1,449 $1,420 $1,392 $1,364 $1,337 $1,310

DMC Small MPV 20% 15% $1,524 $1,493 $1,464 $1,434 $1,406 $1,377 $1,350 $1,323 $1,296

DMC Large MPV 10% 5% $1,776 $1,740 $1,706 $1,672 $1,638 $1,605 $1,573 $1,542 $1,511

DMC Large MPV 15% 10% $1,753 $1,718 $1,684 $1,650 $1,617 $1,585 $1,553 $1,522 $1,491

DMC Large MPV 20% 15% $1,730 $1,696 $1,662 $1,628 $1,596 $1,564 $1,533 $1,502 $1,472

DMC Truck 10% 6% $1,863 $1,826 $1,790 $1,754 $1,719 $1,684 $1,651 $1,618 $1,585
 Technologies Considered in the Agencies’ Analysis

3-196 

DMC Truck 15% 11% $1,834 $1,798 $1,762 $1,726 $1,692 $1,658 $1,625 $1,592 $1,560

DMC Truck 20% 16% $1,805 $1,769 $1,733 $1,699 $1,665 $1,632 $1,599 $1,567 $1,536

IC Small car 10% 5% $926 $924 $568 $567 $566 $565 $564 $563 $562

IC Small car 15% 10% $918 $917 $563 $562 $561 $560 $559 $559 $558

IC Small car 20% 15% $911 $909 $558 $557 $556 $555 $555 $554 $553

IC Standard car 10% 5% $1,030 $1,028 $631 $630 $629 $629 $628 $627 $626

IC Standard car 15% 10% $1,019 $1,017 $624 $623 $622 $621 $620 $620 $619

IC Standard car 20% 15% $1,007 $1,005 $617 $616 $615 $614 $613 $613 $612

IC Large car 10% 5% $1,214 $1,212 $744 $743 $742 $740 $739 $738 $737

IC Large car 15% 10% $1,193 $1,191 $731 $730 $729 $728 $727 $726 $725

IC Large car 20% 15% $1,173 $1,171 $719 $718 $716 $715 $714 $713 $712

IC Small MPV 10% 5% $995 $993 $610 $609 $608 $607 $606 $605 $604

IC Small MPV 15% 10% $985 $983 $604 $603 $602 $601 $600 $599 $598

IC Small MPV 20% 15% $975 $973 $597 $596 $596 $595 $594 $593 $592

IC Large MPV 10% 5% $1,136 $1,134 $696 $695 $694 $693 $692 $691 $690

IC Large MPV 15% 10% $1,122 $1,119 $687 $686 $685 $684 $683 $682 $681

IC Large MPV 20% 15% $1,107 $1,105 $678 $677 $676 $675 $674 $673 $672

IC Truck 10% 6% $1,192 $1,190 $730 $729 $728 $727 $726 $725 $724

IC Truck 15% 11% $1,173 $1,171 $719 $718 $717 $716 $715 $714 $713

IC Truck 20% 16% $1,155 $1,152 $708 $706 $705 $704 $703 $702 $701

TC Small car 10% 5% $2,374 $2,343 $1,958 $1,929 $1,901 $1,874 $1,847 $1,820 $1,794

TC Small car 15% 10% $2,354 $2,323 $1,942 $1,913 $1,885 $1,858 $1,831 $1,805 $1,779

TC Small car 20% 15% $2,334 $2,304 $1,925 $1,897 $1,869 $1,842 $1,816 $1,790 $1,764

TC Standard car 10% 5% $2,641 $2,607 $2,178 $2,146 $2,115 $2,085 $2,054 $2,025 $1,996

TC Standard car 15% 10% $2,611 $2,577 $2,154 $2,122 $2,091 $2,061 $2,031 $2,002 $1,974

TC Standard car 20% 15% $2,582 $2,548 $2,129 $2,098 $2,067 $2,037 $2,008 $1,979 $1,951

TC Large car 10% 5% $3,112 $3,071 $2,566 $2,529 $2,492 $2,456 $2,420 $2,386 $2,352

TC Large car 15% 10% $3,059 $3,019 $2,523 $2,486 $2,450 $2,414 $2,379 $2,345 $2,312

TC Large car 20% 15% $3,006 $2,967 $2,479 $2,443 $2,408 $2,373 $2,338 $2,305 $2,272

TC Small MPV 10% 5% $2,550 $2,517 $2,103 $2,073 $2,042 $2,013 $1,984 $1,955 $1,927

TC Small MPV 15% 10% $2,525 $2,492 $2,082 $2,052 $2,022 $1,992 $1,964 $1,936 $1,908

TC Small MPV 20% 15% $2,499 $2,466 $2,061 $2,031 $2,001 $1,972 $1,944 $1,916 $1,888

TC Large MPV 10% 5% $2,912 $2,874 $2,402 $2,367 $2,332 $2,298 $2,265 $2,233 $2,201

TC Large MPV 15% 10% $2,875 $2,837 $2,371 $2,336 $2,302 $2,269 $2,236 $2,204 $2,173

TC Large MPV 20% 15% $2,837 $2,800 $2,340 $2,306 $2,272 $2,239 $2,207 $2,175 $2,144

TC Truck 10% 6% $3,056 $3,016 $2,520 $2,483 $2,447 $2,412 $2,377 $2,343 $2,309

TC Truck 15% 11% $3,008 $2,969 $2,481 $2,444 $2,409 $2,374 $2,339 $2,306 $2,273

TC Truck 20% 16% $2,960 $2,921 $2,441 $2,405 $2,370 $2,336 $2,302 $2,269 $2,237

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-106 Costs for PHEV20 Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR Net WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 3% $2,463 $2,414 $2,365 $2,318 $2,272 $2,226 $2,182 $2,138 $2,095

DMC Small car 15% 8% $2,406 $2,358 $2,311 $2,264 $2,219 $2,175 $2,131 $2,089 $2,047

DMC Small car 20% 13% $2,349 $2,302 $2,256 $2,211 $2,166 $2,123 $2,081 $2,039 $1,998

DMC Standard car 10% 3% $3,050 $2,989 $2,930 $2,871 $2,814 $2,757 $2,702 $2,648 $2,595

DMC Standard car 15% 8% $2,966 $2,906 $2,848 $2,791 $2,735 $2,681 $2,627 $2,575 $2,523

DMC Standard car 20% 13% $2,881 $2,823 $2,767 $2,712 $2,657 $2,604 $2,552 $2,501 $2,451

DMC Large car 10% 2% $4,389 $4,301 $4,215 $4,131 $4,049 $3,968 $3,888 $3,810 $3,734

DMC Large car 15% 7% $4,238 $4,153 $4,070 $3,988 $3,909 $3,831 $3,754 $3,679 $3,605

DMC Large car 20% 12% $4,086 $4,004 $3,924 $3,846 $3,769 $3,693 $3,620 $3,547 $3,476

DMC Small MPV 10% 3% $2,784 $2,728 $2,673 $2,620 $2,568 $2,516 $2,466 $2,417 $2,368
 Technologies Considered in the Agencies’ Analysis

3-197 

DMC Small MPV 15% 8% $2,712 $2,658 $2,604 $2,552 $2,501 $2,451 $2,402 $2,354 $2,307

DMC Small MPV 20% 13% $2,640 $2,587 $2,536 $2,485 $2,435 $2,386 $2,339 $2,292 $2,246

IC Small car 10% 3% $1,576 $1,572 $965 $964 $963 $961 $960 $958 $957

IC Small car 15% 8% $1,539 $1,536 $943 $942 $940 $939 $937 $936 $935

IC Small car 20% 13% $1,503 $1,500 $921 $919 $918 $917 $915 $914 $913

IC Standard car 10% 3% $1,951 $1,948 $1,196 $1,194 $1,192 $1,190 $1,189 $1,187 $1,185

IC Standard car 15% 8% $1,897 $1,893 $1,163 $1,161 $1,159 $1,157 $1,156 $1,154 $1,152

IC Standard car 20% 13% $1,843 $1,839 $1,129 $1,128 $1,126 $1,124 $1,123 $1,121 $1,119

IC Large car 10% 2% $2,808 $2,802 $1,721 $1,718 $1,715 $1,713 $1,710 $1,708 $1,705

IC Large car 15% 7% $2,711 $2,706 $1,661 $1,659 $1,656 $1,654 $1,651 $1,649 $1,646

IC Large car 20% 12% $2,614 $2,609 $1,602 $1,599 $1,597 $1,594 $1,592 $1,590 $1,587

IC Small MPV 10% 3% $1,781 $1,777 $1,091 $1,089 $1,088 $1,086 $1,085 $1,083 $1,081

IC Small MPV 15% 8% $1,735 $1,731 $1,063 $1,061 $1,060 $1,058 $1,057 $1,055 $1,054

IC Small MPV 20% 13% $1,689 $1,686 $1,035 $1,033 $1,032 $1,030 $1,029 $1,027 $1,026

TC Small car 10% 3% $4,039 $3,986 $3,331 $3,282 $3,234 $3,187 $3,141 $3,096 $3,052

TC Small car 15% 8% $3,945 $3,894 $3,254 $3,206 $3,159 $3,114 $3,069 $3,025 $2,982

TC Small car 20% 13% $3,851 $3,801 $3,177 $3,130 $3,084 $3,040 $2,996 $2,953 $2,911

TC Standard car 10% 3% $5,002 $4,937 $4,125 $4,065 $4,006 $3,948 $3,891 $3,835 $3,780

TC Standard car 15% 8% $4,863 $4,800 $4,011 $3,952 $3,894 $3,838 $3,783 $3,728 $3,675

TC Standard car 20% 13% $4,724 $4,663 $3,896 $3,839 $3,783 $3,728 $3,675 $3,622 $3,570

TC Large car 10% 2% $7,197 $7,104 $5,936 $5,849 $5,764 $5,680 $5,598 $5,518 $5,440

TC Large car 15% 7% $6,949 $6,858 $5,731 $5,647 $5,565 $5,484 $5,405 $5,328 $5,252

TC Large car 20% 12% $6,700 $6,613 $5,526 $5,445 $5,366 $5,288 $5,212 $5,137 $5,064

TC Small MPV 10% 3% $4,565 $4,505 $3,765 $3,709 $3,655 $3,602 $3,550 $3,500 $3,450

TC Small MPV 15% 8% $4,447 $4,389 $3,668 $3,614 $3,561 $3,510 $3,459 $3,409 $3,361

TC Small MPV 20% 13% $4,329 $4,273 $3,570 $3,518 $3,467 $3,417 $3,367 $3,319 $3,272

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-107 Costs for PHEV20 Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 3% $2,524 $2,474 $2,424 $2,376 $2,328 $2,282 $2,236 $2,191 $2,148

DMC Small car 15% 8% $2,464 $2,414 $2,366 $2,319 $2,272 $2,227 $2,182 $2,139 $2,096

DMC Small car 20% 13% $2,403 $2,355 $2,308 $2,262 $2,217 $2,172 $2,129 $2,086 $2,044

DMC Standard car 10% 3% $3,166 $3,102 $3,040 $2,979 $2,920 $2,861 $2,804 $2,748 $2,693

DMC Standard car 15% 8% $3,075 $3,013 $2,953 $2,894 $2,836 $2,779 $2,724 $2,669 $2,616

DMC Standard car 20% 13% $2,984 $2,924 $2,865 $2,808 $2,752 $2,697 $2,643 $2,590 $2,538

DMC Large car 10% 2% $4,574 $4,483 $4,393 $4,305 $4,219 $4,135 $4,052 $3,971 $3,892

DMC Large car 15% 7% $4,414 $4,325 $4,239 $4,154 $4,071 $3,989 $3,910 $3,832 $3,755

DMC Large car 20% 12% $4,253 $4,168 $4,084 $4,003 $3,923 $3,844 $3,767 $3,692 $3,618

DMC Small MPV 10% 3% $2,919 $2,861 $2,804 $2,748 $2,693 $2,639 $2,586 $2,534 $2,484

DMC Small MPV 15% 8% $2,841 $2,784 $2,728 $2,674 $2,620 $2,568 $2,516 $2,466 $2,417

DMC Small MPV 20% 13% $2,762 $2,707 $2,653 $2,600 $2,548 $2,497 $2,447 $2,398 $2,350

IC Small car 10% 3% $1,615 $1,612 $990 $988 $986 $985 $984 $982 $981

IC Small car 15% 8% $1,576 $1,573 $966 $964 $963 $961 $960 $959 $957

IC Small car 20% 13% $1,537 $1,534 $942 $941 $939 $938 $936 $935 $934

IC Standard car 10% 3% $2,025 $2,021 $1,241 $1,239 $1,237 $1,235 $1,233 $1,232 $1,230

IC Standard car 15% 8% $1,967 $1,963 $1,205 $1,203 $1,202 $1,200 $1,198 $1,196 $1,195

IC Standard car 20% 13% $1,909 $1,905 $1,170 $1,168 $1,166 $1,164 $1,162 $1,161 $1,159

IC Large car 10% 2% $2,926 $2,920 $1,793 $1,790 $1,788 $1,785 $1,782 $1,780 $1,777

IC Large car 15% 7% $2,824 $2,818 $1,730 $1,727 $1,725 $1,722 $1,720 $1,717 $1,715

IC Large car 20% 12% $2,721 $2,715 $1,667 $1,664 $1,662 $1,659 $1,657 $1,655 $1,652

IC Small MPV 10% 3% $1,868 $1,864 $1,144 $1,143 $1,141 $1,139 $1,137 $1,136 $1,134

IC Small MPV 15% 8% $1,817 $1,814 $1,114 $1,112 $1,110 $1,108 $1,107 $1,105 $1,104

IC Small MPV 20% 13% $1,767 $1,763 $1,083 $1,081 $1,079 $1,078 $1,076 $1,075 $1,073

TC Small car 10% 3% $4,139 $4,085 $3,414 $3,364 $3,315 $3,267 $3,220 $3,174 $3,128
 Technologies Considered in the Agencies’ Analysis

3-198 

TC Small car 15% 8% $4,040 $3,987 $3,332 $3,283 $3,235 $3,188 $3,142 $3,097 $3,053

TC Small car 20% 13% $3,940 $3,889 $3,250 $3,202 $3,156 $3,110 $3,065 $3,021 $2,978

TC Standard car 10% 3% $5,191 $5,123 $4,281 $4,218 $4,157 $4,097 $4,038 $3,980 $3,923

TC Standard car 15% 8% $5,042 $4,976 $4,158 $4,097 $4,037 $3,979 $3,922 $3,865 $3,810

TC Standard car 20% 13% $4,892 $4,829 $4,035 $3,976 $3,918 $3,861 $3,805 $3,751 $3,697

TC Large car 10% 2% $7,501 $7,403 $6,186 $6,096 $6,007 $5,920 $5,834 $5,751 $5,669

TC Large car 15% 7% $7,237 $7,143 $5,969 $5,881 $5,796 $5,712 $5,629 $5,549 $5,470

TC Large car 20% 12% $6,974 $6,883 $5,752 $5,667 $5,585 $5,504 $5,424 $5,347 $5,270

TC Small MPV 10% 3% $4,787 $4,725 $3,948 $3,890 $3,834 $3,778 $3,724 $3,670 $3,618

TC Small MPV 15% 8% $4,658 $4,597 $3,842 $3,785 $3,730 $3,676 $3,623 $3,571 $3,520

TC Small MPV 20% 13% $4,529 $4,470 $3,735 $3,681 $3,627 $3,574 $3,523 $3,472 $3,423

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-108 Costs for PHEV40 Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 15% 2% $2,474 $2,425 $2,376 $2,329 $2,282 $2,237 $2,192 $2,148 $2,105

DMC Small car 20% 7% $2,417 $2,369 $2,322 $2,275 $2,230 $2,185 $2,141 $2,099 $2,057

DMC Std car 15% 3% $3,050 $2,989 $2,930 $2,871 $2,814 $2,757 $2,702 $2,648 $2,595

DMC Std car 20% 8% $2,966 $2,906 $2,848 $2,791 $2,735 $2,681 $2,627 $2,575 $2,523

DMC Large car 15% 1% $4,420 $4,331 $4,245 $4,160 $4,076 $3,995 $3,915 $3,837 $3,760

DMC Large car 20% 6% $4,268 $4,183 $4,099 $4,017 $3,937 $3,858 $3,781 $3,705 $3,631

DMC Small MPV 15% 3% $2,784 $2,728 $2,673 $2,620 $2,568 $2,516 $2,466 $2,417 $2,368

DMC Small MPV 20% 8% $2,712 $2,658 $2,604 $2,552 $2,501 $2,451 $2,402 $2,354 $2,307

IC Small car 15% 2% $1,583 $1,580 $970 $968 $967 $966 $964 $963 $961

IC Small car 20% 7% $1,546 $1,543 $948 $946 $945 $943 $942 $940 $939

IC Std car 15% 3% $1,951 $1,948 $1,196 $1,194 $1,192 $1,190 $1,189 $1,187 $1,185

IC Std car 20% 8% $1,897 $1,893 $1,163 $1,161 $1,159 $1,157 $1,156 $1,154 $1,152

IC Large car 15% 1% $2,827 $2,822 $1,732 $1,730 $1,727 $1,725 $1,722 $1,719 $1,717

IC Large car 20% 6% $2,730 $2,725 $1,673 $1,670 $1,668 $1,665 $1,663 $1,661 $1,658

IC Small MPV 15% 3% $1,781 $1,777 $1,091 $1,089 $1,088 $1,086 $1,085 $1,083 $1,081

IC Small MPV 20% 8% $1,735 $1,731 $1,063 $1,061 $1,060 $1,058 $1,057 $1,055 $1,054

TC Small car 15% 2% $4,057 $4,005 $3,346 $3,297 $3,249 $3,202 $3,156 $3,111 $3,066

TC Small car 20% 7% $3,964 $3,912 $3,269 $3,221 $3,174 $3,128 $3,083 $3,039 $2,996

TC Std car 15% 3% $5,002 $4,937 $4,125 $4,065 $4,006 $3,948 $3,891 $3,835 $3,780

TC Std car 20% 8% $4,863 $4,800 $4,011 $3,952 $3,894 $3,838 $3,783 $3,728 $3,675

TC Large car 15% 1% $7,247 $7,153 $5,977 $5,889 $5,804 $5,719 $5,637 $5,556 $5,477

TC Large car 20% 6% $6,998 $6,907 $5,772 $5,687 $5,605 $5,523 $5,444 $5,366 $5,289

TC Small MPV 15% 3% $4,565 $4,505 $3,765 $3,709 $3,655 $3,602 $3,550 $3,500 $3,450

TC Small MPV 20% 8% $4,447 $4,389 $3,668 $3,614 $3,561 $3,510 $3,459 $3,409 $3,361

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-109 Costs for PHEV40 Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 15% 3% $2,524 $2,474 $2,424 $2,376 $2,328 $2,282 $2,236 $2,191 $2,148

DMC Small car 20% 8% $2,464 $2,414 $2,366 $2,319 $2,272 $2,227 $2,182 $2,139 $2,096

DMC Std car 15% 3% $3,166 $3,102 $3,040 $2,979 $2,920 $2,861 $2,804 $2,748 $2,693

DMC Std car 20% 8% $3,075 $3,013 $2,953 $2,894 $2,836 $2,779 $2,724 $2,669 $2,616

DMC Large car 15% 2% $4,574 $4,483 $4,393 $4,305 $4,219 $4,135 $4,052 $3,971 $3,892

DMC Large car 20% 7% $4,414 $4,325 $4,239 $4,154 $4,071 $3,989 $3,910 $3,832 $3,755

DMC Small MPV 15% 3% $2,919 $2,861 $2,804 $2,748 $2,693 $2,639 $2,586 $2,534 $2,484

DMC Small MPV 20% 8% $2,841 $2,784 $2,728 $2,674 $2,620 $2,568 $2,516 $2,466 $2,417
 Technologies Considered in the Agencies’ Analysis

3-199 

IC Small car 15% 3% $1,615 $1,612 $990 $988 $986 $985 $984 $982 $981

IC Small car 20% 8% $1,576 $1,573 $966 $964 $963 $961 $960 $959 $957

IC Std car 15% 3% $2,025 $2,021 $1,241 $1,239 $1,237 $1,235 $1,233 $1,232 $1,230

IC Std car 20% 8% $1,967 $1,963 $1,205 $1,203 $1,202 $1,200 $1,198 $1,196 $1,195

IC Large car 15% 2% $2,926 $2,920 $1,793 $1,790 $1,788 $1,785 $1,782 $1,780 $1,777

IC Large car 20% 7% $2,824 $2,818 $1,730 $1,727 $1,725 $1,722 $1,720 $1,717 $1,715

IC Small MPV 15% 3% $1,868 $1,864 $1,144 $1,143 $1,141 $1,139 $1,137 $1,136 $1,134

IC Small MPV 20% 8% $1,817 $1,814 $1,114 $1,112 $1,110 $1,108 $1,107 $1,105 $1,104

TC Small car 15% 3% $4,139 $4,085 $3,414 $3,364 $3,315 $3,267 $3,220 $3,174 $3,128

TC Small car 20% 8% $4,040 $3,987 $3,332 $3,283 $3,235 $3,188 $3,142 $3,097 $3,053

TC Std car 15% 3% $5,191 $5,123 $4,281 $4,218 $4,157 $4,097 $4,038 $3,980 $3,923

TC Std car 20% 8% $5,042 $4,976 $4,158 $4,097 $4,037 $3,979 $3,922 $3,865 $3,810

TC Large car 15% 2% $7,501 $7,403 $6,186 $6,096 $6,007 $5,920 $5,834 $5,751 $5,669

TC Large car 20% 7% $7,237 $7,143 $5,969 $5,881 $5,796 $5,712 $5,629 $5,549 $5,470

TC Small MPV 15% 3% $4,787 $4,725 $3,948 $3,890 $3,834 $3,778 $3,724 $3,670 $3,618

TC Small MPV 20% 8% $4,658 $4,597 $3,842 $3,785 $3,730 $3,676 $3,623 $3,571 $3,520

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-110 Costs for EV75 Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 10% $199 $193 $187 $182 $176 $171 $168 $164 $161

DMC Small car 15% 15% $124 $120 $116 $113 $109 $106 $104 $102 $100

DMC Small car 20% 20% $48 $47 $45 $44 $43 $41 $40 $40 $39

DMC Std car 10% 10% $921 $893 $866 $840 $815 $791 $775 $759 $744

DMC Std car 15% 15% $809 $784 $761 $738 $716 $694 $681 $667 $654

DMC Std car 20% 20% $697 $676 $655 $636 $617 $598 $586 $574 $563

DMC Large car 10% 10% $1,659 $1,609 $1,560 $1,514 $1,468 $1,424 $1,396 $1,368 $1,340

DMC Large car 15% 15% $1,458 $1,414 $1,372 $1,331 $1,291 $1,252 $1,227 $1,202 $1,178

DMC Large car 20% 20% $1,257 $1,220 $1,183 $1,148 $1,113 $1,080 $1,058 $1,037 $1,016

DMC Small MPV 10% 9% -$183 -$177 -$172 -$167 -$162 -$157 -$154 -$151 -$148

DMC Small MPV 15% 14% -$278 -$269 -$261 -$254 -$246 -$239 -$234 -$229 -$225

DMC Small MPV 20% 19% -$373 -$362 -$351 -$340 -$330 -$320 -$314 -$307 -$301

IC Small car 10% 10% $153 $153 $152 $152 $152 $151 $151 $151 $97

IC Small car 15% 15% $95 $95 $95 $94 $94 $94 $94 $94 $60

IC Small car 20% 20% $37 $37 $37 $37 $37 $37 $36 $36 $23

IC Std car 10% 10% $709 $707 $705 $703 $701 $699 $698 $697 $449

IC Std car 15% 15% $623 $621 $619 $618 $616 $614 $613 $612 $394

IC Std car 20% 20% $536 $535 $533 $532 $531 $529 $528 $527 $339

IC Large car 10% 10% $1,277 $1,273 $1,270 $1,266 $1,263 $1,260 $1,258 $1,256 $808

IC Large car 15% 15% $1,123 $1,119 $1,116 $1,113 $1,110 $1,107 $1,106 $1,104 $710

IC Large car 20% 20% $968 $965 $963 $960 $958 $955 $954 $952 $613

IC Small MPV 10% 9% -$141 -$140 -$140 -$140 -$139 -$139 -$139 -$138 -$89

IC Small MPV 15% 14% -$214 -$213 -$213 -$212 -$212 -$211 -$211 -$210 -$135

IC Small MPV 20% 19% -$287 -$286 -$285 -$285 -$284 -$283 -$283 -$282 -$182

TC Small car 10% 10% $352 $346 $340 $334 $328 $322 $319 $315 $258

TC Small car 15% 15% $219 $215 $211 $207 $204 $200 $198 $195 $160

TC Small car 20% 20% $85 $84 $82 $81 $79 $78 $77 $76 $62

TC Std car 10% 10% $1,630 $1,600 $1,571 $1,543 $1,516 $1,490 $1,473 $1,457 $1,193

TC Std car 15% 15% $1,431 $1,405 $1,380 $1,356 $1,332 $1,309 $1,294 $1,279 $1,048

TC Std car 20% 20% $1,233 $1,211 $1,189 $1,168 $1,147 $1,127 $1,114 $1,102 $902

TC Large car 10% 10% $2,936 $2,882 $2,830 $2,780 $2,731 $2,684 $2,654 $2,624 $2,149

TC Large car 15% 15% $2,581 $2,534 $2,488 $2,444 $2,401 $2,359 $2,333 $2,306 $1,889

TC Large car 20% 20% $2,226 $2,185 $2,146 $2,108 $2,071 $2,035 $2,012 $1,989 $1,629

TC Small MPV 10% 9% -$324 -$318 -$312 -$306 -$301 -$296 -$293 -$289 -$237

TC Small MPV 15% 14% -$492 -$483 -$474 -$466 -$458 -$450 -$444 -$439 -$360
 Technologies Considered in the Agencies’ Analysis

3-200 

TC Small MPV 20% 19% -$660 -$648 -$636 -$625 -$614 -$603 -$596 -$590 -$483

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-111 Costs for EV75 Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 10% $250 $242 $235 $228 $221 $215 $210 $206 $202

DMC Small car 15% 15% $172 $166 $161 $157 $152 $147 $144 $142 $139

DMC Small car 20% 20% $93 $91 $88 $85 $83 $80 $79 $77 $75

DMC Std car 10% 9% $1,043 $1,012 $982 $952 $924 $896 $878 $861 $843

DMC Std car 15% 14% $926 $898 $871 $845 $820 $795 $779 $764 $748

DMC Std car 20% 19% $808 $784 $761 $738 $716 $694 $680 $667 $653

DMC Large car 10% 10% $1,799 $1,745 $1,693 $1,642 $1,593 $1,545 $1,514 $1,484 $1,454

DMC Large car 15% 15% $1,592 $1,544 $1,498 $1,453 $1,409 $1,367 $1,340 $1,313 $1,287

DMC Large car 20% 20% $1,385 $1,343 $1,303 $1,264 $1,226 $1,189 $1,165 $1,142 $1,119

DMC Small MPV 10% 9% -$51 -$49 -$48 -$46 -$45 -$44 -$43 -$42 -$41

DMC Small MPV 15% 14% -$152 -$148 -$143 -$139 -$135 -$131 -$128 -$126 -$123

DMC Small MPV 20% 19% -$254 -$246 -$239 -$232 -$225 -$218 -$214 -$209 -$205

IC Small car 10% 10% $192 $192 $191 $191 $190 $190 $189 $189 $122

IC Small car 15% 15% $132 $132 $131 $131 $131 $130 $130 $130 $84

IC Small car 20% 20% $72 $72 $72 $71 $71 $71 $71 $71 $46

IC Std car 10% 9% $803 $801 $799 $797 $795 $793 $791 $790 $508

IC Std car 15% 14% $713 $711 $709 $707 $705 $703 $702 $701 $451

IC Std car 20% 19% $623 $621 $619 $617 $616 $614 $613 $612 $394

IC Large car 10% 10% $1,386 $1,382 $1,378 $1,374 $1,370 $1,367 $1,365 $1,362 $877

IC Large car 15% 15% $1,226 $1,222 $1,219 $1,216 $1,212 $1,209 $1,207 $1,205 $776

IC Large car 20% 20% $1,066 $1,063 $1,060 $1,057 $1,054 $1,052 $1,050 $1,048 $675

IC Small MPV 10% 9% -$39 -$39 -$39 -$39 -$39 -$38 -$38 -$38 -$25

IC Small MPV 15% 14% -$117 -$117 -$117 -$116 -$116 -$116 -$115 -$115 -$74

IC Small MPV 20% 19% -$195 -$195 -$194 -$194 -$193 -$193 -$193 -$192 -$124

TC Small car 10% 10% $442 $434 $426 $419 $412 $404 $400 $395 $324

TC Small car 15% 15% $304 $298 $293 $288 $283 $278 $275 $271 $222

TC Small car 20% 20% $165 $162 $159 $157 $154 $151 $149 $148 $121

TC Std car 10% 9% $1,847 $1,813 $1,781 $1,749 $1,718 $1,689 $1,669 $1,651 $1,352

TC Std car 15% 14% $1,639 $1,609 $1,580 $1,552 $1,525 $1,498 $1,481 $1,465 $1,199

TC Std car 20% 19% $1,431 $1,405 $1,380 $1,355 $1,331 $1,308 $1,293 $1,279 $1,047

TC Large car 10% 10% $3,185 $3,127 $3,071 $3,016 $2,963 $2,912 $2,879 $2,846 $2,331

TC Large car 15% 15% $2,818 $2,767 $2,717 $2,669 $2,622 $2,576 $2,547 $2,518 $2,062

TC Large car 20% 20% $2,451 $2,406 $2,363 $2,321 $2,280 $2,241 $2,215 $2,190 $1,794

TC Small MPV 10% 9% -$90 -$88 -$86 -$85 -$83 -$82 -$81 -$80 -$66

TC Small MPV 15% 14% -$270 -$265 -$260 -$255 -$251 -$246 -$244 -$241 -$197

TC Small MPV 20% 19% -$449 -$441 -$433 -$426 -$418 -$411 -$406 -$402 -$329

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-112 Costs for EV100 Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type

EPA

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 4% $290 $281 $273 $264 $256 $249 $244 $239 $234

DMC Small car 15% 9% $214 $208 $202 $195 $190 $184 $180 $177 $173

DMC Small car 20% 14% $139 $135 $130 $127 $123 $119 $117 $114 $112

DMC Std car 10% 4% $1,055 $1,024 $993 $963 $934 $906 $888 $870 $853

DMC Std car 15% 9% $943 $915 $887 $861 $835 $810 $794 $778 $762
 Technologies Considered in the Agencies’ Analysis

3-201 

DMC Std car 20% 14% $831 $806 $782 $759 $736 $714 $699 $685 $672

DMC Large car 10% 5% $1,859 $1,803 $1,749 $1,697 $1,646 $1,596 $1,565 $1,533 $1,503

DMC Large car 15% 10% $1,659 $1,609 $1,560 $1,514 $1,468 $1,424 $1,396 $1,368 $1,340

DMC Large car 20% 15% $1,458 $1,414 $1,372 $1,331 $1,291 $1,252 $1,227 $1,202 $1,178

DMC Small MPV 10% 3% -$69 -$67 -$65 -$63 -$61 -$59 -$58 -$57 -$56

DMC Small MPV 15% 8% -$164 -$159 -$154 -$150 -$145 -$141 -$138 -$135 -$132

DMC Small MPV 20% 13% -$259 -$251 -$244 -$236 -$229 -$222 -$218 -$213 -$209

IC Small car 10% 4% $223 $222 $222 $221 $221 $220 $220 $219 $141

IC Small car 15% 9% $165 $164 $164 $164 $163 $163 $162 $162 $104

IC Small car 20% 14% $107 $106 $106 $106 $106 $105 $105 $105 $68

IC Std car 10% 4% $813 $810 $808 $806 $804 $802 $800 $799 $514

IC Std car 15% 9% $726 $724 $722 $720 $718 $716 $715 $714 $460

IC Std car 20% 14% $640 $638 $636 $635 $633 $631 $630 $629 $405

IC Large car 10% 5% $1,432 $1,427 $1,423 $1,420 $1,416 $1,412 $1,410 $1,408 $906

IC Large car 15% 10% $1,277 $1,273 $1,270 $1,266 $1,263 $1,260 $1,258 $1,256 $808

IC Large car 20% 15% $1,123 $1,119 $1,116 $1,113 $1,110 $1,107 $1,106 $1,104 $710

IC Small MPV 10% 3% -$53 -$53 -$53 -$53 -$52 -$52 -$52 -$52 -$34

IC Small MPV 15% 8% -$126 -$126 -$125 -$125 -$125 -$124 -$124 -$124 -$80

IC Small MPV 20% 13% -$199 -$199 -$198 -$198 -$197 -$197 -$196 -$196 -$126

TC Small car 10% 4% $513 $503 $494 $486 $477 $469 $464 $458 $375

TC Small car 15% 9% $379 $372 $366 $359 $353 $347 $343 $339 $277

TC Small car 20% 14% $245 $241 $237 $232 $228 $224 $222 $219 $180

TC Std car 10% 4% $1,868 $1,834 $1,801 $1,769 $1,738 $1,708 $1,688 $1,669 $1,367

TC Std car 15% 9% $1,669 $1,639 $1,610 $1,581 $1,553 $1,526 $1,509 $1,492 $1,222

TC Std car 20% 14% $1,471 $1,444 $1,418 $1,393 $1,369 $1,345 $1,330 $1,315 $1,077

TC Large car 10% 5% $3,291 $3,231 $3,173 $3,116 $3,062 $3,009 $2,974 $2,941 $2,408

TC Large car 15% 10% $2,936 $2,882 $2,830 $2,780 $2,731 $2,684 $2,654 $2,624 $2,149

TC Large car 20% 15% $2,581 $2,534 $2,488 $2,444 $2,401 $2,359 $2,333 $2,306 $1,889

TC Small MPV 10% 3% -$122 -$120 -$117 -$115 -$113 -$111 -$110 -$109 -$89

TC Small MPV 15% 8% -$290 -$285 -$280 -$275 -$270 -$265 -$262 -$259 -$212

TC Small MPV 20% 13% -$458 -$450 -$442 -$434 -$426 -$419 -$414 -$409 -$335

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-113 Costs for EV100 Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

EPA

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 10% 4% $344 $333 $323 $314 $304 $295 $289 $284 $278

DMC Small car 15% 9% $266 $258 $250 $242 $235 $228 $223 $219 $215

DMC Small car 20% 14% $187 $182 $176 $171 $166 $161 $158 $154 $151

DMC Std car 10% 3% $1,184 $1,149 $1,114 $1,081 $1,049 $1,017 $997 $977 $957

DMC Std car 15% 8% $1,067 $1,035 $1,004 $974 $945 $916 $898 $880 $862

DMC Std car 20% 13% $949 $921 $893 $866 $841 $815 $799 $783 $767

DMC Large car 10% 4% $2,048 $1,987 $1,927 $1,869 $1,813 $1,759 $1,724 $1,689 $1,656

DMC Large car 15% 9% $1,841 $1,786 $1,732 $1,680 $1,630 $1,581 $1,549 $1,518 $1,488

DMC Large car 20% 14% $1,633 $1,584 $1,537 $1,491 $1,446 $1,403 $1,375 $1,347 $1,320

DMC Small MPV 10% 3% $71 $69 $67 $65 $63 $61 $60 $59 $58

DMC Small MPV 15% 8% -$30 -$29 -$29 -$28 -$27 -$26 -$26 -$25 -$25

DMC Small MPV 20% 13% -$132 -$128 -$124 -$120 -$117 -$113 -$111 -$109 -$107

IC Small car 10% 4% $265 $264 $263 $262 $262 $261 $261 $260 $167

IC Small car 15% 9% $204 $204 $203 $203 $202 $202 $201 $201 $129

IC Small car 20% 14% $144 $144 $143 $143 $143 $142 $142 $142 $91

IC Std car 10% 3% $912 $909 $907 $904 $902 $900 $898 $897 $577

IC Std car 15% 8% $822 $819 $817 $815 $813 $810 $809 $808 $520

IC Std car 20% 13% $731 $729 $727 $725 $723 $721 $720 $719 $463

IC Large car 10% 4% $1,577 $1,573 $1,568 $1,564 $1,560 $1,556 $1,553 $1,551 $998
 Technologies Considered in the Agencies’ Analysis

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IC Large car 15% 9% $1,418 $1,413 $1,410 $1,406 $1,402 $1,398 $1,396 $1,394 $897

IC Large car 20% 14% $1,258 $1,254 $1,251 $1,247 $1,244 $1,241 $1,239 $1,237 $796

IC Small MPV 10% 3% $55 $55 $55 $54 $54 $54 $54 $54 $35

IC Small MPV 15% 8% -$23 -$23 -$23 -$23 -$23 -$23 -$23 -$23 -$15

IC Small MPV 20% 13% -$102 -$101 -$101 -$101 -$100 -$100 -$100 -$100 -$64

TC Small car 10% 4% $608 $597 $587 $576 $566 $556 $550 $544 $445

TC Small car 15% 9% $470 $461 $453 $445 $437 $430 $425 $420 $344

TC Small car 20% 14% $331 $325 $320 $314 $308 $303 $300 $296 $243

TC Std car 10% 3% $2,096 $2,058 $2,021 $1,985 $1,951 $1,917 $1,895 $1,874 $1,534

TC Std car 15% 8% $1,888 $1,854 $1,821 $1,788 $1,757 $1,727 $1,707 $1,688 $1,382

TC Std car 20% 13% $1,680 $1,650 $1,620 $1,591 $1,564 $1,536 $1,519 $1,502 $1,230

TC Large car 10% 4% $3,626 $3,560 $3,496 $3,434 $3,373 $3,315 $3,277 $3,240 $2,654

TC Large car 15% 9% $3,258 $3,199 $3,142 $3,086 $3,032 $2,979 $2,945 $2,912 $2,385

TC Large car 20% 14% $2,891 $2,839 $2,788 $2,738 $2,690 $2,644 $2,613 $2,584 $2,116

TC Small MPV 10% 3% $126 $124 $122 $119 $117 $115 $114 $113 $92

TC Small MPV 15% 8% -$54 -$53 -$52 -$51 -$50 -$49 -$49 -$48 -$39

TC Small MPV 20% 13% -$234 -$229 -$225 -$221 -$217 -$214 -$211 -$209 -$171

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-114 Costs for EV150 Non-Battery Components for the 2008 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 20% 2% $321 $312 $302 $293 $284 $276 $270 $265 $260

DMC Std car 20% 2% $1,101 $1,068 $1,036 $1,005 $975 $945 $927 $908 $890

DMC Large car 20% 3% $1,941 $1,882 $1,826 $1,771 $1,718 $1,667 $1,633 $1,601 $1,569

DMC Small MPV 20% 1% -$30 -$29 -$29 -$28 -$27 -$26 -$26 -$25 -$25

IC Small car 20% 2% $247 $247 $246 $245 $245 $244 $244 $243 $157

IC Std car 20% 2% $848 $845 $843 $841 $838 $836 $835 $834 $536

IC Large car 20% 3% $1,494 $1,490 $1,486 $1,482 $1,478 $1,474 $1,472 $1,469 $946

IC Small MPV 20% 1% -$23 -$23 -$23 -$23 -$23 -$23 -$23 -$23 -$15

TC Small car 20% 2% $569 $558 $548 $538 $529 $520 $514 $508 $416

TC Std car 20% 2% $1,949 $1,913 $1,879 $1,846 $1,813 $1,782 $1,761 $1,742 $1,426

TC Large car 20% 3% $3,435 $3,373 $3,312 $3,253 $3,196 $3,141 $3,105 $3,070 $2,514

TC Small MPV 20% 1% -$54 -$53 -$52 -$51 -$50 -$49 -$49 -$48 -$39

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 3-115 Costs for EV150 Non-Battery Components for the 2010 Baseline (2010$) 

Cost 

type

Vehicle 

class

Applied 

WR

Net 

WR 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Small car 20% 1% $391 $379 $368 $357 $346 $336 $329 $322 $316

DMC Std car 20% 1% $1,231 $1,194 $1,159 $1,124 $1,090 $1,057 $1,036 $1,016 $995

DMC Large car 20% 3% $2,090 $2,027 $1,966 $1,907 $1,850 $1,795 $1,759 $1,724 $1,689

DMC Small MPV 20% 1% $112 $108 $105 $102 $99 $96 $94 $92 $90

IC Small car 20% 1% $301 $300 $299 $298 $298 $297 $296 $296 $190

IC Std car 20% 1% $948 $945 $943 $940 $938 $935 $934 $932 $600

IC Large car 20% 3% $1,609 $1,605 $1,600 $1,596 $1,592 $1,587 $1,585 $1,582 $1,018

IC Small MPV 20% 1% $86 $86 $86 $85 $85 $85 $85 $85 $54

TC Small car 20% 1% $692 $679 $667 $655 $643 $632 $625 $618 $506

TC Std car 20% 1% $2,180 $2,140 $2,101 $2,064 $2,028 $1,993 $1,970 $1,948 $1,595

TC Large car 20% 3% $3,699 $3,632 $3,566 $3,503 $3,442 $3,382 $3,344 $3,306 $2,707

TC Small MPV 20% 1% $198 $194 $191 $187 $184 $181 $179 $177 $145

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

For Mild HEV non-battery components, the agencies have used a combination of cost 

sources which include the FEV teardown of a Saturn Vue along with estimates used for P2 
 Technologies Considered in the Agencies’ Analysis

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HEVs as described above. For the electrical power distribution and control system and the 

DC-DC converter, estimates presented in the NPRM for subcompacts were used with a 

presumed 20% weight reduction because those systems were estimated to include a 16 kW 

motor (essentially the same as the 15 kW motor assumed for the Mild HEV technology). 

These costs and the FEV Saturn Vue teardown costs we used are shown in Table 3-116. 

Table 3-116 FEV Teardown Results & P2 HEV Values used for MHEV Non-Battery Direct 

Manufacturing Cost Estimates 

System Teardown result

(2007$)

P2 HEV

(2009$)a

2010$

Cooling subsystem 

(including water pumps)

$88.71 $92.37

Accessory drive 

subsystem

$30.75 $32.02

Body system $14.83 $15.44

Brake system $42.30 $44.05

Climate control system $0 $0

Transmission oil pump 

and filter subsystem

$53.86 $56.09

Generator/alternator and 

regulatory subsystem

$51.94 $54.09

Electrical power 

distribution & control system

$203.22 $205.25

DC-DC converter $115.33 $116.48

Total $615.79

a

See the draft Joint TSD, Table 3-80, 20% WR (EPA-420-D-11-901, 

November 2011).

For Mild HEV non-battery components, the direct manufacturing costs shown in 

Table 3-116 are considered applicable MY 2012. The agencies consider the Mild HEV nonbattery component technologies to be on the flat portion of the learning curve during the 

2017-2025 timeframe. The agencies have applied a medium complexity ICM of 1.39 through 

2018 then 1.29 thereafter. The resultant costs used in this final analysis are shown in Table 

3-117. 

Table 3-117 Costs for Mild HEV Non-Battery Components for both the 2008 and 2010 Baselines (2010$) 

Cost type Vehicle class 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC All $534 $524 $513 $503 $493 $483 $473 $464 $455

IC All $235 $234 $175 $175 $175 $174 $174 $174 $173

TC All $769 $758 $688 $678 $667 $657 $647 $637 $628

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.4 Hardware costs for charging grid-connected vehicles 

Grid-connected vehicles such as EVs and PHEVs require a means to charge their onboard batteries to enable their electric range capabilities. These vehicles require certain 

hardware to charge, both on-vehicle and off-vehicle. The agencies’ September 2010 Technical 
 Technologies Considered in the Agencies’ Analysis

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Assessment Report contains an in-depth analysis of the topic of charging and infrastructure. 

The TAR analysis and assumptions did not receive any significant comment, and a review of 

the current state of the industry indicates the assumptions in the TAR are still valid. 

Therefore, the assumptions for the cost of Electric Vehicle Support Equipment (EVSE) are 

unchanged. Additionally, while some of the characteristics of the modeled grid-connected 

vehicles such as battery size and energy demand have changed somewhat due to further 

analysis, the application of Level 1 and Level 2 charging by vehicle type based on charge time 

has not changed. 

Three charging levels are currently under consideration. Level 1 charging uses a 

standard 120 volt (V), 15-20 amps (A) rated (12-16 A usable) circuit and is available in 

standard residential and commercial buildings. Level 2 charging uses a single phase, 240 V, 

20-80 A circuit and allows much shorter charge times. Level 3 charging—sometimes 

colloquially called “quick” or “fast” charging—uses a 480 V, three-phase circuit, available in 

mainly industrial areas, typically providing 60-150 kW of off-board charging power. It is 

expected that 97 to 99% of charging will take place at home, so a cost for a home charger, 

appropriate to the duty cycle of the vehicle, is added to the vehicle cost. Level 3 charging is 

available to commercial users and vehicles that charge at Level 3 stations will be assumed to 

pay at the charge station for the convenience of fast charging. Therefore Level 3 charger 

costs are not included in overall vehicle cost. 

The specific equipment required for charging a grid-connected vehicle consists of the 

following: 

Charger: A charger that converts electricity from alternating current (AC) from the 

electricity source to direct current (DC) required for the battery, and also converts the 

incoming 120 or 240 volt current to 300 or higher volts. Grid-connected vehicles carry an onboard charger capable of accepting AC current from a wall plug (Level 1 circuit) or, from a 

Level 2 charging station. On-board charger power capability ranges from 1.4 to 10 kW and is 

usually proportional to the vehicle’s battery capacity. The lowest charging power, 1.4 kW, is 

expected only when grid-connected vehicles are connected to 120 volt (Level 1) outlets, and 

all currently known PHEV and EV on-board chargers are expected to provide at least 3.3 kW 

charging when connected to a Level 2 (220 volt, 20+ A) charging station. The latest SAE 

connection recommended practice, J1772, allows for delivery of up to ~19 kW to an on-board 

vehicle charger. For higher capacity charging under Level 3, a charging station that delivers 

DC current directly to the vehicle’s battery is incorporated off-board in the wall or pedestal 

mounted. 

Charging Station: The charging station needed to safely deliver energy from the 

electric circuit to the vehicle, called electric vehicle support equipment (EVSE). The EVSE 

may at a minimum, be a specialized cordset that connects a household Level 1/120V socket to 

the vehicle; otherwise, the EVSE will include a cordset and a charging station (a wall or 

pedestal mounted box incorporating a charger and other equipment). Charging stations may 

include optional advanced features such as timers to delay charging until off-peak hours, 

communications equipment to allow the utility to regulate charging, or even electricity 

metering capabilities. Stakeholders are working on which features are best located on the 

EVSE or on the vehicle itself, and it is possible that redundant capabilities and features may 
 Technologies Considered in the Agencies’ Analysis

3-205 

be present in both the vehicle and EVSEs in the near future until these issues are worked out. 

EVSE and vehicle manufacturers are also working to ensure that current SAE-compliant 

“basic” EVSEs are charge-compatible with future grid-connected vehicles. 

Dedicated Circuit: A Level 1 circuit is standard household current, 120V AC, rated at 

15 or 20 A (12 or 16 A usable). A Level 2 circuit is rated at 208 to 240V and up to 80 A and 

is similar to the type of circuit that powers electric stoves (up to 50 A) and dryers (usually 30 

A). Generally, Level 1 and 2 circuits used for electric vehicle recharging must be dedicated 

circuits, i.e., there cannot be other appliances on that circuit. For a Level 2 circuit, the 

homeowner or other user must install a charging station and will need a permit. A homeowner 

may choose to install the charger on a separately-metered circuit to take advantage of special 

electrical rates for off-peak charging, where available. 

In addition to the costs of purchasing and installing charging equipment, charging 

station installation may include the costs of upgrading existing electrical panels and installing 

the electrical connection from the panel to the desired station location. These costs may be 

dramatically lowered if new construction incorporates the panel box and wiring required for 

charging stations, or even includes charging stations or outlets for charging stations as 

standard equipment. 

The current costs of charging stations are highly variable depending on the level of 

service (and alternative power capabilities within these categories), location (individual 

residence, grouped residences, retail or business, parking lot or garage), level of sophistication 

of the station, and installation requirements, including electrical upgrading requirements. 

Estimated costs for charging stations are included in Table 3-118 below. 

Table 3-118: Estimated Costs for Charging Stations Used in the 2010 TAR (2008$) 

Level Location Equipment Installation

1 Single 

Residence

$30- $200 (charge cord only, included 

at no cost to consumer with EV/PHEV) 

when an accessible household plug 

(e.g., in a garage or adjacent to a 

driveway) with a ground fault 

interrupter is already available

$400-$1000+ may be necessary 

depending on difficulty of installing a 

new circuit at the desired location, but 

in most cases, owners with sufficient 

panel capacity would opt for a more 

capable 220 VAC Level 2 installation 

instead of a Level 1 dedicated circuit 

because the additional installation cost 

is only marginally higher
 Technologies Considered in the Agencies’ Analysis

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2 Residential, 

Apartment 

Complex, or 

Fleet Depotb

3.3 kW EVSE (each): $300- $4,000 

6.6 kW EVSE (each): $400- $4,000

3.3- 6.6 kW installation cost: 

$400-$2,300 without wiring/service 

panel upgrade, or

$2,000-$5,000 with panel upgrade

refs: 77,78,79,80,a

a

 Detailed information on charger cost for each charging level and location and specific sources for cost 

estimates are available in the TAR, Appendix G. 

b Level 2 EVSE installation costs vary considerably for single-family residences, multi-family residences, and 

fleet depots, depending upon the need for wiring and service panel upgrades. The range depicted here reflects 

the anticipated variability of these costs. However, EPRI estimates that the typical residential Level 2 

installation costs to be approximately $1,500. See the TAR, Appendix G for additional information. 

3.4.4.1 Application of charging level by vehicle type 

The home charging availability for a specific consumer will need to be differentiated 

among EV/PHEVs with different battery capacity. The electric outlets in existing homes are 

most likely ready for Level 1 charging, which is about sufficient for fully recharging a 

PHEV20 SUV during normal nighttime, provided the outlet is not being heavily utilized by 

other loads. Shorter available charging time or owning a PHEV or an EV with a larger 

battery make the capability to fully charge overnight with a Level 1 system less likely, but 

upgrading to a Level 2 system in such cases will allow full recharge to happen more quickly. 

Table 3-119 shows the application of charge level by vehicle type and range. 

Charging types were chosen based on nominal time to charge a fully-depleted battery in a 

vehicle with 0% net weight reduction. Charge times exceeding 9 hours for Level 1 were 

deemed unacceptable and Level 2 charging was specified. For charge times between 6 hours 

and 9 hours on Level 1, a mix of Level 1 and Level 2 was specified. This was done to 

recognize the varying consumer value of faster, but more expensive, Level 2 charging over 

Level 1 charging. 

Table 3-119: Charger Type by Vehicle Technology and Class 

EPA Vehicle 

Class PHEV20 PHEV40 EV75 EV100 EV150

Small car 100% L1 25% L1

75% L2 100% L2 100% L2 100% L2

Standard Car 100% L1 10% L1

90% L2 100% L2 100% L2 100% L2

Large Car 100% L1 100% L2 100% L2 100% L2 100% L2

Small MPV 100% L1 100% L2 100% L2 100% L2 100% L2

Large MPV 100% L1 100% L2 100% L2 100% L2 100% L2

Truck 50% L1

50% L2 100% L2 100% L2 100% L2 100% L2
 Technologies Considered in the Agencies’ Analysis

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For this final rule, consistent with the proposal, the resultant costs associated with inhome chargers and installation of in-home chargers are included in the total cost for an EV 

and or PHEV. However, here we summarize specially the costs for chargers and installation 

labor. The agencies have estimated the DMC of a level 1 charge cord at $31 (2010$) based 

on typical costs of similar electrical equipment sold to consumers today and that for a level 2 

charger at $204 (2010$). Labor associated with installing either of these chargers is estimated 

at $1,020 (2010$). Further, we have estimated that all PHEV20 vehicles (PHEVs with a 20 

mile range) would be charged via a level 1 charger and that all EVs, regardless of range, 

would be charged via a level 2 charger. For the PHEV40 vehicles (PHEVs with a 40 mile 

range), we have estimated that: 25% of small cars would be charged with a level 1 charger 

with the remainder charged via a level 2 charger; 10% of standard cars would be charged with 

a level 1 charger with the remainder charged via a level 2 charger; and all remaining PHEV 

40 vehicles would be charged via a level 2 charger. All costs presented here are considered 

applicable in the 2025 model year. The agencies have applied the learning curve presented in 

Section 3.2.3 to all charger costs. The agencies have also applied a High1 ICM of 1.56 

through 2024 then 1.34 thereafter. Installation costs, being labor costs, have no learning 

impacts or ICMs applied. The resultant costs are shown in Table 3-120. 

Table 3-120 Costs for EV/PHEV In-home Chargers (2010$) 

Cost 

type

Technology EPA 

Vehicle 

Class

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC PHEV20 

Charger All $60 $48 $48 $38 $38 $38 $38 $38 $31

DMC PHEV40 

Charger

Small car $314 $251 $251 $201 $201 $201 $201 $201 $161

Std car $365 $292 $292 $233 $233 $233 $233 $233 $187

Large car

Small 

MPV

$398 $319 $319 $255 $255 $255 $255 $255 $204

DMC EV Charger All $398 $319 $319 $255 $255 $255 $255 $255 $204

IC PHEV20 

Charger All $19 $18 $18 $18 $18 $18 $18 $18 $11

IC PHEV40 

Charger

Small car $100 $96 $96 $93 $93 $93 $93 $93 $55

Std car $117 $112 $112 $108 $108 $108 $108 $108 $64

Large car

Small 

MPV

$128 $122 $122 $118 $118 $118 $118 $118 $70

IC EV Charger All $128 $122 $122 $118 $118 $118 $118 $118 $70

TC PHEV20 

Charger All $79 $66 $66 $56 $56 $56 $56 $56 $41

TC PHEV40 

Charger

Small car $414 $347 $347 $294 $294 $294 $294 $294 $216

Std car $481 $404 $404 $342 $342 $342 $342 $342 $251

Large car

Small 

MPV

$526 $441 $441 $373 $373 $373 $373 $373 $274

TC EV Charger All $526 $441 $441 $373 $373 $373 $373 $373 $274

TC Charger 

labor All $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $1,020 $1,020

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost
 Technologies Considered in the Agencies’ Analysis

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3.4.5 Other Technologies Assessed that Reduce CO2 and Improve Fuel Economy 

In addition to the technologies already mentioned above, the other technologies 

generally considered in the agencies’ analysis are described below. They fall into five broad 

categories: engine technologies, transmission technologies, vehicle technologies, 

electrification/accessory technologies, hybrid technologies and mass reduction 

3.4.5.1 Lower Rolling Resistance Tires 

Tire rolling resistance is the frictional loss associated mainly with the energy 

dissipated in the deformation of the tires under load and thus influences fuel economy and 

CO2 emissions. Other tire design characteristics (e.g., materials, construction, and tread 

design) influence durability, traction (both wet and dry grip), vehicle handling, and ride 

comfort in addition to rolling resistance. A typical low rolling resistance tire’s attributes 

could include: increased specified tire inflation pressure, material changes, and tire 

construction with less hysteresis, geometry changes (e.g., reduced aspect ratios), and 

reduction in sidewall and tread deflection. These changes would generally be accompanied 

with additional changes to vehicle suspension tuning and/or suspension design. 

The agencies expect that greater reductions in tire rolling resistance will be possible 

during the rulemaking timeframe than are currently available, as tire manufacturers continue 

to improve their products in order to meet increasing demand by auto OEMs for tires that 

contribute more to their vehicles’ fuel efficiency. Thus, for this final rule, consistent with the 

proposal, the agencies considered two “levels” of lower rolling resistance tires. The first level 

(“LRR1”) is defined as a 10 percent reduction in rolling resistance from a base tire, which was 

estimated to be a 1 to 2 percent effectiveness improvement in MYs 2012-2016 final rule. 

Based on the 2011 Ricardo study the agencies are now using 1.9 percent effectiveness 

improvement for LRR1for all vehicle classes. LRR1 tires are widely available today, and 

appear to comprise a larger and larger portion of tire manufacturers’ product lines as the 

technology continues to improve and mature. The second level (“LRR2”) is defined as a 20 

percent reduction in rolling resistance from a base tire, yielding an estimated 3.9 percent 

effectiveness improvement. In the CAFE model this results in a 2.0 percent incremental 

effectiveness increase from LRR1. LRR2 represents an additional level of rolling resistance 

improvement beyond what the agencies considered in the MYs 2012-2016 rulemaking 

analysis. NHTSA assumed that the increased traction requirements for braking and handling 

for performance vehicles could not be fully met with the ROLL2 designs in the MYs 2017-

2025 timeframe. For this reason the CAFE model did not apply ROLL2 to performance 

vehicle classifications. However, the agency did assume that tractions requirement for 

ROLL1 could be met in this timeframe and thus allowed ROLL1 to be applied to performance 

vehicle classifications in the MYs 2017-2025 timeframe. 

In the 2012-2016 light duty vehicle rule, the agencies estimated the incremental DMC 

at an increase of $5 (2007$) per vehicle. This included costs associated with five tires per 
 Technologies Considered in the Agencies’ Analysis

3-209 

vehicle, four primary and one spare tire. There is no learning applied to this technology due to 

the commodity based nature of this technology. Looking forward from 2016, the agencies 

continue to apply this same estimated DMC adjusted for 2010 dollars.bbb The agencies 

consider LRR1 to be fully learned out or “off” the learning curve (i.e., the DMC does not 

change year-over-year) and have applied a low complexity ICM of 1.24 through 2018, and 

then 1.19 thereafter, due to the fact that this technology is already well established in the 

marketplace. 

To analyze the feasibility and cost for a second level of rolling resistance 

improvement, EPA, NHTSA, and CARB met with a number of the largest tire suppliers in the 

United States. The suppliers were generally optimistic about the ability to reduce tire rolling 

resistance in the future without the need to sacrifice traction (safety) or tread life (durability). 

Suppliers all generally stated that rolling resistance levels could be reduced by 20 percent 

relative to today’s tires by MY 2017. As such, the agencies agreed, based on these 

discussions, to consider LRR2 as initially available for purposes of this analysis in MY 2017, 

but not widespread in the marketplace until MYs 2022-2023. In alignment with introduction 

of new technology, the agencies limited the phase-in schedule to 15 percent of a 

manufacturer’s fleet starting in 2017, and did not allow complete application (100 percent of a 

manufacturer’s fleet) until 2023. The agencies believe that this schedule aligns with the 

necessary efforts for production implementation, such as system and electronic system 

calibration and verification. 

LRR2 technology does not yet exist in the marketplace today, making cost estimation 

challenging without disclosing potentially confidential business information. To develop a 

transparent cost estimate, the agencies relied on LRR1 history, costs, market implementation, 

and information provided by the 2010 NAS report. The agencies assumed low rolling 

resistance technology (“LRR1”) first entered the marketplace in the 1993 time frame with 

more widespread adoption being achieved in recent years, yielding approximately 15 years to 

maturity and widespread adoption. Then, using MY 2017 as the starting point for market 

entry for LRR2 and taking into account the advances in industry knowledge and an assumed 

increase in demand for improvements in this technology, the agencies interpolated DMC for 

LRR2 at $10 (2010$) per tire, or $40 ($2010) per vehicle. This estimate is generally fairly 

consistent with CBI suggestions by tire suppliers. The agencies have not included a cost for 

the spare tire because we believe manufacturers are not likely to include a LRR2 as a spare 

given the $10 DMC. In some cases and when possible pending any state-level requirements, 

manufacturers have removed spare tires replacing them with tire repair kits to reduce both 

cost and weight associated with a spare tire.81 The agencies consider this estimated cost for 

LRR2 to be applicable in MY 2021. Further, the agencies consider LRR2 technology to be on 

the steep portion of the learning curve where costs would be reduced quickly in a relative 

 

bbb As noted elsewhere in this chapter, we show dollar values to the nearest dollar. However, dollars and cents 

are carried through each agency’s respective analysis. Thus, while the cost for lower rolling resistance tires in 

the 2012-2016 final rule was shown as $5, the specific value used in that rule was $5.15 (2007$) and is now 

$5.40 (2010$). We show $5 for presentation simplicity. 
 Technologies Considered in the Agencies’ Analysis

3-210 

short period of time. The agencies have applied a low complexity ICM of 1.24 through 2024, 

and then 1.19 thereafter. The ICM timing for LRR2 is different from that for LRR1 because 

LRR2 is brand-new for this rulemaking and is not yet being implemented in the fleet. The 

resultant costs are shown in Table 3-121. Note that both LRR1 and LRR2 are incremental to 

the baseline system, so LRR2 is not incremental to LRR1. 

Table 3-121 Costs for Lower Rolling Resistance Tires Levels 1 & 2 (2010$) 

Cost type

Lower 

Rolling 

Resistance 

Tire 

Technology

2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Level 1 $5 $5 $5 $5 $5 $5 $5 $5 $5

DMC Level 2 $63 $63 $51 $51 $40 $39 $38 $37 $36

IC Level 1 $1 $1 $1 $1 $1 $1 $1 $1 $1

IC Level 2 $10 $10 $10 $10 $10 $10 $10 $10 $8

TC Level 1 $7 $7 $6 $6 $6 $6 $6 $6 $6

TC Level 2 $73 $73 $60 $60 $50 $49 $48 $47 $44

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Note that both levels of lower rolling resistance tires are incremental to today’s baseline tires.

Given that the final standards cover such a long timeframe, the agencies also 

considered introducing a third level of rolling resistance reduction (“LRR3”), defined as a 30 

percent reduction in rolling resistance. The agencies evaluated the potential of LRR3 entering 

the marketplace during this final rulemaking timeframe. 

Tire technologies that enable improvements of 10 and 20 percent have been in 

existence for many years. Achieving improvements up to 20 percent involves optimizing and 

integrating multiple technologies, with a primary contributor being the adoption of a silica 

tread technology.82 This approach was based on the use of a new silica along with a specific 

polymer and coupling agent combination. The use of the polymer, coupling agent and silica 

was known to reduce tire rolling resistance at the expense of tread wear, but new approach 

using novel silica reduced the tread wear tradeoff. Tire suppliers have indicated there are one 

or more innovations/inventions that they expect to occur in order to move the industry to the 

next quantum reduction of rolling resistance. However, based on the historical development 

and integration of tire technologies, there appears to be little evidence supporting 

improvements beyond LRR2 by 2025. Therefore, the agencies decided not to incorporate 

LRR3 at this time. 

The agencies sought comment on whether we should consider application of a 30 

percent reduction from today’s rolling resistance levels being available for mass production 

implementation by MY 2025 or sooner. The agencies also sought comment on the viability of 

this technology, maturity by MY 2025, as well as market introduction timing and the 

technological ways that this level of rolling resistance improvement will be achieved without 

any tradeoffs in terms of vehicle handling capability and tire life from what consumers expect 

today. Finally, the agencies sought cost information regarding the potential incorporation of 

LRR3 relative to today’s costs as well as during the timeframe covered by this final rule. No 

comments were submitted on any of these topics. 
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3.4.5.2 Low Drag Brakes 

Low drag brakes reduce the sliding friction of disc brake pads on rotors when the 

brakes are not engaged because the brake pads are pulled away from the rotating disc either 

by mechanical or electric methods 

The 2012-2016 final rule and TAR estimated the effectiveness of low drag brakes to 

be as much as 1 percent. NHTSA and EPA have slightly revised the effectiveness down to 

0.8 percent based on the 2011 Ricardo study and updated lumped-parameter model. 

 In the 2012-2016 rule, the agencies estimated the DMC at $57 (2007$). This DMC 

becomes $59 (2010$) for this analysis after adjusting to 2010 dollars. The agencies consider 

low drag brake technology to be off the learning curve (i.e., the DMC does not change yearover-year) and have applied a low complexity ICM of 1.24 through 2018 then 1.19 thereafter. 

The resultant costs are shown in Table 3-122. 

Table 3-122 Costs for Low Drag Brakes (2010$) 

Cost type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC $59 $59 $59 $59 $59 $59 $59 $59 $59

IC $14 $14 $11 $11 $11 $11 $11 $11 $11

TC $74 $74 $71 $71 $71 $71 $71 $71 $71

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.5.3 Front or Secondary Axle Disconnect for Four-Wheel Drive Systems 

Energy is required to continually drive the front, or secondary, axle in a four-wheel 

drive system even when the system is not required during most operating conditions. This 

energy loss directly results in increased fuel consumption and CO2 emissions. Many part-time 

four-wheel drive systems use some type of front axle disconnect to provide shift-on-the-fly 

capabilities. The front axle disconnect is normally part of the front differential assembly. As 

part of a shift-on-the-fly four-wheel drive system, the front axle disconnect serves two basic 

purposes. First, in two-wheel drive mode, it disengages the front axle from the front driveline 

so the front wheels do not turn the front driveline at road speed, saving wear and tear. 

Second, when shifting from two- to four-wheel drive “on the fly” (while moving), the front 

axle disconnect couples the front axle to the front differential side gear only when the transfer 

case’s synchronizing mechanism has spun the front driveshaft up to the same speed as the rear 

driveshaft. Four-wheel drive systems that have a front axle disconnect typically do not have 

either manual- or automatic-locking hubs. To isolate the front wheels from the rest of the 

front driveline, front axle disconnects use a sliding sleeve to connect or disconnect an axle 

shaft from the front differential side gear. NHTSA and EPA are not aware of any 

manufacturer offering this technology in the U.S. today on unibody frame vehicles; however, 

it is possible this technology could be introduced by manufacturers within the MYs 2017-

2025 time period. 
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The 2012-2016 final rule estimated an effectiveness improvement of 1.0 to 1.5 percent 

for axle disconnect. Based on the 2011 Ricardo report, NHTSA and EPA refined this range to 

1.2 to 1.4 percent. 

In the 2012-2016 rule, the agencies estimated the DMC at $78 (2007$) which was 

considered applicable to the 2015MY. This DMC becomes $82 (2010$) for this analysis after 

adjusting to 2010 dollars. The agencies consider secondary axle disconnect technology to be 

on the flat portion of the learning curve and have applied a low complexity ICM of 1.24 

through 2018 then 1.19 thereafter. The resultant costs are shown in Table 3-123. 

Table 3-123 Costs for Secondary Axle Disconnect (2010$) 

Cost type 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC $78 $76 $75 $73 $72 $70 $69 $68 $66

IC $20 $20 $16 $16 $16 $16 $16 $16 $16

TC $98 $96 $91 $89 $88 $86 $85 $83 $82

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

3.4.5.4 Aerodynamic Drag Reduction 

Many factors affect a vehicle’s aerodynamic drag and the resulting power required to 

move it through the air. The overall drag force can be simplified as proportional to vehicle’s 

frontal area, vehicle’s drag coefficient, air density and the second order of vehicle’s velocity. 

Therefore reducing vehicle’s frontal area and drag coefficient can reduce fuel consumption 

and CO2 emissions. Although frontal areas tend to be relatively similar within a vehicle class 

(mostly due to market-competitive size requirements), significant variations in drag 

coefficient can be observed. Significant changes to a vehicle’s aerodynamic performance 

may need to be implemented during a redesign (e.g., changes in vehicle shape). However, 

shorter-term aerodynamic reductions, with a somewhat lower effectiveness, may be achieved 

through the use of revised exterior components (typically at a model refresh in mid-cycle) and 

add-on devices that are currently being applied. The latter list would include revised front 

and rear fascias, modified front air dams and rear valances, addition of rear deck lips and 

underbody panels, and lower aerodynamic drag exterior mirrors. 

The 2012-2016 final rule estimated that a fleet average of 10 to 20 percent total 

aerodynamic drag reduction is attainable which equates to incremental reductions in fuel 

consumption and CO2 emissions of 2 to 3 percent for both cars and trucks. These numbers 

are generally supported by the Ricardo study and public technical literature and therefore 

NHTSA and EPA are retaining these estimates, as confirmed by joint review, for the purposes 

of this final rule, consistent with the proposal. Importantly, the effectiveness values presented 

here represent two-cycle effectiveness. Because active aerodynamic technologies (i.e., aero 

level 2) provide additional off-cycle benefits, both agencies apply an off-cycle credit value to 

the technology. Off-cycle credits are discussed in Chapter 5 of this Joint TSD. 
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For this final rule, consistent with the proposal, the agencies considered two levels of 

aero improvements. The first level is that discussed in the 2012-2016 final rule and the 2010 

TAR and includes such body features as air dams, tire spats, and perhaps one underbody 

panel. In the 2012-2016 rule, the agencies estimated the DMC of aero-level 1 at $39 (2007$). 

This DMC becomes $41 (2010$) for this analysis, applicable in the 2015MY. The agencies 

consider aero-level 1 technology to be on the flat portion of the learning curve and have 

applied a low complexity ICM of 1.24 through 2018 then 1.19 thereafter. 

The second level of aero—level 2 which includes such body features as active grille 

shuttersccc, rear visors, larger under body panels or low-profile roof racks —was discussed in 

the 2010 TAR where the agencies estimated the DMC at $120 (2008$) incremental to the 

baseline vehicle. The agencies inadvertently used that cost as inclusive of aero-level 1 

technologies when it should have been incremental to aero-1 technologies. As a result, the 

agencies now consider the TAR cost to more appropriately be incremental to aero-level 1 with 

a DMC for this analysis of $123 (2010$). The agencies consider this cost to be applicable in 

the 2015MY. Further, the agencies consider aero-level 2 technology to be on the flat portion 

of the learning curve. The agencies have applied a medium complexity ICM of 1.39 through 

2024 then 1.29 thereafter. The timing of the aero-level 2 ICMs is different than that for the 

level 1 technology because the level 2 technology is newer and not yet being implemented in 

the fleet. The resultant costs are shown in Table 3-124. 

Table 3-124 Costs for Aerodynamic Drag Improvements – Levels 1 & 2 (2010$) 

Cost 

type

Aero 

Technology

Incremental 

to 2017 2018 2019 2020 2021 2022 2023 2024 2025

DMC Level 1 Baseline $39 $38 $37 $37 $36 $35 $35 $34 $33

DMC Level 2 Aero-level 1 $117 $115 $112 $110 $108 $106 $104 $102 $100

IC Level 1 Baseline $10 $10 $8 $8 $8 $8 $8 $8 $8

IC Level 2 Aero-level 1 $47 $47 $47 $47 $47 $47 $47 $47 $35

TC Level 1 Baseline $49 $48 $45 $45 $44 $43 $42 $42 $41

TC Level 2 Aero-level 1 $164 $162 $160 $157 $155 $153 $150 $148 $135

TC Level 2 Baseline $213 $210 $205 $202 $199 $196 $193 $190 $176

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Because a large percent of the performance vehicles already have some level of 

aerodynamic treatments, when running the CAFE model NHTSA only applies level 1 of 

aerodynamic treatment to these vehicles. Also for specific vehicles, such as Toyota Prius, 

which already have extensive aerodynamic treatment, the level of the aerodynamic that could 

be further applied by NHTSA in the CAFE model is limited in the market input file. 

 

ccc For details on how active aerodynamics are considered for off-cycle credits, see TSD Chapter 5.2.2. 
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3.4.5.5 Mass Reduction 

From 1987-2011, there has been a generally increasing trend in the weight of the light 

duty vehicle fleet as shown in Figure 3-26 from EPA’s Fuel Economy Trends Report83. A 

number of factors have contributed to this weight increase, including the choices of 

manufacturers and consumers to build and purchase larger vehicles, including heavier trucks, 

SUVs, and CUVs. Also contributing to this weight increase has been an increase in vehicle 

content including: safety features (air bags, antilock brakes, energy absorbent and intrusion 

resistant vehicle structures, etc.), noise reduction (additional damping material), added 

comfort and convenience features (air conditioning, power locks and windows), luxury 

features (infotainment systems, powered seats), etc. 

Figure 3-26 Light duty fleet weight trends: 1975-2011

Despite this increase in weight, the average acceleration of vehicles has grown steadily 

faster without any marked or consistent reduction in fuel economy since 1987, as shown in 

Figure 3-27. This combination of increased vehicle performance, stable fuel economy, and 

increased vehicle weight has been partially enabled by the development and adoption of more 

efficient technologies, especially in engines and transmissions. The impressive improvements 

in powertrain efficiency during this period have offset increases in energy consumption that 

result from improvements in weight carrying, towing and volume capacities, safety, consumer 

features, vehicle refinement, and acceleration performance. 

3000

3500

4000

4500

5000

1975 1980 1985 1990 1995 2000 2005 2010

Vehicle test weight (lb)

Model year

Trucks

Both Cars
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Figure 3-27 Light duty fleet trends for acceleration and fuel economy: 1975-201184

Figure 3-28 U.S. Vehicle Fatality Rates for the past 60 years85

Vehicle mass reduction (also referred to as “down-weighting” or ‘light-weighting”), 

reduces the energy needed to overcome inertial forces, thus yielding lower fuel consumption 

and GHG emissions. While keeping everything else constant, a lighter vehicle will require 

less energy to operate than a heavier vehicle. Mass reduction can be achieved through a 

number of approaches described below, even while maintaining vehicle size. Alternatively, 

mass reduction can also be achieved by vehicle “downsizing” which involves reducing 

vehicle exterior dimensions, such as shifting from a midsize vehicle to a compact vehicle. 

Consistent with the proposal, the agencies did not analyze downsizing as a mass reduction 

strategy in this analysis for the final rule. In part, this is because a manufacturer’s ability to 

downsize its vehicles is constrained by consumer preferences (such as for interior passenger 

or cargo volume), which are in turn influenced by many factors that are difficult to predict in 

8

9

1 0

1 1

1 2

1 3

1 4

1 5

1 6

8

1 0

1 2

1 4

1 6

1 8

2 0

2 2

2 4

1975 1980 1985 1990 1995 2000 2005 2010

Acceleration, 0-60 mph (sec)

Adjusted fuel economy (mpg)

Model year

acceleration

fuel economy
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the future, such as the consumer’s utility needs, fuel prices, economic conditions, etc. Also, 

the final CAFE and GHG emission standards are based on vehicle footprint (the area bounded 

by where the four tires contract the ground), and assign higher fuel economy targets (and 

lower CO2 emission targets) for vehicles with smaller footprints and lower fuel economy 

targets (and higher CO2 emission targets) for vehicles with larger footprints. As discussed in 

Chapter 2 of the joint TSD, the agencies believe the shape of the footprint-based target curves 

will not create incentives for manufacturers to either upsize or downsize their vehicles. Based 

on these considerations, the agencies are assuming that manufacturers will favor mass 

reduction through material substitution, design optimization, and adopting other advanced 

manufacturing technologies rather than compromising a vehicle’s attributes and functionality, 

such as occupant or cargo space, vehicle safety, comfort, acceleration, etc. Consequently, the 

compliance paths the agencies have investigated for the promulgated standards do not include 

downsizing. 

Mass reduction has an important relationship with vehicle powertrain selection and 

sizing. Vehicle powertrain selection depends on an OEM’s product strategy, and may include 

a variety of options such as naturally aspirated engines, boosted and downsized gasoline 

engines, diesel engines, or vehicle electrification (P/H/EV) Regardless of the strategy 

selected, vehicle mass reduction for non-powertrain systems is an important enabler to further 

reduce vehicle fuel consumption and reduce the size of the powertrain system. The term 

“glider” refers to a complete vehicle minus the powertrain. Figure 3-29 illustrates the mass 

breakdown by system for a typical vehicle86. The non-powertrain systems normally account 

for 75 percent of vehicle weight. The agencies have accounted for some of the costs of 

engine mass reduction when applying engine downsizing technologies. The agencies have 

also accounted for the amount of mass change due to the application of hybrid and 

electrification technologies in the vehicle electrification sections. Therefore, this section 

focuses on both the mass reduction of the glider as well as mass reduction technologies that 

are specifically targeted at reducing the weight of the powertrain.ddd rather than on mass 

reduction resulting from powertrain efficiency improvements. An example of a mass 

reduction technology for the powertrain that is not related to powertrain efficiency 

improvement is material substitution, such as changing the engine block from cast iron to 

aluminum or changing the size of the fuel tank.). Mass reduction is calculated for both the 

glider and the vehicle including powertrain in the studies sponsored by the agencies as shown 

later in this section. 

 

ddd Rather than on mass reduction resulting from powertrain efficiency improvements, such as in the case of 

adding a turbocharger to a downsized engine. 
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Figure 3-29 Vehicle system mass approximation 

A vehicle can be divided into 6 major systems, which are shown in Figure 3-29. Mass 

reduction can potentially be applied to any of a vehicle’s subsystems, including the engine, 

exhaust system, transmission, chassis, suspension, brakes, body, closure panels, glazing, seats 

and other interior components, engine cooling systems, and HVAC systems. While 

manufacturers may reduce the mass of some individual components during a vehicle refresh, 

they generally undertake larger amounts of mass reduction systematically and more broadly 

across all vehicle systems when redesigning a vehicle. In the redesign process, OEMs 

normally set weight targets by benchmarking other vehicles in the same segment and 

projecting weight trends into the future, and then identifying targets for all components and 

subsystems that support achieving the target. The agencies believe this holistic approach, 

which takes into consideration all secondary mass savings, is likely the most effective way for 

OEMs to achieve large amounts of mass reduction. 

During a vehicle redesign where mass reduction is a strategic vehicle program goal, 

OEMs can consider modular systems design, secondary mass effects, multi-material concepts, 

and new manufacturing processes to help optimize the design. There are several studies in the 

public domain that illustrate the potential for these approaches to achieve significant amounts 

of mass reduction, although it is important to also recognize that the studies use some 

assumptions that do not account for some of the considerations that are important to 

manufacturers. One example is the need to share some components across platforms to 

manage cost and part complexity for assembly and service, which limits the ability to 

optimize the amount of mass reduction on every vehicle component. Care must also be taken 

in any study to assure that vehicle functionality and performance, such as stiffness, NVH, 

safety and vehicle dynamics, continue to meet manufacturer objectives and consumer 

demands. It is important for design studies to use tools such as simulation modeling to assess 

the design’s ability to meet functionality and performance targets. In this rulemaking, the 

agencies have targeted to preserve vehicle function and performance in their analysis of mass 

reduction. 
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An example of this approach is illustrated in Figure 3-30, which summarizes the 

results of the 2010 phase I Lotus Engineering mass reduction study of a Toyota Venza.

Mass-reduction 

features, findings

Redesign conventional mid-size vehicle for mass optimization, with two redesign architectures

Low Development vehicle technology with industry-leading manufacturing techniques that were 

deemed feasible for 2014 (for model year 2017 production) for assembly at existing facilities

High Development vehicle technology, with modifications to conventional joining and assembly 

processes that were deemed feasible for 2017 (for model year 2020) production

Extensive use of material substitution with high-strength steel, advanced high–strength steel, 

aluminum, magnesium, plastics and composites throughout vehicles

Conservative use of emerging design and parts integration concepts to minimize technical risk

Using synergistic total vehicle substantial mass reduction opportunities found at minimized piece costs

The Low Development vehicle was found to have likely piece cost reductions, whereas the High 

Development vehicle had nominal estimated cost increase of 3% (with potential for cost reduction)

Mass-reduction 

impact

Body structure reduction for Low Development Vehicle: 55 lb (6.6%)

Body structure reduction for High Development Vehicle: 356 lb (42%)

Overall glider reduction for Low Development Vehicle: 538 lb (19%)

Overall glider reduction for High Development Vehicle: 1096 lb (39%)

Overall vehicle reduction for Low Development Vehicle (with hybrid powertrain): 657 lb (17.6%)

Overall vehicle reduction for High Development Vehicle (with hybrid powertrain): 1209 lb (32%)

Status Engineering design study conducted by Lotus Engineering

First phase of project, development of two mass-reduced vehicle designs completed in April 2010

Second phase to test structural integrity, impact load paths, crash worthiness to validate the vehicle 

designs.

Source Lotus Engineering, Inc. 2010. An Assessment of Mass Reduction Opportunities for a 2017-2020 

Model Year Vehicle Program 

Illustrations

Figure 3-30 Example of a holistic vehicle redesign study from Lotus Engineering87

Mass reduction can be considered in terms of the “percent by which the redesigned 

vehicle is lighter than the previous version,” recognizing that the value likely represents both 

“primary” mass reduction (that which the manufacturer set out to make lighter), and 

“secondary” mass reduction (from ancillary systems and components that can now be lighter 

due to the primary mass reductions). 

As summarized by NAS in its 2011 report,88 there are two key strategies for primary 

mass reduction: 1) changing the design to use less material or 2) substituting lighter materials 

for heavier materials. The first key strategy of using less material compared to the baseline 

component can be achieved by optimizing the design and structure of the component, system 

or vehicle structure. For example, a number of “body on frame” vehicles have been 

redesigned with a lighter “unibody” construction, eliminating components, reducing the 

weight of the body structure, and resulting in significant reductions in overall mass and 

related costs. The unibody design currently dominates the passenger car segment and has 

increased penetration into what used to be mostly body-on-frame vehicles, such as SUVs. 
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This technique was used in the 2011 Ford Explorer redesign, which also employed the 

extensive use of high strength steels.89 Figure 3-31 depicts body-on-frame and unibody 

designs for two sport utility vehicles. 

Figure 3-31 Illustration of Body-on-Frame (BoF) and Unibody vehicle construction 

To further reduce mass inefficiencies in vehicle design, vehicle manufacturers are 

using continually-improving Computer Aided Engineering (CAE) tools. For example, the 

Future Steel Vehicle (FSV) project90 sponsored by WorldAutoSteel used three levels of 

optimization: topology optimization, low fidelity 3G (Geometry Grade and Gauge) 

optimization, and sub-system optimization, to achieve 30 percent mass reduction in the body 

structure of a vehicle with a mild steel unibody structure (see Figure 3-32). Designs similar 

to those proposed in the FSV project have been applied in production vehicles, such as the Bpillar of 2010 Ford Focus.91

 
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Figure 3-32 Example of vehicle body load path mapping for mass optimization 

Vehicle manufacturers have long used these continually-improving CAE tools to 

optimize vehicle designs. But because any design must meet component and system 
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functionality and manufacturability targets, there are practical limitations to the amount of 

additional mass reduction that can be achieved through optimization. For example, an 

optimization program would need to account for safety, stiffness, NVH, manufacturing, and 

other requirements to assure the design is suitable for its intended function and for mass 

production. Additionally, ultimate optimization of vehicle design for mass reduction may be 

limited by an OEM’s use of shared components and common platform for multiple vehicle 

models. While optimization may concentrate on the vehicle that has the largest production 

volume for a platform, designs must also support the most demanding functional requirements 

of all of the vehicles that share that platform, or those functional requirements will not be met. 

In addition, the engineering resources and capital for tooling and equipment that would be 

needed to optimize every vehicle component at each redesign affects the ability to fully 

optimize a new vehicle to achieve all of the theoretically possible secondary mass reduction. 

Therefore, some level of mass inefficiency will inherently exist on many or all of the vehicles 

that share a platform. The agencies sought comment and information in the NPRM on the 

degree to which shared vehicle components and architectures affect the feasible amount of 

mass reduction and the cost for mass reduction relative to what could be achieved if mass 

reduction was optimized for a single vehicle design. Volkswagen confirmed in its comments 

that with platform sharing, “a weight reduction technology which may be acceptable in terms 

of price or performance for one model may disrupt the economics or utility of another.”92

 

Using less material can also be achieved through improving the manufacturing 

process, such as by using improved joining technologies and parts consolidation. This 

method is often used in combination with applying new materials. For example, more precise 

manufacturing techniques such as laser welding may reduce the flange size necessary for 

welding, and thus marginally decrease the mass of an assembly. Also, when complex 

assemblies are constructed from fewer pieces, the mass of the assembly tends to be lower. 

However, while synergies in mass reduction certainly exist, and while certain technologies 

can enable one another (e.g., parts consolidation and molding of advanced composites), others 

may be incompatible (e.g., laser welding and magnesium casting). 

The second key strategy to reduce mass of an assembly or component involves the 

substitution of lower density and/or higher strength materials. Table 3-125 shows material 

usage typical of contemporary high-volume vehicles. Material substitution includes replacing 

materials, such as mild steel, with higher-strength and advanced steels, aluminum, 

magnesium, and composite materials. The substitution of advanced high strength steel 

(AHSS) for mild steel can reduce the mass of a strength-critical part because the gauge of the 

AHSS components can be reduced, despite the fact that the densities of the materials are not 

significantly different. Aluminum has also been used over the years in a variety of 

components, such as vehicle closures, suspension parts, engine cradles, etc. Aluminum has 

one third the density of steel and therefore can provide a notable amount of mass reduction. 

Changing parts from steel to aluminum generally requires part redesign, and extra material 

may have to be added for strength or durability. Aluminum also has a shorter fatigue life than 

steel, and therefore the alloy selected and the application must be carefully considered. 

Magnesium can provide additional mass reduction as it has lower density than aluminum. It 

has been used for instrument panel cross-car beams by several OEMs for a number of years. 

It has also been used in an engine block produced by BMW for several years. Its brittle 
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nature must be considered, however, when selecting the alloy and the application within the 

vehicle. 

Table 3-125 Distribution of Material in Typical Contemporary Vehicles (e.g., Toyota Camry or Chevrolet 

Malibu)93

Automobiles also utilize a wide range of plastic types, including polypropylenes, 

polyesters, and vinyl esters. These materials are utilized in hatches, roofs, interior panels, 

instrument panels, and hundreds of other parts. Although primarily used in nonstructural 

vehicle components, plastics have continued to make in-roads in bumper systems and in 

composite beam applications, and some studies have found potential to supplant structural 

beams and frame components. Lighter plastics have also been developed by the industry, and 

the application of these materials has been increasing. 

Included in the category of plastics are composites like glass fiber and carbon fiber 

reinforced polymers. While these more costly advanced materials have primarily been used 

in a limited number of low production volume vehicle applications, some manufacturers are 

considering these composites for broader use. While these materials currently have the 

potential to be applied to components with little or no exposure to impact pulses, the 

advanced microstructure and limited industry experience may make these longer-term 

solutions. For example, advanced composite materials (such as carbon fiber-reinforced 

plastic), depending on the specific fiber, matrix, reinforcement architecture, and processing 

method, can be subject to dozens of competing damage and failure mechanisms that may 

complicate a manufacturer’s ability to ensure equivalent levels of durability and 

crashworthiness. As the industry gains experience with these materials, these concerns will 

inevitably diminish, but may remain relevant during the timeframe of this final rulemaking. 

In practice, material substitution tends to be quite specific to the manufacturer and 

situation. Some materials work better than others for particular vehicle components, and a 

manufacturer may invest more heavily in adjusting to a particular type of advanced material, 

thus complicating its ability to consider others. The agencies recognize that like any type of 

mass reduction, material substitution has to be conducted not only with consideration to 

maintaining equivalent component strength, but also to maintaining all the other attributes of 

that component, system or vehicle, such as crashworthiness, durability, and NVH. 

If vehicle mass is reduced sufficiently through application of the two primary 

strategies of using less material and material substitution described above, secondary mass 

reduction options may become available. Secondary mass reduction is enabled when the load 

requirements of a component are reduced as a result of primary mass reduction. If the 
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primary mass reduction reaches a sufficient level, a manufacturer may use a smaller, lighter, 

and potentially more efficient powertrain while maintaining vehicle acceleration performance. 

If a powertrain is downsized, approximately half of the mass reduction may be attributed to 

the reduced torque requirement which results from the lower vehicle mass. The lower torque 

requirement enables a reduction in engine displacement, changes to transmission including 

the torque converter and gear ratios, and changes to final drive gear ratio. The reduced 

powertrain torque enables the downsizing and/or mass reduction of powertrain components 

and accompanying reduced rotating mass (e.g., for transmission, driveshafts/halfshafts, 

wheels, and tires) without sacrificing powertrain durability. Likewise, the combined mass 

reductions of the engine, drivetrain, and body in turn reduce stresses on the suspension 

components, steering components, wheels, tires, and brakes, which can allow further 

reductions in the mass of these subsystems. Reducing the unsprung masses such as the 

brakes, control arms, wheels, and tires further reduce stresses in the suspension mounting 

points, which will allow for further optimization and potential mass reduction. 

Secondary mass reduction can occur for each kg of primary mass reduction, when all 

subsystems are redesigned to take the initial primary mass reduction into account. In the MYs 

2012-2016 rulemaking analysis, the agencies assumed that 1 kg of primary mass reduction 

could enable up to 1.25 kg of secondary mass reduction. In the two most recent mass 

reduction projects by EPA and NHTSA, every 1 kg of primary mass reduction enabled 0.7 kg 

of secondary mass reduction. We note that these estimates may not be applicable in all realworld instances of mass reduction, and that the literature indicates that the amount of 

secondary mass reduction potentially available varies significantly from an additional 0.5 kg 

to 1.25 kg per 1 kg of primary mass reduction, depending on assumptions such as which 

components or systems primary mass reduction is applied to, and whether the powertrain is 

available for downsizing. 94,95,96 The amount of secondary mass reduction is also affected by 

the degree of component sharing that occurs among a manufacturer’s models. Component 

sharing is used by manufacturers to achieve production economies of scale that affect cost and 

the number of unique parts that must be managed in production and for service. In addition, 

the engineering resources and capital for tooling and equipment that would be needed to 

optimize every vehicle component at each redesign affects the ability to fully optimize a new 

vehicle to achieve all of the theoretically possible secondary mass reduction. While there is 

agreement in the literature that primary mass reduction can enable secondary mass reduction, 

the agencies recognize that care must be taken when reviewing reports on mass reduction 

methods and practices to ascertain the manner and extent to which compounding effects have 

been considered. 

All manufacturers are using some or all of these methods to reduce mass in the 

vehicles they are producing today, and the agencies expect that the industry will continue to 

learn and improve the application of these techniques for more vehicles during the rulemaking 

timeframe. We consider mass reduction in net percentage terms in our analysis not only 

because effectively determining specific appropriate mass reduction methods for each vehicle 

in the baseline fleet is a large task beyond the scope of this rulemaking, but also because we 

recognize that even as manufacturers reduce mass to make vehicles more efficient, they may 

also be adding mass in the form of increased vehicle features and safety content in response to 

market forces and other governmental regulations. For these reasons, when the agencies 
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discuss the amount of mass reduction that we are assuming is feasible for purposes of our 

analysis, we are implicitly balancing both the considerable opportunities that we believe exist 

for mass reduction in the future, and the reality that vehicle manufacturing is complex and 

that mass reduction methods must be applied thoughtfully and judiciously as safety and 

content demands on vehicles continue to increase over time. Despite our considerable 

discussion of the topic, the agencies’ application of mass reduction in our analysis is fairly 

simplified. As applied in our models, the percentage reduction for a given vehicle that is 

assumed for a given year is an abstraction of all the specific mass reduction methods 

described above. 

How much mass reduction do the agencies believe is feasible in the rulemaking timeframe? 

Feasibility, if narrowly defined as the ability to reduce mass without any constraints, is 

nearly unbounded. However, in practice, the feasible amount of mass reduction is affected by 

other considerations. Cost effectiveness is one of those constraints and is discussed further 

below in the mass reduction cost section. In the analysis for the current rulemaking for MYs 

2017-2025, the agencies reviewed a number of public reports and accompanying data, as well 

as confidential information from manufacturers, and believe that mass reduction of up to 20 

percent from a MY 2008 baseline vehicle can be achieved in a cost effective manner using 

technologies currently in production. More detail on studies reviewed by the agencies and 

additional studies currently in progress by the agencies is located below in Table 3-9 and in 

the paragraphs below under the question “What additional studies are the agencies 

conducting to inform our estimates of mass reduction amounts, cost, and effectiveness?”

From a general planning perspective, nearly all automakers have made some public 

statement regarding vehicle mass reduction being a core part of the overall technology 

strategy that they will utilize to achieve future fuel economy and CO2 emission standards. 

- Estimates from Ducker Worldwide indicate that the automobile industry will see 

an annual increase in AHSS of about 10% through 202097. 

- Ford has stated that it intends to reduce the weight of its vehicles by 250-750 lb 

per model from 2011 to 202098. For context, the midpoint of that range of 

reductions would correspond to a 12% reduction from the current Ford new light 

duty vehicle sales fleet. 

- Mazda has released a statement about achieving a 220-lb reduction per vehicle by 

201699. This is equivalent to about a 6% reduction for the company’s current fleet. 

- Land Rover executives have stated that the company remains committed to a goal 

of reducing curb weights of its SUVs by as much as 500 kilograms over the next 

10 years100

. 

- In its comment to the NPRM, Volkswagen stated that they expect to reduce the 

mass of their vehicles by 7-10% on average during the period of this regulation. 

Several reports focusing on the OEM’s approaches for light weighting are 

summarized in the University of California Davis study as shown in Table 3-126 101. 
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Table 3-126 Automaker industry statements regarding plans for vehicle mass-reduction technology 

Affiliation Quote Source

General 

Motors

”We use a lot of aluminum today – about 300 pounds per vehicle - and are likely to 

use more lightweight materials in the future”

Keith, 2010

Ford “The use of advanced materials such as magnesium, aluminum and ultra highstrength boron steel offers automakers structural strength at a reduced weight to help 

improve fuel economy and meet safety and durability requirements”

BMW and 

SGL, 2010

Nissan “We are working to reduce the thickness of steel sheet by enhancing the stregnth, 

expanding the use of aluminum and other lightweight materials, and reducing vehicle 

weight by rationalizing vehicle body structure”

Goede et al, 

2009

BMW “Lightweight construction is a core aspect for sustainable mobility improving both 

fuel consumption and CO2 emissions, two key elements of our Efficient Dynamics 

strategy ... we will be able to produce carbon fiber components in large volumes at 

competitive costs for the first time. This is particularly relevant for electric-powered 

vehicles.”

Nunez, 2009

Volkswagen “Material design and manufacturing technologies remain key technologies in vehicle 

development. Only integrated approaches that work on these three key technologies 

will be successful in the future. In addition to the development of metals and light 

metals, the research on fibre-reinforced plastics will play a major role.”

Goede et al, 

2009

Fiat “A reduction of fuel consumption attains big importance because of the possible 

economical savings. In order to achieve that, different ways are followed: alternative 

engine concepts (for example electric engines instead of combustion ones) or weight 

reduction of the vehicle structure. Using lightweight materials and different joining 

techniques hleps to reach this aim”

Nunez, 2009

Volkswagen “Lightweight design is a key measure for reducing vehicle fuel consumption along 

with powertrain efficiency, aerodynamics and electrical power management”

Krinke, 2009

BMW “A dynamic vehicle with a low fuel consumption finally demands a stiff body with a 

low weight. To achieve the initially mentioned targets, it is therefore necessary to 

design a body which offers good stiffness values and a high level of passive safety at 

a low weight.”

Prestorf, 

2009

BMW “Light weight design can be achieved by engineering light weight, manufacutring 

light weight and material light weight design.”

Prestorf, 

2009

Although the focus on mass reduction by manufacturers is widespread, the agencies 

believe the practical limits of mass reduction will be different for each vehicle model as each 

model starts with a different mix of conventional and advanced materials, components, and 

features intended to meet the function and price of a particular market segment. A vehicle 

that already has a significant fraction of advanced high strength steel (AHSS) or any other 

advanced material in its structure, for example, will not have the opportunity to realize the 

same percentage of mass reduction as a vehicle of more traditional construction. Given the 

myriad methods of achieving mass reduction, and the difficulty in obtaining data, accounting 

for the current level of mass reduction technology for every model in production in a baseline 

model year would be an impractical task. However, the agencies believe that reducing 

vehicle weight to reduce fuel consumption has a continuum of solutions and the technologies 

employed will have levels of effectiveness and feasibility that will vary by manufacturers and 

by vehicle. In estimating the amount of mass reduction for this analysis, the agencies also 

consider fleet safety effects for mass reduction. See Section II.G of the preamble for a 

detailed discussion of the safety considerations in establishing CAFE and GHG standards. In 

the CAFE and OMEGA analyses, the agencies considered several levels of mass reduction 

applicable to all of the models in each subclass, as discussed below. 
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Based on the many aspects of mass reduction (i.e., feasibility, cost and safety), for the 

final rule, consistent with the proposal, the agencies believe that mass reduction of up to 20 

percent is feasible on light trucks, CUVs and minivans, but that less mass reduction should be 

implemented on other vehicle types to avoid increases in societal fatalities. While the 

agencies continue to examine mass reduction, we remain alert to safety considerations and 

seek to ensure that any CAFE and CO2 standards can be achieved in a safety-neutral or 

improved manner. 

In the CAFE model, NHTSA applied amounts of mass reduction shown in Table 

3-127, which was based on the ability to achieve overall fleet fatality estimates of close to 

zero. The results are described in Preamble Section II.G and Chapter V of NHTSA’s RIA. 

The amount of mass reduction applied in EPA’s OMEGA model follows the safety neutral 

analysis is described in Section II.G of the Preamble with a variety of tables in EPA’s RIA 

(Chapter 3.8.2). 

Table 3-127 MAXIMUM MASS REDUCTION AMOUNT APPLIED IN CAFE MODEL

Absolute

%

Subcompact 

and 

Subcompact 

Perf. PC

Compact 

and 

Compact 

Perf. PC

Midsize PC 

and Midsize 

Perf. PC

Large PC 

and Large 

Perf. PC

Minivan LT

Small, 

Midsize and 

Large LT

MR1* 0.0% 0.0% 1.5% 1.5% 1.5% 1.5%

MR2 0.0% 0.0% 3.5% 7.5% 7.5% 7.5%

MR3 0.0% 0.0% 0.0% 10.0% 10.0% 10.0%

MR4 0.0% 0.0% 0.0% 0.0% 15.0% 15.0%

MR5 0.0% 0.0% 0.0% 0.0% 20.0% 20.0%

Notes:

*MR1-MR5: different levels of mass reduction used in CAFE model

The amounts of mass reduction shown in Table 3-127 are for conventional vehicles. 

The agencies assume that vehicles with hybrid and electric powertrain are heavier than 

conventional vehicles because of the mass of battery systems. In comparing anecdotal data 

for HEVs, EPA and NHTSA assume a slight weight increase of 4-5% for HEVs as compared 

to baseline non-hybridized vehicles. The added weight of the Li-ion pack, motor and other 

electric hardware were offset partially by the reduced size of the base engine as stated in TSD 

section 3.4.3.8. We believe that this assumption accurately reflects real-world HEV, PHEV 

and EV construction. As an example, for a subcompact PHEV with 20 mile range operating 

on electricity, the agencies assume that to achieve no change in total vehicle mass, it would be 

necessary to reduce the mass of the glider by 6 percent because of the additional weight of the 

electrification system. The mass reduction for P/H/EVs can be found section 3.3.3.9 in the 

joint TSD, and in EPA’s RIA Chapter 1 and Chapter V, section E.3.h.4, of NHTSA’s FRIA. 

How much do the agencies estimate mass reduction will cost in the rulemaking timeframe?

Automakers are currently utilizing various mass reduction techniques across the lightduty vehicle fleet, and will continue to use and in some cases expand these approaches for the 
 Technologies Considered in the Agencies’ Analysis

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2017 to 2025 time frame. These approaches may include optimized design, geometry, part 

consolidations, and materials substitution. Unlike the other technologies described in this 

chapter, mass reduction is potentially more complex in that we cannot define it as a single 

piece of equipment or hardware change to implement the technological improvement. Mass 

reduction, depending upon the level of reduction targeted, has the potential to impact nearly 

every system on the vehicle. Because of this complexity, there are unique challenges to 

estimating the cost for mass reduction and for demonstrating the feasibility of reducing 

vehicle mass by a given amount. This section describes the cost estimates used for the 

agencies’ analysis. 

In the analysis for the MYs 2012-2016 rulemaking, the agencies assumed a constant 

cost for mass reduction of $1.32 for each pound reduced up to a mass reduction level of 10 

percent (or $1.48/lb using an ICM factor of 1.1 for a low-complexity technology). The 

$1.32/lb estimate was based on averaging three studies: the 2002 NAS Report, a 2008 study 

by Sierra Research, and a 2007 study by MIT researchers.eee

Since the MYs 2012-2016 final rule, the agencies have given further consideration to 

the cost of mass reduction, and now believe that a cost that varies with the level of mass 

reduction provides a better estimate. The agencies believe that as the vehicle fleet progresses 

from lower to higher levels of mass reduction and becomes increasingly optimized for mass 

and other attributes, the cost for mass reduction will progressively increase. The higher levels 

of mass reduction may, for example, require applying more advanced materials and 

technologies than lower levels of mass reduction, which means that the cost of achieving 

those higher levels may increase accordingly. The unit cost of mass reduction versus the 

amount of mass reduction might be linear, parabolic, or some other higher order relationship. 

In the 2017-2025 Notice of Intent, 75 FR 62739 (Oct. 13, 2010), CARB, EPA and NHTSA 

derived a second order curve based on a study with two vehicle redesigns conducted by Lotus 

Engineering completed in 2010, such that zero mass reduction had zero cost, and the dollars 

per pound increased with greater levels of mass reduction. Since the publication of the TAR, 

the agencies have identified a number of additional studies in the literature relating to the 

costs of vehicle mass reduction, which are discussed below. The studies show that for low or 

high mass reduction, the costs can range from small cost savings to significant cost increases. 

The economic costs associated with mass reduction are difficult to determine conclusively 

due to the broad range of methods employed to achieve mass reduction. The costs on a 

specific vehicle or component depend on many factors, such as the design, materials selected, 

raw material price, appropriate manufacturing processes, production volume, component 

functionality, required engineering and development, etc. 

 

eee Specifically, the 2002 NAS Report estimated that vehicle weight could be reduced by 5 percent (without 

engine downsizing) at a cost of $210-$350, which translates into $1.50/lb assuming a 3,800 lb base vehicle and 

using the midpoint cost; Sierra Research estimated that a 10 percent reduction (with compounding) could be 

accomplished for $1.01/lb, and MIT researchers estimated that a 14 percent reduction (with no compounding) 

could be accomplished for $1.36/lb. References for these studies are available in endnotes to Chapter 3 of the 

TSD for the MYs 2012-2016 final rule. 
 Technologies Considered in the Agencies’ Analysis

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Cost data thus varies widely in the literature. Of the various studies reviewed by the 

agencies, not all are equal in their original intent, rigor, transparency, or applicability to this 

regulatory purpose. The individual studies range from complete vehicle redesign to advanced 

optimization of individual components, and were conducted by researchers with a wide range 

of experience and background. Some of the studies were literature reviews, while others 

developed new designs for lighter components or complete lighter vehicles, while yet others 

built physical components or systems, and conducted testing on those components and 

systems. Some of the studies focused only on a certain sub-system (which is a building block 

for the overall vehicle design), while some of them took a systematical approach and redesigned the whole vehicle to achieve the maximum mass reduction and cost reduction. The 

latter studies typically identified a specific baseline vehicle, and then utilized different 

engineering approaches and investigated a variety of mass-reduction concepts that could be 

applied to that vehicle. Some of the differences between studies emanate from the 

characteristics of the baseline vehicle and its adaptability to the new technology or method, 

and the cost assumptions relating to the original components and the redesigned components. 

Assumptions regarding the degree and cost of any associated mass de-compounding can also 

confound comparisons.fff Despite this variation in the literature, in actual practice, we 

believe manufacturers will choose a target mass reduction for a whole vehicle and for each 

sub-system, and work to find the lowest total cost method to achieve those targets. Such a 

process would consider numerous primary and secondary cost factors (including engineering, 

facilities, equipment, tooling, and retraining costs) as well as technological and manufacturing 

risks.ggg 

Regardless of the confidence in specific estimates, the agencies must select a curve 

that will be applied to the whole fleet that will define the average cost per kg of mass 

reduction as a function of total percentage of mass reduction. There are many significant 

challenges that make it difficult for the agencies to establish an estimated cost curve based on 

the literature, such as the differences in the baselines used in the studies, whether the studies 

considered platform sharing and powertrain sharing, and other considerations. 

 

fff The concept of secondary weight savings or mass compounding (also called mass decompounding) derives 

from the qualitative understanding that as vehicle weight decreases, other vehicle systems can also decrease in 

mass while maintaining the original vehicle level of performance and function. For instance, following a primary 

weight reduction in the vehicle (e.g. Body in White), the designs of some of the other dependant vehicle 

subsystems (tires, suspensions, brakes, powertrain, body structure) may be redesigned and reduced in mass to 

account for the overall lighter vehicle. The lighter vehicle is also associated with lighter loads, less friction and 

drag, and may require less power to be accelerated, and the powertrain may therefore be scaled down in size with 

a potential for reduced mass, even while maintaining equivalent acceleration performance and functionality. The 

compounded or secondary mass savings from these additional systems may then drive further mass reductions in 

the original primary weight reduction (e.g. Body in White). Mass compounding factors found in literature are 

rough estimates of the secondary mass reduction amount. 

ggg We also note that the cost of mass reduction in the CAFE model is quantified on a per pound basis that is a 

function of the percentage decrease in vehicle mass. We assume that OEMs would find the most cost-effective 

approach to achieve such a mass reduction. Realistically, this would depend heavily on the baseline vehicle as 

well as the size and adaptability of the initial design to the new technology. Thus, the CAFE model strives to be 

realistic in the aggregate while recognizing that the figures proposed for any specific model may be debatable. 
 Technologies Considered in the Agencies’ Analysis

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The costs for mass reduction employed for the main analysis for this final rule are the 

same as those in the NPRM. The agencies considered updating cost estimates based on the 

studies that were underway when the NPRM was issued. Those studies included the 

EPA/ICCT funded Phase 2 Toyota Venza Low Development project and the NHTSA funded 

Honda Accord mass reduction project, which are described in the section titled “What 

additional studies are the agencies conducting to inform our estimates of mass reduction 

amounts, cost, and effectiveness?” However, these studies were in the middle of the peer 

review process and had not yet been finalized at the time when the inputs for the main 

analysis for this final rule were required. For the final rule, the agencies decided to continue 

to use the same costs for mass reduction that were used in the NRPM. 

The agencies examined all the studies in Table 3-128 including information supplied 

by manufacturers (during meetings held subsequent to the TAR) when deciding the mass 

reduction cost estimate used for the proposal, which has been carried forward for this FRM.hhh 

The agencies considered three major factors in examining these studies. First, whether a 

study was rigorous in terms of how it evaluates and validates mass reduction from 

technological and design perspectives. This includes consideration of a study’s 

comprehensiveness, the technical rigor of its methodology, the validation methods employed, 

and the relevance of the technologies evaluated in the study given our rulemaking time frame. 

Second, whether a study was rigorous in terms of its estimation of costs, including the 

completeness and rigor of the methodology, such as whether the study includes data for all 

categories of direct manufacturing costs, and whether the study presents detailed cost 

information for both the baseline and the light-weighted design. And third, the degree of peer 

review, including if the study is peer-reviewed, and whether it has effectively addressed any 

critical technical, methodological, and cost issues raised by the peer-review, if this 

information is available. 

Some of the variation may be attributed to the complexity of mass reduction as it is 

not one single discrete technology and can have direct as well as indirect effects on other 

systems and components. The 2011 NAS study speaks to this point when it states on page 7-1 

that “[t]he term material substitution oversimplifies the complexity of introducing advanced 

materials, because seldom does one part change without changing others around it.” These 

variations underscore that there is not a unique mass reduction solution as there are many 

different methods with varying costs for taking mass out of vehicles, and every manufacturer, 

even every vehicle, could have a different approach depending on the specific vehicle, 

assembly plant and model year of implementation. The agencies recognize that there are 

challenges to characterizing the mass reduction plans for the entire future fleet due to the 

complexity and variety of methods available. So far the agencies have not found any study 

that addresses how to generalize the mass reduction that is achievable on a single vehicle to 

 

hhh The agencies considered confidential cost information provided by OEMs that covered a range of 

components, systems, designs and materials. Some of these cost estimates are higher than some of the literature 

studies, and manufacturers provided varying levels of detail on the basis for the costs such as whether mass 

compounding is included, or whether the costs include markup factors. 
 Technologies Considered in the Agencies’ Analysis

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the whole fleet. Table 3-128 contains a summary of the data contained in the studies, and the 

OEM CBI data, which the agencies reviewed. There is a degree of uncertainty associated 

with comparing the costs from the range of studies in the literature when trying to summarize 

them in a single table, and we encourage interested stakeholders to carefully review the 

information in the literature. For some of the cost estimates presented in the papers there are 

unknowns such as: what year the costs are estimated for, whether mass decompounding (and 

potential resultant cost savings) was taken into account, and whether mark-ups or indirect 

costs were included. The agencies tried to normalize the cost estimations from all these 

studies by converting them to 2009 year dollar, applying mass compounding factor of 1.35 for 

mass reduction amount more than 10 percent if it has not been applied in the study and 

factoring out the RPE specified in the study to derive direct manufacture costs for 

comparison. There are some papers that give cost for only component mass reduction, others 

that have more general subsystem costs and others yet that estimate total vehicle mass 

reduction costs (which often include and present data at the subsystem level). Other studies 

have multiple scenarios for different materials, different vehicle structures and mass reduction 

strategies. Thus, a single study which contains more than one vehicle can be broken down 

into a range of vehicle types, or at the subsystem level, or even at the component level. While 

Table 3-128 is inclusive of all of the information reviewed by the agencies for the NPRM, for 

the reasons described above the technical staff for the two agencies applied various 

approaches in evaluating the information. The linear mass-cost relationship developed for the 

proposal is carried forward to this final rule and presented below is the consensus assessment 

from the two agencies of the appropriate mass cost for this final rule. 

Table 3-128 Mass Reduction Studies Considered for Estimating Mass Reduction Cost for this FRM 

studies 

Cost Information from Studies

Cost YearMass Reduction [lb]Compounding FactorMass Reduction with Compounding [lb]Baseline Vehicle Weight [lb]Mass Reductioning w/Compounding [%]Cost [$]RPEDollar Multiplier to 20092009 Direct Manufacturing Cost [$]Unit Cost of Mass Reduction [$/lb]

Individual Cost Data Points

AISI, 1998 (ULSAB) 1998 103 1 103 2977 3.5% -$32 1.0 1.28 -$41 -$0.40

AISI, 2000 (ULSAC) 2000 6 1 6 2977 0.2% $15 1.0 1.24 $18 $2.99

Austin et al, 2008 (Sierra Research) -

ULS Unibody 2008 320 1 320 3200 10.0% $209 1.61 1.01 $131 $0.41

Austin et al, 2008 (Sierra Research) - AL 

Unibody 2008 573 1 573 3200 17.9% $1,805 1.61 1.01 $1,134 $1.98

Austin et al, 2008 (Sierra Research) -

ULS BoF 2008 176 1 176 4500 3.9% $171 1.61 1.01 $107 $0.61

Austin et al, 2008 (Sierra Research) - AL 

BoF 2008 298 1 298 4500 6.6% $1,411 1.61 1.01 $887 $2.98

Bull et al, 2008 (Alum Assoc.) - AL BIW 2008 279 1 279 3378 8.3% $455 1.0 1.01 $460 $1.65

Bull et al, 2008 (Alum Assoc.) - AL 

Closure 2008 70 1 70 3378 2.1% $151 1.0 1.01 $153 $2.17

Bull et al, 2008 (Alum Assoc.) - Whole 

Vehicle 2008 573 1 573 3378 17.0% $122 1.0 1.03 $126 $0.22
 Technologies Considered in the Agencies’ Analysis

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Cheah et al, 2007 (MIT) - 20% 2007 712 1 712 3560 20.0% $646 1.0 1.03 $667 $0.94

Das, 2008 (ORNL) - AL Body & Panel 2008 637 1 637 3363 19.0% $180 1.5 1.01 $121 $0.19

Das, 2008 (ORNL) – FRPMC 2008 536 1.0 536 3363 15.9% -$280 1.5 1.01 -$189 -$0.35

Das, 2009 (ORNL) - CF Body & Panel, AL 

Chassis 2009 933 1 933 3363 27.7% $1,490 1.5 1.00 $993 $1.06

Das, 2010 (ORNL) - CF Body & Panel, 

Mg Chassis 2010 1173 1 1173 3363 34.9% $373 1.5 1.00 $248 $0.21

EEA, 2007 - Midsize Car - Adv Steel 2007 236 1 236 3350 7.0% $179 1.0 1.03 $185 $0.78

EEA, 2007 - Midsize Car - Plast/Comp 2007 254 1 254 3350 7.6% $239 1.0 1.03 $247 $0.97

EEA, 2007 - Midsize Car – Al 2007 586 1.35 791 3350 23.6% $1,388 1.0 1.03 $1,434 $1.81

EEA, 2007 - Midsize Car – Mg 2007 712 1.35 961 3350 28.7% $1,508 1.0 1.03 $1,558 $1.62

EEA, 2007 - Light Truck - Adv Steel 2007 422 1 422 4750 8.9% $291 1.0 1.03 $301 $0.71

EEA, 2007 - Light Truck - Plast/Comp 2007 456 1 456 4750 9.6% $398 1.0 1.03 $411 $0.90

EEA, 2007 - Light Truck - Al 2007 873 1.35 1179 4750 24.8% $1,830 1.0 1.03 $1,891 $1.60

EEA, 2007 - Light Truck - Mg 2007 1026 1.35 1385 4750 29.2% $1,976 1.0 1.03 $2,042 $1.47

Geck et al, 2008 (Ford) 2008 1310 1 1310 5250 25.0% $500 1.0 1.01 $506 $0.39

Lotus, 2010 – LD 2010 660 1 660 3740 17.6% -$121 1.0 1.00 -$120 -$0.18

Lotus, 2010 – HD 2010 1217 1 1217 3740 32.5% $362 1.0 1.00 $360 $0.30

Montalbo et al, 2008 (GM/MIT) -

Closure - HSS 2008 25 1 25 4000 0.6% $10 1.0 1.01 $10 $0.41

Montalbo et al, 2008 (GM/MIT) -

Closure - AL 2008 120 1 120 4000 3.0% $110 1.0 1.01 $111 $0.92

Montalbo et al, 2008 (GM/MIT) -

Closure - Mg/AL 2008 139 1 139 4000 3.5% $110 1.0 1.01 $111 $0.80

Plotkin et al, 2009 (Argonne) 2009 683 1 683 3250 21.0% $1,300 1.0 1.00 $1,300 $1.90

(... Continued) Mass Reduction Studies Considered for Estimating Mass Reduction Cost for this FRM

Studies

Cost Information from Studies

Cost YearMass Reduction [lb]Compounding FactorMass Reduction with Compounding [lb]Baseline Vehicle Weight [lb]Mass Reductioning w/Compounding [%]Cost [$]RPEDollar Multiplier to 20092009 Direct Manufacturing Cost [$]Unit Cost of Mass Reduction [$/lb]

Cost Curves

NAS, 2010

2010 1.0% $ 1.41 

2010 2.0% $ 1.46 

2010 5.0% $ 1.65 

2010 10.0% $ 1.52 

2010 20.0% $ 1.88 
 Technologies Considered in the Agencies’ Analysis

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EPA and NHTSA scrutinized the various available studies in the literature as well as 

confidential information provided by several auto firms based on the kinds of factors 

described above for purposes of estimating the cost of mass-reduction in the 2017-2025 

timeframe. We determined that there was wide variation across the studies with respect to 

costs estimates, applicability to the 2017-2025 time frame, and technical rigor. The mass cost 

curve that was developed is defined by the following equation and is shown in 

Figure 3-33: 

Mass Reduction Direct Manufacturing Cost (DMC) ($/lb) = $4.36/(%-lb) x Percentage of 

Mass Reduction Level (%) (2010$) 

OEM1

2010 8.0% $ 6.00 

2010 9.0% $ 7.00 

2010 9.5% $ 8.00 

2010 10.0% $ 12.00 

2010 11.0% $ 25.00 

OEM2

2010 0.4% $ - 

2010 0.9% $ 0.10 

2010 1.9% $ 0.20 

2010 2.3% $ 0.33 

2010 2.4% $ 0.38 

2010 3.1% $ 0.60 

2010 3.6% $ 0.76 

2010 4.0% $ 0.85 

2010 4.1% $ 0.88 

2010 4.5% $ 0.98 

2010 4.8% $ 1.09 

2010 5.0% $ 1.17 

OEM3

2010 4.0% $ 0.57 

2010 7.5% $ 1.01 

2010 10.0% $ 1.51 

OEM4

2011 6.9% $ 0.97 

2011 8.1% $ 1.02 

2011 16.4% $ 1.95 
 Technologies Considered in the Agencies’ Analysis

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Figure 3-33 NPRM and FRM Mass Reduction Direct Manufacturing Cost 

For example, this results in an estimated $175 cost increase for a 10% mass reduction of a 

4,000lb vehicle (or $0.44/lb), and a $394 cost increase for 15% reduction on the same vehicle 

(or $0.66/lb). 

As mentioned in the NPRM, due to the wide variation in data used to select this 

estimated cost curve, the agencies have also conducted cost sensitivity studies in their 

respective RIAs in both the proposal and final rule using values of +/-40%. The wide 

variability in the applicability and rigor of the studies also provides justification for continued 

research in this field. 

The agencies consider this DMC to be applicable to the MY2017 and consider mass 

reduction technology to be on the flat portion of the learning curve in the MY2017-2025 

timeframe. To estimate indirect costs for applied mass reduction of up to 15%, the agencies 

have applied a low complexity ICM of 1.24 through 2018 and 1.19 thereafter. To estimate 

indirect costs for applied mass reduction of 15% to 25%, the agencies have applied a medium 

complexity ICM of 1.39 through 2024 and 1.29 thereafter. To estimate indirect costs for 

applied mass reduction greater than 25%, the agencies believe it is appropriate to apply a 

high1 complexity ICM of 1.56 through 2024 and 1.35 thereafter. 

The agencies sought comment in the draft Joint TSD for the NPRM (p. 210) regarding 

options for realistically and appropriately assessing the degree of feasible mass reduction for 

vehicles in the rulemaking timeframe and the total costs to achieve that mass reduction, but 

got no specific response. The agencies also sought comments on what practical limiting 

factors need to be considered when considering maximum feasible amount of mass reduction; 

the degree to which these limiting factors will impact the amount of feasible mass reduction 
 Technologies Considered in the Agencies’ Analysis

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(in terms of the percent of mass reduction); the best method(s) to assess an appropriate and 

feasible fleet-wide amount mass reduction amount (because each study mainly focuses on a 

single vehicle); etc. In its comments, VW stated that it “projects full vehicle weight reductions 

during the time period of this regulation on average in the order of 7-10%.” VW noted that 

this was lower than the agencies’ estimates in the NPRM of upwards of 20% mass reduction 

for large cars and some trucks, which VW stated may exceed cost effective limits. As stated 

later in this section, the detailed studies sponsored by the agencies suggest that 20% mass 

reduction is likely feasible for the rulemaking period without using exotic materials or highly 

advanced technologies. The accompanying detailed cost analysis indicates that the cost of 

reducing mass by 20% can potentially be economical. The agencies also noted in the NPRM 

that we expected to refine our estimate of both the amount and the cost of mass reduction 

between the NPRM and the final rule based on the agencies’ ongoing work described a later 

section, below. As stated before, due to the limited time and the extensive scope of these 

studies, the agencies did not finish them in time for inclusion in the final rule analysis. 

How effective do the agencies estimate that mass reduction will be? 

A rule of thumb used by researchers and industry, based on testing and simulation, is 

that 10 percent reduction in vehicle mass can be expected to generate a 6 to 8 percent increase 

in fuel economy if the vehicle powertrain and other components are also downsized 

accordingly.102 In the analysis for the MYs 2012-2016 final rule, NHTSA and EPA estimated 

that a 10 percent mass reduction with engine downsizing would result in a 6.5 percent 

reduction in fuel consumption while maintaining equivalent vehicle performance (i.e., 0-60 

mph time, towing capacity, etc.), consistent with estimates in the 2002 NAS report. For small 

amounts of mass reduction, such as the 1.5 percent used at vehicle refresh in NHTSA’s 

modeling, no engine downsizing was used, so a 10 percent mass reduction without engine 

downsizing was assumed to result in a 3.5 percent reduction in fuel consumption. In this 

FRM, both agencies have chosen to use the effectiveness value for mass reduction from 

EPA’s lumped parameter model to maintain consistency. EPA’s lumped parameter modelestimated mass reduction effectiveness is based on a simulation model developed by Ricardo, 

Inc. under contract to EPA. The 2011 Ricardo simulation results show an effectiveness of 5.1 

percent for every 10 percent reduction in mass. NHTSA has assumed that for mass reduction 

amounts less than 10 percent, the effectiveness is 3.5 percent. For mass reduction greater than 

10 percent, NHTSA estimates the effectiveness is 5.1 percent in order to avoid double 

counting benefits – because the effectiveness of engine downsizing is included in the 

effectiveness of the engine decision tree when applying engine downsizing, it should 

appropriately be removed from the mass reduction effectiveness value in the mass reduction 

decision tree. EPA applies an effectiveness of 5.1 percent for every 10 percent mass 

reduction, and this scales linearly from 0 percent mass reduction, up to the maximum applied 

mass reduction for any given vehicle, which in this final rule is never larger than 20 percent.

What additional studies are the agencies conducting to inform our estimates of mass 

reduction amounts, cost, and effectiveness? 

In the MYs 2012-2016 final rule, the agencies stated that there are several areas 

concerning vehicle mass reduction and vehicle safety on which the agencies would focus their 

research efforts and undertake further study. The following vehicle level projects focus on the 
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goals stated in the MYs 2012-2016 final rule, which include determining the maximum 

potential for mass reduction in the MY 2017-2025 timeframe by using advanced materials and

improved designs while continuing to meeting safety regulations and voluntary guidelines and 

while maintaining all aspects of vehicle functionality. The fourth study investigates the 

effects of resultant study designs on fleet safety by evaluating crash performance with objects 

and other vehicles of different size and mass. 

1. NHTSA sponsored mass reduction study on a Honda Accord 

2. EPA sponsored mass reduction study on a Toyota Venza (Phase 2 Low 

Development) 

3. California Air Resources Board mass reduction study on a Toyota Venza (Phase 2 

High Development) 

4. NHTSA fleet-wide simulation study - crash analysis using the resultant designs 

from the studies 1-3 with objects and the design models of other vehicles with 

different size and mass. 

Due to the extensive scope of work for these studies and tight time schedule, some of the 

studies were finished, but peer reviews and response to peer reviews were not completed in 

time to enable the results to inform the final rule. We note, however, that the intermediate 

results from the mass reduction studies would corroborate the level of feasible amount of 

mass reduction the agencies chose to apply in the NPRM and FRM analyses. Rulemaking 

modeling results show that the costs for mass reduction are not sensitive to the cost curve of 

the rulemaking. In the NPRM, EPA found that a +/- 40% change in the cost of mass 

reduction had very little impact on the cost of the program. This is largely because of safety 

restraints imposed in the amount of mass reduction selected for the various vehicle classes 

primarily drive the penetration rates of the technology, rather than the relative costeffectiveness of the technology itself. 

The following sections describe the status and results of the studies sponsored by the 

agencies. 

NHTSA Sponsored Mass Reduction Study

BACKGROUND: NHTSA awarded a contract in December 2010 to Electricore, with 

EDAG and George Washington University (GWU) as subcontractors, to study the maximum 

feasible amount of mass reduction for a mid-size car – specifically, a Honda Accord - while 

keeping the vehicle functionality the same as the baseline vehicle. The 

Electricore/EDAG/GWU project team was charged with maximizing the amount of mass 

reduction using technologies that are considered feasible for production of 200,000 units per 

year during the time frame of this rulemaking while maintaining retail price in parity (within 

±10%) with the baseline vehicle. In addition, all designs, materials, technologies and 

manufacturing processes must be realistically projected to be feasible for industry-wide 

application in MYs 2017-2025. The project focused on mass reduction and allowed 

powertrain downsizing, however alternative powertrains, such as diesels, HEVs and EVs, 

were not to be considered. 
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MATERIAL AND TECHNOLOGY SELECTION: For vehicle redesigns, OEMs 

normally select technologies, materials and manufacturing processes that are currently in use 

on existing vehicle platforms or planned to be in use on future vehicle platforms. The use of 

the same or similar technologies, materials and manufacturing processes helps maintain or 

improve component and vehicle reliability, manufacturability and cost. New materials, 

technologies and processes are often introduced in low-volume, high price vehicles first and 

then migrate to high production volume vehicle lines over time. This significantly reduces the 

risk to OEMs associated with implementing new technologies. Recognizing this when 

selecting materials, technologies and manufacturing processes, the Electricore/EDAG/GWU 

team utilized, to the extent possible, only those materials, technologies and design which are 

currently used or planned to be introduced in the near term (MY 2012-2015) on low-volume 

production vehicles. The recommended materials (Advanced High Strength Steels, 

Aluminum, Magnesium and Plastics) manufacturing processes (Stamping, Hot Stamping, Die 

Casting, Extrusions, Roll Forming) and assembly methods (Spot welding, Laser welding and 

Adhesive Bonding) are at present used, some to a lesser degree than others. These 

technologies can be fully developed within the normal product design cycle using the current 

design and development methods. The process parameters for manufacturing with Advanced 

High Strength Steels can be supported by computer simulation. This approach minimized 

those material and technology options which would likely be overly aggressive or unrealistic 

to implement in mass production in model years 2017-2025. 

ENGINEERING APPROACH: The Electricore/EDAG/GWU team took a “clean 

sheet of paper” approach and adopted collaborative design, engineering and CAE process 

with built-in feedback loops to incorporate results and outcomes from each of the design steps 

into the overall vehicle design and analysis. The team torn down and benchmarked 2011 

Honda Accord and then undertook a series of baseline, noting the designs, materials, 

technologies and overall design optimization level of the baseline vehicle. Vehicle 

performance, safety simulation and cost analyses were run in parallel to the design study to 

help ensure that the design decisions for the concept vehicle would be informed by a welldocumented baseline, thus enabling the resultant design to meet the defined project criteria. 

While working within the constraint of maintaining the baseline Honda Accord’s 

exterior size and shape, the body structure was first redesigned using topology optimization 

with six load cases including bending stiffness, torsion stiffness, IIHS frontal impact, IIHS 

side impact, FMVSS pole impact, FMVSS rear impact and FMVSS roof crush cases. The 

load paths from topology optimization were analyzed and interpreted by technical experts and 

the results were then fed into low fidelity 3G (Gauge, Grade and Geometry) optimization 

programs to further optimize for material properties, material thicknesses and cross-sectional 

shapes while trying to achieve the maximum amount of mass reduction. The 

Electricore/EDAG/GWU team carefully reviewed the optimization results and built detailed 

CAD/CAE models for the body structure, closures, bumpers, suspension, and instrumentation 

panel. The vehicle designs were also carefully reviewed by manufacturing technical experts to 

ensure that they could be manufactured at high volume production rates. Detailed 

manufacturing layouts were created and were later used to estimate costs. 

Multiple materials were used for this study. The body structure was redesigned using a 

significant amount of advanced high strength steel (AHSS). The closure and suspension were 
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designed using a significant amount of aluminum. Magnesium was used for the 

instrumentation cross-car beam. A limited amount of composite material was used for the seat 

structure. Electricore and its sub-contractors consulted industry leaders and experts for each 

component and sub-system when deciding which mass reduction technologies were feasible. 

DESIGN AND FUNCTION VALIDATION: In order to ensure that the light weighted 

vehicle had the same functionality as the baseline vehicle, Electricore and its sub-contractors 

used the CAD/CAE/powertrain models and conducted simulation modeling. This is the first 

mass reduction study that has been released publicly that includes such a broad array of 

vehicle simulation modeling analyses to assess vehicle functionality and performance relative 

to these critical attributes. These significant additional analyses provide greater confidence 

that the designs employed in this study are more feasible for production implementation than 

a study without these analyses, although the agency notes that significantly more testing and 

validation work is required to refine and finalize a design for production. 

Safety: Safety performance of the light-weighted design is compared to the safety 

rating of the baseline MY2011 Honda Accord for seven consumer information and 

federal safety crash tests using LS-DYNAiii. These seven tests are NCAP frontal test, 

NCAP lateral MDB test, NCAP lateral pole test, IIHS roof crush, IIHS lateral MDB, 

IIHS front offset test, and FMVSS No. 301 rear impact tests. All tests achieved safety 

performance equivalent to MY 2011 Honda Accord when comparing crash pulse and 

passenger compartment intrusion levels, with no damage to the fuel tank. This study 

does not include restraint systems and dummy which would be part of NHTSA’s fleet 

simulation study. 

Body Stiffness/ Ride and Handling/NVH: Vehicle body torsional and bending 

stiffness are signatures for the vehicle structure performance. Higher stiffness is 

generally associated with a refined ride and handling qualities. The baseline vehicle 

body structure underwent testing for normal modes of vibration, and torsion and 

bending stiffness. A detailed FEA model of the light-weighted structure was created 

and analyzed using the MSC/NASTRAN simulation. The torsional stiffness of the 

light-weighted design is 30% higher than the baseline vehicle while the bending 

stiffness is 40% higher. The normal mode frequency test results for the light-weighted 

body structure, which represents vehicle dynamic stiffness, also are within 2.3% of the 

targets. These stiffness and modes results show that the light-weighted design will 

have improved ride and handling and improved NVH performance comparing to a 

vehicle with lower stiffness. 

Vehicle Ride and Handling: In the light-weighted design, the front suspension is 

redesigned using a MacPherson strut instead of the heavier double wishbone used in 

 

iii LS-DYNA is a software developed by Livermore Software Technologies Corporation used widely by industry 

and researchers to perform highly non-linear transient finite element analysis. 
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the baseline vehicle. Vehicle ride and handling is evaluated using MSC/ADAMSjjj

modeling on five maneuvers, fish-hook test, double lane change maneuver, pothole 

test, 0.7G constant radius turn test and 0.8G forward braking test. The results from the 

fish-hook test show that the light-weighted vehicle can achieve a five-star rating for 

rollover, same as baseline vehicle. The double lane change maneuver tests according 

to the ISO standard show that the chosen suspension geometry and vehicle parameter 

of the light-weighted design are within acceptable range for safe high speed 

maneuvers. These simulations are performed to further validate the chosen light 

weighted front suspension design. 

Durability: There are two types of durability, stress related and corrosion related. 

Stress related durability for the light-weighted vehicle is evaluated using strain-based 

analysis based on pot hole, 0.8G forward braking and 0.7G cornering road load cases 

using ADAMS model. Results from the simulation show that the life of the lightweighted vehicle body structure exceeds the targets. Although timing and funding did 

not allow corrosion testing to be conducted, the Electricore/EDAG/GWU team 

considered the properties of materials used, and the location and the functionality of 

the components to avoid potential issues with corrosion. 

Powertrain Performance: The powertrain of the light-weighted vehicle is downsized 

from 2.4L naturally aspirated engine to 1.8L naturally aspirated engine to maintain the 

same vehicle acceleration and towing compared to the baseline 2011 Honda Accord. A 

powertrain simulation tool PSATkkk is used to verify and validate the light-weighted 

vehicle for fuel economy and powertrain performance. The light-weighted vehicle 

with 1.8L NA engine will have 32 mpg fuel economy with comparable 0-30 mph time, 

0-60 mph time, quarter mile time, gradability and maximum speed at grade. The only 

metrics that the light-weighted vehicle performs less than the baseline vehicle is 

vehicle maximum speed (127 mph for the baseline Accord and 112 mph for the lightweighted design) which the Electricore/EDAG/GWU team and NHTSA believe is 

acceptable. As a result of the improved fuel economy, the fuel tank for the lightweighted vehicle can be reduced from 18.5 gallon to 15.8 gallon with the same driving 

range, which further reduced vehicle weight both by reducing fuel tank mass and the 

mass of fuel carried by the vehicle. 

Manufacturability: The manufacturability of all proposed body structure panels were 

then assessed using simulation tools, which included HYPER-FORM for stamping 

parts, and other single step process simulation tools for parts manufactured using other 

methods, such as hot stamping for B-pillar. 

 

jjj MSC/ADAMS: Macneal-Schwendler Corporation/Automatic Dynamic Analysis of Mechanical Systems. 

kkk PSAT is a plug-and-play architecture software that allows the user to build and evaluate a vehicle's fuel 

economy and powertrain performance under varying load conditions and drive cycles. It uses MATLAB in a 

Simulink environment to record data, calculate and input powertrain requirements based on driver demand and 

current powertrain values. The software is sponsored by the U.S. Department of Energy and developed by 

Argonne National Laboratory (ANL). http://www.transportation.anl.gov/modeling_simulation/PSAT/index.html 
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COST ANALYSIS; A detailed cost analysis for the light weighted design and cost 

estimates for alternative design options were also conducted. For OEM-manufactured parts, a 

detailed cost model was built based on a Technical Cost Modeling (TCM) approach 

developed by the Massachusetts Institute of Technology (MIT) Materials Systems 

Laboratory’s research103 for estimating the manufacturing costs of OEM parts. The costs 

were broken down into each of the operations involved in the manufacturing, such as for a 

sheet metal part production by starting from blanking the steel coil, until the final operation to 

fabricate the component. Total costs were then categorized into fixed cost, such as tooling, 

equipment, and facilities; and variable costs such as labor, material, energy, and maintenance. 

These costs were assessed through an interactive process between the product designer, 

manufacturing engineers and cost analysts. For OEM-purchased parts, the costs were 

estimated by consultation with experienced cost analysts and Tier 1 suppliers. Forty-one 

concise spreadsheets are created for both the baseline vehicle and the light-weighted design in 

the cost model to calculate both the manufacturing and assembly costs. 

FINAL RESULTS: To achieve the same vehicle performance as the baseline vehicle, 

the size of the engine for the light-weighted vehicle was proportionally reduced from 2.4L177 HP to 1.8L-140HP. Overall the complete light weight vehicle achieved a total weight 

savings of 22 percent (332kg) relative to the baseline vehicle (1480 kg) at an incremental cost 

increase of $319 or $0.96 per kg. Without the mass and cost reduction allowance for the 

powertrain (including engine, transmission, fuel system, exhaust system and fuel) the mass 

saving for the ‘glider’ is 24 percent (264 kg) at mass saving cost premium of $1.63 per kg of 

mass saving. The Electricore/EDAG/GWU team also developed a cost curve to cover a range 

of mass reduction levels from 0% to 28% for both the full vehicle with engine downsizing and 

for the glider only. When developing the cost curves, the project team used data that were 

developed in the study to derive a mass compounding factor (secondary mass reduction/total 

mass reduction), which was determined to be 0.7. The cost curves are shown in Figure 3-34

and Figure 3-35. 
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Figure 3-34 Mass Reduction Cost with Allowance for Powertrain Downsizing 

Figure 3-35. Mass Reduction Cost for the Glider Only 
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PEER REVIEW: The study has been peer reviewed by three technical experts from 

the industry, academia and a DOE national lab. In the peer reviewer charge letter, the agency 

asked the peer reviewers to comment on the following five specific items as well as any other 

potential areas for comments. 

Assumptions and data sources 

Vehicle design and optimization methodology and its rigorousness 

Vehicle functionality and crashworthiness testing methodological rigor 

Vehicle manufacturing cost methodology and its rigorousness 

Conclusions and findings 

Comments from peer reviewers were generally positive. The peer reviewers concurred 

with the methodologies employed in the study and the technologies applied to the lightweighted design, although one peer reviewer commented that not enough composite materials 

were used in the design. One peer reviewer stated in his comments that “the main findings 

appear to be based on sound economic and engineering principles.” The peer reviewers stated 

that the cost estimates developed in the study, particularly based on the TCM model, seem to 

be reasonable, with one peer reviewer commenting the final cost is on the lower side and 

another commenting it is on the higher side. All three peer reviewers looked into the details of 

the CAE and cost modeling. One significant concern identified in the peer review was 

whether the light-weighted vehicle maintained the same performance level in the NCAP side 

MDB test. In response to that concern, the Electricore/EDAG/GWU team conducted 

simulation testing and revised the B-pillar design, increasing the gauge for the steel for better 

performance. Because NCAP only measures injuries to dummies and the crash performance 

of the light-weighted design is based on the vehicle center of gravity crash pulse level, Bpillar velocity and passenger compartment and intrusion, to assess correlation of the model 

performance to the baseline vehicle, NHTSA asked a contractor who performs NHTSA’s 

NCAP testing to take additional measurements of the interior intrusion for the 2011 baseline 

Honda Accord. The updated design and the Honda Accord test data showed similar intrusion 

results for both NCAP and IIHS side impact tests, and those results support that the lightweighted design could possibly achieve similar NCAP and IIHS ratings, especially when the 

structure design is fine tuned with the restraint system design which NHTSA will study in the 

fleet simulation study described later on in this section. For other peer review comments, the 

Electricore/EDAG/GWU team addressed the comments fully in the report and also composed 

a response to peer review comment document, which is included at the end of the report. The 

final report104, CAE model and cost model, and peer review comments105 are available in 

Docket No. NHTSA-2010-0131 and can also be found on NHTSA’s websitelll. 

EPA Sponsored Mass Reduction Study

EPA, along with ICCT, funded a contract with FEV, with subcontractors EDAG (CAE 

modeling) and Munro & Associates, Inc. (component technology research) to study the 

 
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feasibility, safety and cost of 20% mass reduction on a 2017-2020 production ready mid-size 

crossover utility vehicle (CUV) specifically, a Toyota Venza while maintaining cost parity or 

reduction. The EPA report is entitled “Light-Duty Vehicle Mass-Reduction and Cost Analysis 

– Midsize Crossover Utility Vehicle”.106 This study is a Phase 2 study of the low development 

design in the 2010 Lotus Engineering study “An Assessment of Mass Reduction 

Opportunities for a 2017-2020 Model Year Vehicle Program”107, herein described as “Phase 

1”. 

Results for the EPA Phase 2 study of the 1710kg 2010 Toyota Venza include an 18% 

mass reduction (with powertrain), 312kg, at -$0.43/kg cost (cost savings), including tooling. 

While the results for $/kg appear similar between the Phase 1 Lotus study (without 

powertrain, 19% mass reduction, 246kg, at -$0.44/kg), it should be noted that each study took 

slightly different approaches. The Phase 1 study included mass reduction of every system 

except the powertrain. The EPA Phase 2 study focused on the vehicle as a whole (including 

all systems), but also included the powertrain. 

LOTUS PHASE 1 STUDY: The original 2009/2010 Phase 1 effort by Lotus 

Engineering was funded by Energy Foundation and ICCT to generate a technical paper which 

would identify potential mass reduction opportunities for a selected vehicle representing the 

crossover utility segment, a 2009 Toyota Venza. Lotus examined mass reduction for two 

scenarios – a low development (20% MR and 2017 production with technology readiness of 

2014) and high development (40% MR and 2020 production with technology readiness of 

2017). Lotus disassembled a 2009 Toyota Venza and created a bill of materials (BOM) with 

all components. Lotus then investigated emerging/current technologies and opportunities for 

mass reduction. The report included the BOM for full vehicle, systems, sub-systems and 

components as well as recommendations for next steps. The potential mass reduction for the 

low development design includes material changes to portions of the body in white 

(underfloor and body, roof, body side, etc.), seats, console, trim, brakes, etc. The original 

powertrain was changed to a hybrid configuration. The Phase 1 project achieved 19% 

(without the powertrain) at 99% of original cost at full phase-in after peer review comments 

taken into consideration.mmm This was calculated to be -$0.45/kg utilizing information from 

Lotus. 

The Lotus Phase 1 study created a good foundation for the next step of analyses of 

CAE modeling for safety evaluations and in-depth costing (these steps were not within the 

scope of the Phase 1 study) as noted by the peer reviewer recommendations.108

 The study was 

peer reviewed. Mr. Sujit Das, of ORNL and an author of several reports on mass reduction, 

reported that the mass reduction opportunities were reasonable and likely to meet the stated 

objectives. Mr. Das also recommended using a consistent cost methodology. Dr. Malen, a 

professor at the University of Michigan, reported the mass reduction opportunities were 

 

mmm Cost estimates were given in percentages – no actual cost analysis was presented for it was outside the scope 

of the study, though costs were estimated by the agency based on the report. 
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reasonable and likely to meet the stated objectives and also recommended a data driven 

methodology that can be examined at each step of the analysis.109 

OBJECTIVES OF EPA PHASE 2 STUDY: The study works to maximize the amount 

of mass reduction with technologies and techniques that are considered feasible in 

manufacturability and cost effective for a MY 2017 high volume production vehicle. The 

EPA Phase 2 study includes the creation of several CAE body in white (BIW) models which 

could be used to analyze body stiffness, NVH modal characteristics (overall torsion mode, 

overall lateral bending mode, rear end match boxing mode and overall vertical bending rear 

end mode in addition to overall and bending and torsional stiffness) and crash (FMVSS and 

NCAP) performance. The study also includes a rigorous cost analysis including tooling and 

piece cost. The in-depth cost analysis utilizes several cost models including the one described 

in the NHTSA project above. In addition, EPA expanded the scope of the work to include an 

updated look (2012) at all of the mass reduction technologies and techniques so that FEV was 

not limited to only the ideas originally generated by Lotus which were determined in 2009. 

As part of this EPA Phase 2 study, FEV/EDAG analyzed the BIW ideas from Lotus’s Phase 1 

study through CAE modeling and FEV included the technologies for mass reduction with the 

information provided in the Phase 1 Lotus Engineering report for the low development 

scenario. 

VERIFICATION OF THE LOTUS BIW DESIGN FOR NVH: Similar to Lotus Phase 

1 study, the EPA Phase 2 study begins with vehicle tear down and BOM development. FEV 

and its subcontractors tore down a MY 2010 Toyota Venza in order to create a BOM as well 

as understand the production methods for each component. Approximately 140 coupons from 

the BIW were analyzed in order to understand the full material composition of the baseline 

vehicle. A baseline CAE model was created based on the findings of the vehicle teardown 

and analysis. The model’s results for static bending, static torsion, and modal frequency 

simulations (NVH) were obtained and compared to actual results from a Toyota Venza 

vehicle. After confirming that the results were within acceptable limits, this model was then 

modified to create light-weighted vehicle models. EDAG reviewed the Lotus Phase 1 low 

development BIW ideas and found redesign was needed to achieve the full set of acceptable 

NVH characteristics. EDAG utilized a commercially available computerized optimization tool 

called HEEDS MDO to build the optimization model. The model consisted of 484 design 

variables, 7 load cases (2 NVH + 5 crash), and 1 cost evaluation. The outcome of EDAG’s 

lightweight design optimization included the optimized vehicle assembly and incorporated the 

following while maintaining the original BIW design: optimized gauge and material grades 

for body structure parts, laser welded assembly at shock towers, rocker, roof rail, and rear 

structure subassemblies, aluminum material for front bumper, hood, and tailgate parts, TRBs 

on B-pillar, A-pillar, roof rail, and seat cross member parts, design change on front rail side 

members. EDAG achieved 13% mass reduction in the BIW including closure. If aluminum 

doors were included then an additional decrease of 28kg could be achieved for a total of 18% 

mass reduction from the body structure. All other systems within the vehicle were examined 

for mass reduction, including the powertrain (engine, transmission, fuel tank, exhaust, etc.). 

FEV and Munro incorporated the Lotus Phase 1 low development concepts into their own 

idea matrix. Each component and sub-system chosen for mass reduction was scaled to the 

dimensions of the baseline vehicle, trying to maximize the amount of mass reduction with 
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cost effective technologies and techniques that are considered feasible and manufacturable in 

high volumes in MY2017. FEV included a full discussion of the chosen mass reduction 

options for each component and subsystem. 

UPDATE RESEARCH ON MASS REDUCTION TECHNOLOGIES: FEV and 

Munro created a BOM based on the teardown analysis. Mass reduction technology review 

was conducted at the system and sub-system level. The staff at FEV and Munro consists of 

experts from the automotive industry and discussion also included outside venders of mass 

reduction technologies. Forty of the 150 Lotus Phase 1 concepts were included in the final 

mass reduction technology selection. SAFETY FEASIBILITY: Safety performance of the 

baseline and light-weighted designs (Lotus Phase 1 low development and the final EPA Phase 

2 design) were evaluated by EDAG through their constructed detailed CAD/CAE vehicle 

models. Five federal safety crash tests were performed, including FMVSS flat frontal crash, 

side impact, rear impact and roof crush (using IIHS resistance requirements) as well as Euro 

NCAP/IIHS offset frontal crash. Criteria including the crash pulse, intrusion and visual crash 

information were evaluated to compare the results of the light weighted models to the results 

of the baseline model (which had been compared qualitatively to the available actual NHTSA 

crash results of the Venza). Potential compliance with safety and performance of the light 

weighted CAE model in FMVSS and NCAP tests was inferred using quantitative 

measurements of vehicle delta velocity and intrusion. The light weighted vehicle achieved 

equivalent safety performance in all tests to the baseline model with no damage to the fuel 

tank. In addition, CAE was used to evaluate the BIW vibration modes in torsion, lateral 

bending, rear end match boxing, and rear end vertical bending, and also to evaluate the BIW 

stiffness in bending and torsion. 

COST ANALYSIS: The development of a bill of materials (BOM), on systems and 

sub-systems by FEV and Munro, was the basis for the cost analysis. This methodology is 

consistent with the peer reviewed approach described earlier in this chapter. The cost for the 

mass reduced technologies were developed by determining the difference in cost for those 

new components compared to the old, and under the assumption of production scales of 

200,000 units (appropriate for the Venza global production). FEV and Munro developed 

several thousand cost spreadsheets as the basis for the cost analyses for the mass reduction 

technologies and the BIW and closures. Costs include manufacturing (material, labor, 

burden) and markup (end item scrap, Sales, General and Administrative (SG&A), Profit, 

Engineering, Development and Testing (ED&T) and Research and Development (R&D)). A 

separate tooling cost analysis was also performed and at 18% mass reduction calculated a 

$0.05/kg for tooling. The cost analysis of the BIW and closures were done by EDAG and 

were based on a Technical Cost Modeling (TCM) approach developed by the Massachusetts 

Institute of Technology (MIT) Materials Systems Laboratory’s research110. 
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RESULTS: The light-weighting effort achieved an 18% mass reduction (with 

downsized powertrainnnn) on the base 1710 kg Toyota Venza at a cost of $-0.43/kg (a cost 

savings) which includes tooling (cost increase of $0.04/kg). A cost curve was developed to 

show the estimated $/kg over a variety of mass reduction levels utilizing the subset of 

technologies and techniques developed throughout the study (see Figure 3-36). The two 

curves represent non-compounded mass reduction technologies (“primary”) and compounded 

mass reduction scenario (a total of “primary” and “secondary”). These curves were 

determined by reviewing the BOM part by part and identifying the parts within systems that 

would benefit from mass reduction and be able to utilize mass compounding. It is important 

to note that the potential for secondary mass reduction was evaluated at many points along the 

whole cost curve. The cost curve was used to determine a value for the average cost per 

kilogram of cumulative mass reduction (in terms of $/kg for mass reduction at a specific mass 

reduction level). 

Figure 3-36 Cost Curve for the 2010 Toyota Venza – EPA Study (FEV/EDAG/Munro) 

PEER REVIEW: The peer review comments for this study were generally positive 

and concurred with the ideas and methodology of the EPA study. The documents for the peer 

review can be found in EPA docket EPA-HQ-OAR-2010-0799. After accounting for peer 

 

nnn The engine was downsized and downweighted, however the number of cylinders remained the same and it 

remained naturally aspirated. 
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review comments to the draft report, mass reduction decreased by 0.5% and though some of 

the adjustments resulted in a cost savings, the overall cost increased slightly. Changes to the 

BIW CAE models resulted in minimal differences.

There were many positive comments about the report. While the report included mass 

reduction and cost analyses for several hundred items, there were some concerns identified in 

the peer review comments that influenced the overall amount of mass reduction and the cost. 

These included 1) engine magnesium block cost, 2) the (brake) rotor design, 3) aluminum 

hollow suspension stabilizer bar, and 4) the closure aluminum material cost.

There were several areas where peer reviewers suggested changes that did not impact percent 

mass reduction or cost. First, more information was included to better describe the wheel 

mass technology. Second the BIW models were updated to eliminate the inconsistencies in 

material assignments - revising the number of through thickness integration points for the 

shell elements and correcting the asymmetrical thickness assignments. Finally, the baseline 

and optimized BIW models were further refined to include definitions of welding properties, 

transverse shear scale factor, element type, element formulation and material failure criteria. 

Based on these updates the crash models were rerun (resulting in statistically insignificant 

change and the results included in the final report.

California Air Resources Board Sponsored Mass Reduction Study: The California Air 

Resources Board (CARB) funded a study with Lotus Engineering to further develop the high 

development design from Lotus’ 2010 Toyota Venza work (“Phase 1”). The CARBsponsored Lotus “Phase 2” study provides the updated design, crash simulation results, 

detailed costing, and analysis of the manufacturing feasibility of the BIW and closures. Based 

on the findings of the safety validation work, Lotus made revisions to strengthen the vehicle 

structure through the use of a more aluminum-intensive BIW (and with less magnesium). In 

addition to the increased use of advanced materials, the new design by Lotus included a 

number of instances in which multiple parts were integrated, resulting in a reduction in the 

number of manufactured parts in the lightweight BIW. The Phase 2 study reports that the 

number of parts in the BIW was reduced from greater than 250 to less than 170. The BIW 

was analyzed for torsional stiffness and crash test safety with Computer-Aided Engineering 

(CAE). The new design’s torsional stiffness was 32.9 kNm/deg, which is higher than the 

baseline vehicle and comparable to more performance-oriented models. The analysis 

included validation of the lightweight vehicle design for standard FMVSS/IIHS front, side, 

rear, offset, roof, intrusion, and seatbelt safety tests. Crash tests simulated in CAE showed 

results that were acceptable for all crash tests analyzed. No comparisons or conclusions were 

made if the vehicle performed better or worse than the baseline Venza. For FMVSS 208 

frontal impact, Lotus based its CAE crash test analyses on vehicle crash acceleration data 

rather than occupant injury as is done in the actual vehicle crash. The report from the study 

stated that accelerations were within acceptable levels compared to current production vehicle 

acceleration results and it should be possible to tune the occupant restraint system to handle 

the specific acceleration pulses of the Phase 2 high development vehicle. FMVSS 210 

seatbelt anchorages is concerned with seatbelt retention and certain dimensional constraints 

for the relationship between the seatbelts and the seats. Overall both the front and rear 

seatbelt anchorages met the requirements specified in the standard. FMVSS 214 side impact 
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show the energy is effectively managed. Since dummy injury criteria was not used in the 

CAE modeling, a maximum intrusion tolerance level of 300mm was instituted which is the 

typical distance between the door panel and most outboard seating positions. For example, 

the Phase 2 design was measured at 115mm for the crabbed barrier test. The side pole test 

resulted in 120mm intrusion for the 5th percentile female and intrusion was measured at 

190mm for the 50th percentile male. The report stated FMVSS 216 roof crush simulation 

shows the Phase 2 high development vehicle will meet roof crush performance requirements 

under the specified load case of 3 times the vehicle weight. For the FMVSS rear impact, 

results show plastic strain in the fuel tank/system components to be less than 3.5%, which is 

less than the 10% strain allowed in the test. The pressure change in the fuel tank is less than 

2% so risk of tank splitting is minimal. The IIHS low speed front and rear show no body 

structural issues, however styling adjustments should be made to improve the rear bumper 

low speed performance.

The cost analysis for the Phase 2 lightweight design involved new piece, tooling, and 

assembly work on the BIW and closures, and the technologies and costs for the non-BIW 

components were carried over from the Phase 1 work. The Lotus design achieved a 37% (141 

kg) mass reduction in the body structure, a 38% (484kg) mass reduction in the vehicle 

excluding the powertrain, and a 32% (537 kg) mass reduction in the entire vehicle including 

the powertrain. The Phase 2 report included an investigation into the manufacturing and 

assembly processes to assess whether the low mass aluminum BIW design can feasibly and 

cost-effectively be constructed for 60,000 units. Lotus found that the assembly and tooling 

cost savings, due to the lower number of BIW parts, relative to the base Venza partially offset 

the 60% increase in piece costs for the BIW for a resulting BIW cost increase of $239. 

Accounting for all of the other systems (excluding the powertrain) using the results from 

Phase I study, the impact is a cost savings of $476 for 484 kg reduced, or -$0.98/kg. For the 

complete vehicle with powertrain (hybrid powertrain), the overall cost savings for the whole 

vehicle including powertrain is $318 for 537 kg reduced, or -$0.59/kg. The hybrid engine 

was downsized from 120hp to 100hp and the corresponding hybrid system related 

components were removed or exchanged for a minimal change in overall mass. The report 

was peer reviewed by a cross section of experts, from academia, a DOE lab, DOE and an 

aluminum industry representative. The peer review comments were addressed in the peer 

review document and were incorporated in the final Phase 2 report. The documents will be 

found on EPA’s website 

http://www.epa.gov/otaq/climate/publications.htm#vehicletechnologies. 

NHTSA Fleet Simulation Study

NHTSA has contracted with GWU to build a fleet simulation model to study the 

impact and relationship of light-weighted vehicle design with injuries and fatalities. This 

study will also include an evaluation of potential countermeasures to reduce any safety 

concerns associated with lightweight vehicles in the second phase. NHTSA has included 

three light-weighted vehicle designs in this study: the one from Electricore/EDAG/GWU 

mentioned above, one from Lotus Engineering funded by California Air Resource Board for 

the second phase of the study, evaluating mass reduction levels around 35 percent of total 

vehicle mass, and one funded by EPA and the International Council on Clean Transportation 

(ICCT). In addition to the lightweight vehicle models, these projects also created CAE 
 Technologies Considered in the Agencies’ Analysis

3-248 

models of the baseline vehicles. To estimate the fleet safety implications of light-weighting, 

CAE crash simulation modeling was conducted to generate crash pulse and intrusion data for 

the baseline and three light-weighted vehicles when they crash with objects (barriers and 

poles) and with four other vehicle models (Chevy Silverado, Ford Taurus, Toyota Yaris and 

Ford Explorer) that represent a range of current vehicles. The simulated acceleration and 

intrusion data were used as inputs to MADYMO occupant models to estimate driver injury. 

The crashes were conducted at a range of speeds and the occupant injury risks were combined 

based on the frequency of the crash occurring in real world data. The change in driver injury 

risk between the baseline and light-weighed vehicles will provide insight into the safety 

performance these light-weighting design concepts. This is a large and ambitious project 

involves several stages over several years. NHTSA and GWU have completed the first stage 

of this study. The frontal crash simulation part of the study is being finished and will be peer 

reviewed. The report for this study will be available in NHTSA-2010-0131. Information for 

this study can also be found at NHTSA’s websiteooo

. 

The countermeasures section of the study is expected to be finished in early 2013. This 

phase of the study is expected to provide information about the relationship of light-weighted 

vehicle design with injuries and fatalities and to provide the capability to evaluate the 

potential countermeasures to safety concerns associated with light-weighted vehicles. NHTSA 

plans to include the following items in future phases of the study to help better understanding 

the impact of mass reduction on safety. 

Simulation of crashes between two light-weighted concept vehicles; 

Additional crash configurations, such as side impact, oblique and rear impact 

tests; 

Risk analysis for elderly and vulnerable occupants; 

Safety of light-weighted concept vehicles for different size occupants. 

Partner vehicle protection in crashes with other light-weighted concept 

vehicles; 

While this study is expected to provide information about the relationship of light-weighted 

vehicle design with injuries and fatalities and to provide meaningful information to NHTSA 

on potential countermeasures to reduce any safety concerns associated with lightweight 

vehicles, because this study cannot incorporate all of the variations in vehicle crashes that 

occur in the real world, it is expected to provide trend information on the effect of potential 

future designs on highway safety, but is not expected to provide information that can be used 

to modify the coefficients derived by Kahane that relate mass reduction to highway crash 

fatalities. Because the coefficients from the Kahane study are used in the agencies’ 

assessment of the amount of mass reduction that may be implemented with a neutral effect on 

highway safety, the fact that the fleet simulation modeling study is not complete does not 

affect the agencies’ assessment of the amount of mass reduction that may be implemented 

with a neutral effect on safety. 

 

ooo Website for fleet study can be found at http://www.nhtsa.gov/fuel-economy. 
 Technologies Considered in the Agencies’ Analysis

3-249 

Safety considerations in establishing CAFE/GHG standards along with discussion of 

NHTSA’s February 25, 2011, mass-size-safety workshop at DOT headquarters, can be found 

in Section II.G of the preamble for this final rule. NHTSA intends to host additional 

workshops when the studies have reached a sufficient level of completion, to share the results 

with the public and continue the fruitful ongoing public dialogue on these issues. 

3.5 How did the agencies consider real-world limits when defining the rate at which 

technologies can be deployed? 

3.5.1 Refresh and redesign schedules 

During MYs 2017-2025 manufacturers are expected to go through the normal 

automotive business cycle of redesigning and upgrading their light-duty vehicle products, and 

in some cases introducing entirely new vehicles not in the market today. The MY 2017-2025 

standards timeframe allows manufacturers the time needed to incorporate GHG reduction and 

fuel-saving technologies into their normal business cycle while considering the requirements 

of the MY 2012-2016 standards. This is important because it has the potential to avoid the 

much higher costs that could occur if manufacturers need to add or change technology at 

times other than their scheduled vehicle redesigns. This time period also provides 

manufacturers the opportunity to plan for compliance using a multi-year time frame, again 

consistent with normal business practice. Over these 9 model years, and the 5 prior model 

years that make up the 2012-2016 standards, there will be an opportunity for manufacturers to 

evaluate, presumably, every one of their vehicle platforms and models and add technology in 

a cost effective way to control GHG emissions and improve fuel economy. This includes all 

the technologies considered here and the redesign of the air conditioner systems in ways that 

will further reduce GHG emissions and improve fuel economy. 

Because of the complexities of the automobile manufacturing process, manufacturers 

are generally only able to add new technologies to vehicles on a specific schedule; just 

because a technology exists in the marketplace or is made available, does not mean that it can 

be immediately applied on all of a manufacturer’s vehicles. In the automobile industry there 

are two terms that describe when technology changes to vehicles occur: redesign and refresh 

(i.e., freshening). Vehicle redesign usually refers to significant changes to a vehicle’s 

appearance, shape, dimensions, and powertrain. Redesign is traditionally associated with the 

introduction of “new” vehicles into the market, often characterized as the “next generation” of 

a vehicle, or a new platform. Across the industry, redesign of models generally takes place 

about every 5 years. However, while 5 years is a typical design period, there are many 

instances where redesign cycles can be longer or shorter. For example, it has generally been 

the case that pickup trucks and full size vans have longer redesign cycles (e.g., 6 to 7 years), 

while high-volume cars have shorter redesign cycles in order to remain competitive in the 

market. There are many other factors that can also affect redesign such as availability of 

capital and engineering resources and the extent of platform and component sharing between 

models, or even manufacturers. 

Vehicle refresh usually refers to less extensive vehicle modifications, such as minor 

changes to a vehicle’s appearance, a moderate upgrade to a powertrain system, or small 

changes to the vehicle’s feature or safety equipment content. Refresh is traditionally 
 Technologies Considered in the Agencies’ Analysis

3-250 

associated with mid-cycle cosmetic changes to a vehicle, within its current generation, to 

make it appear “fresh.” Vehicle refresh generally occurs no earlier than two years after a 

vehicle redesign or at least two years before a scheduled redesign. For the majority of 

technologies discussed today, manufacturers will only be able to apply them at a refresh or 

redesign, because their application would be significant enough to involve some level of 

engineering, testing, and calibration work. 

Most vehicles would likely undergo two redesigns during the 2017-2025 period. Even 

with the potential of multiple refresh and redesign cycles, it is still likely that some of the 

more advanced and costly technologies (such as cooled boosted EGR engines, or advanced 

(P)HEVs) may not be able to be fully implemented within the timeframe of this rule. These 

limitations are captured in “phase-in caps,” discussed in the next section, and “maximum 

technology penetration rates” within the modeling analysis. 

The broad technology classes evaluated for purposes of this analysis are defined below 

and a brief discussion of the limiting factors considered are presented. 

Conventional Spark Ignition (SI) - This technology category includes all 

technologies, such as gasoline direct injection engines, cylinder deactivation, six 

and eight speed automatic and dual clutch transmissions, and start-stop microhybrid technology that are not contained in other categories. Many of these 

technologies were anticipated as being available in the MYs 2012-2016 time frame 

in the recent NHTSA and EPA final rule, and it is expected manufacturers could 

expand production to all models by model year 2025. Conventional SI also 

includes turbocharged and downsized engines and turbocharged and downsized 

engines that include cooled EGR with additional levels of boost and a larger 

degree of engine downsizing than seen in the current light-duty gasoline fleet. 

These latter technologies are similar to the technologies that many OEMs indicated 

were under development and which they anticipate will be introduced into the 

market in the 2017-2025 time frame. 

Hybrid – While the agencies recognize there are many types of full-hybrids either 

in production or under development, for the purposes of this analysis we have 

specifically modeled the P2 type hybrid, as explained in section 3.4.3.6.3. While 

the agencies expect the proliferation of these vehicles to increase in this timeframe, 

the maximum technology penetration rate and phase-in caps are set at less than 

100% in MY 2025 due to industry-wide engineering and capacity constraints for 

converting the entire new vehicle fleet to strong hybrids (like P2 and others) in this 

time frame. As described above, these technologies (along with PHEVs and EVs) 

require a significant cost and complexity, and thus are not expected to be able to 

be fully phased into the 2017-2025 fleet like other more conventional (but 

advanced) engines. 

Plug-in Hybrid (PHEV) - In the agencies’ analyses, this technology includes 

PHEVs with a range of 20, 30 and 40 miles. The maximum technology 

penetration rates and phase-in caps are set at less than 100% in MY 2025 due to 

the same general potential constraints as listed for the HEVs, but are lower for 
 Technologies Considered in the Agencies’ Analysis

3-251 

PHEVs due to the current status of the development of these advanced vehicles 

and the higher cost relative to HEVs. In addition, some consumers may have 

limited or no access to charging infrastructure and, for those consumers, the PHEV 

offers little benefit over an HEV at a higher cost. Further, we project (based on 

what we know today) that PHEV technology is not available to some vehicle 

types, such as large pickups, which are expected to provide a true “work” function. 

While it is technically possible to electrify such vehicles, there are tradeoffs in 

terms of cost, electric range, and utility (e.g., loss of towing and/or payload 

capacity) that may limit the appeal of the vehicle to a narrower market. However, 

the agencies are interested in promoting innovation to overcome these potential 

obstacles and are thus incentivizing more HEV and PHEV pickup trucks with 

credit flexibilities as described in the preamble for this final rule. 

Electric Vehicle (EV) - In our analyses, this technology includes vehicles with 

actual on-road ranges of 75, 100, and 150 miles. The actual on-road range was 

calculated using a projected 30% gap between two-cycle and on-road range. 

These vehicles are powered solely by electricity and are not powered by any liquid 

fuels. The maximum technology penetration rates and phase-in caps are set at less 

than 100% in MY 2025 due to the same general potential constraints as discussed 

for PHEVs. EVs have additional constraints due to limited infrastructure and 

range as well. Further, as with PHEVs, we assume that EV technology is not 

available to some vehicle types, such as large pickups. While it is possible to 

electrify such vehicles, there are tradeoffs in terms of cost, range, and utility that 

would limit the appeal of the vehicle to a narrower market. These trade-offs are 

expected to reduce the market for other vehicle types as well, and for this analysis 

we have considered this in the development of the maximum technology 

penetration rates. 

Mass Reduction - This technology includes changing the design to use less 

material or substituting lighter materials for heavier materials. Mass reduction 

compounding after significant primary mass reduction is achieved can also make 

significant contribution to the overall vehicle mass reduction. NHTSA and EPA 

have conducted a thorough assessment of the levels of mass reduction that could 

be achieved which is both technologically feasible and which can be implemented 

in a safe manner for this joint federal FRM (as described earlier in this 

Chapter).Vehicle phase-in caps 

GHG-reducing and fuel-saving technologies for vehicle applications vary widely in 

function, cost, effectiveness and availability. Some of these attributes, like cost and 

availability vary from year to year. New technologies often take several years to become 

available across the entire market. The agencies use phase-in caps to manage the maximum 

rate that the CAFE and OMEGA models can apply new technologies. 

Phase-in caps are intended to function as a proxy for a number of real-world 

limitations in deploying new technologies in the auto industry. These limitations can include 

but are not limited to, engineering resources at the OEM or supplier level, restrictions on 

intellectual property that limit deployment, and/or limitations in material or component supply 
 Technologies Considered in the Agencies’ Analysis

3-252 

as a market for a new technology develops. Without phase-in caps, the models may apply 

technologies at rates that are not representative of what the industry is actually capable of 

producing, which would suggest that more stringent standards might be feasible than actually 

would be. 

EPA applies the caps on an OEM vehicle platform basis for most technologies. For a 

given technology with a cap of x%, this means that x% of a vehicle platform can receive that 

technology. On a fleet average basis, since all vehicle platforms can receive x% of this 

technology, x% of a manufacturer’s fleet can also receive that technology. EVs and PHEVs 

are an exception to this rule as the agencies limit the availability of these technologies to some 

subclasses. Unlike other technologies, in order to maintain utility, EPA only allows nontowing vehicle types to be electrified in the OMEGA model. As a result, the PHEV and EV 

cap was applied so that the average manufacturer could produce to the cap levels. As would 

be expected, manufacturers that make more non-towing vehicles can have a higher fraction of 

their fleet converted to EVs and PHEVs, while those that make fewer non-towing vehicles 

have a lower potential maximum limit on EV and PHEV production. 

NHTSA applies phase-in caps in addition to refresh/redesign cycles used in the CAFE 

model, which constrain the rate of technology application at the vehicle level so as to ensure a 

period of stability following any modeled technology applications. Unlike vehicle-level cycle 

settings, phase-in caps, defined on a percent per year basis, constrain technology application 

at the OEM level. As discussed above phase-in caps are intended to reflect a manufacturer’s 

overall resource capacity available for implementing new technologies (such as engineering 

and development personnel and financial resources) thereby ensuring that resource capacity is 

accounted for in the modeling process. At a high level, phase-in caps and refresh/redesign 

cycles work in conjunction with one another to avoid the CAFE modeling process out-pacing 

an OEM’s limited pool of available resources during the rulemaking time frame, especially in 

years where many models may be scheduled for refresh or redesign. This helps to ensure 

technological feasibility and economic practicability in determining the stringency of the 

standards. 

Phase-in caps do not necessarily define market penetration rates and they do not 

necessarily define the rate at which a particular technology will be applied, rather they simply 

present an upper limit, or ceiling at which the agencies’ computer models (OMEGA and 

CAFE) can apply new technologies to vehicles to raise their fuel economy and reduce their 

CO2 emissions. Ultimately, phase-in caps are determined by the agencies using engineering 

judgment. However, there are several sources of information on technology penetration that 

the agencies consider in assigning phase-in caps to various technologies: 

Confidential OEM submissions indicate the rate at which an individual 

manufacturer can deploy a particular technology. Manufacturer information is 

especially helpful if multiple manufacturers indicate similar technology 

penetration rates. The agencies consider these CBI submissions along with 

other sources of information. 

Historical data from EPA’s annual Carbon Dioxide Emissions and Fuel 

Economy Trends Report111 database are used to inform the agencies about 

typical historical rates of adoption of technologies. Relevant data include both 
 Technologies Considered in the Agencies’ Analysis

3-253 

the industry-wide technology penetration data that are included in the annual 

Trends report, as well as individual manufacturer-specific technology 

penetration data that have not been published in the Trends report, but which 

are presented below. 

Trade press articles, company publications, press releases, and other reports 

often discuss new technologies, how quickly they will be deployed and 

manufacturing strategies that enable faster penetration rates. These articles 

provide a useful glimpse into how manufacturers are changing in order to 

become more competitive. 

The relative complexity of a technology as well as the availability from 

suppliers. Some technologies can be implemented rather easily—like tires. 

Other technologies are much more sophisticated—like hybridization. 

3.5.1.1 Trends Report and Industry Data 

For over 30 years, EPA’s Fuel Economy Trends report has tracked the fuel economy 

of light duty vehicles and the technology used by automakers to improve fuel economy. A 

particularly interesting aspect of the Trends data is how technology is adopted by the industry 

and how this changes over time. Trends data shows that industry-wide, it has typically taken 

up to 15-20 years for a technology to penetrate the entire fleet. Some technologies such as 

port fuel injection and variable valve timing start slowly and then rapidly progress. Others, 

like torque converter lockup and front wheel drive penetrate rapidly after their first 

appearance on the market. Figure 3-37, below shows these trends. 

Figure 3-37 Technology Penetration After First Significant Use112

There are several cases where technologies have penetrated the fleet rapidly, 

sometimes beginning with significant market penetration, sometimes beginning with 

Years After First Use

Production Share

0%

20%

40%

60%

80%

100%

CVT

Front Wheel Drive

Lockup

Multi Valve

Port FI

Variable Valve Timing

0 5 10 15 20 25 30 35
 Technologies Considered in the Agencies’ Analysis

3-254 

relatively small market penetration. For example, six speed automatic transmissions were in 

7% of the industry-wide fleet in 2006 and by 2010, they were in 36% of the fleet, for an 

increase of 29% in 4 yearsppp. Port fuel injection went from about 12% of the fleet in 1984 to 

88% in 1994. Front wheel drive, a technology that requires a complete change in vehicle 

architecture, increased from 9% in 1979 to 60% by 1988113

.

Recent academic literature has also used deployment rate data from the EPA Fuel 

Economy Trends Report, Wards Factory Installed data, and other sources to report to describe 

historical deployment rates of a variety of technologies (DeCicco, 2010 and Zoepf, 2011). 

DeCicco, for example, cites conversion to fuel injection and front wheel drive in passenger 

cars as having seen maximum growth in adoption of 17% and 11% per year respectively.114

 

Zoepf examines a broader array of automotive technologies and notes a span of maximum 

growth rates in passenger cars from 4% to nearly 24% per year with variance based on feature 

type.115

While these examples show that the industry is capable of adopting certain new 

technologies rapidly industry-wide, considering the rate of introduction of technology by 

individual OEMs shows that the pace of technology introduction can in some cases be even 

faster. Table 3-129 below shows how individual manufacturers can apply technologies rapidly 

to a large fraction of their fleet. Although not typical for most manufacturers and 

technologies, the data below shows that manufacturers have chosen to deploy some 

technologies very rapidly. 

Table 3-129: Historical Phase-In Rates of Selected Technologies 

Manufacturer Technology Technology Market Share Increase

General Motors Lockup Transmission 1980-1982: 83% in 3 years

Ford Fuel Injection 1983-1987: 91% in 5 years

Honda Fuel Injection 1986-1990: 91% in 5 years

Chrysler Fuel Injection 1988: 37% in 1 year

Toyota-cars only Multi-Valve 1987-1989: 85% in 3 years

Nissan-cars only Multi-Valve 1989-1990: 71% in 2 years

Toyota-cars only Variable Valve Timing 2000-2003: 87% in 4 years

Ford Multi-Valve 2004-2005: 36% in 2 years

Nissan Continuously Variable 

Transmission 2007: 45% in 1 year

Volkswagen Gasoline Direct Injection 2008: 52% in 1 year

Hyundai Variable Valve Timing 2009: 48% in 1 year

General Motors Variable Valve Timing 2006-2010: 75% in 5 years

General Motors Gasoline Direct Injection 2010: 27% in 1 year

Often, a rapid application of technology is helped by having similar vehicle 

architecture, or by sharing major components such as engines or transmissions across multiple 

products. As discussed below, platform sharing combined with improvements in platform 

 

ppp EPA staff calculated the penetration rate of 6-speed automatic transmissions from 2010 Trends data. 

Aggregated source data can be seen on page 54 of the 2010 Fuel Economy Trends Report. 
 Technologies Considered in the Agencies’ Analysis

3-255 

and manufacturing flexibility is expected to further enable faster implementation of new 

technologies. 

3.5.1.2 The rate of technology adoption is increasing 

The agencies recognize that new technologies may not achieve rapid deployment 

immediately and that small-scale production is a part of the technology learning process. To 

this end the phase-in caps distinguish between technologies that have been successfully 

applied in existing vehicles and those that under development but are anticipated on 

production vehicles in the near future. 

The rate of technology adoption appears to be increasing as manufacturers increase 

model turnover and decrease the numbers of unique vehicle platforms. This facilitates a 

steady stream of new products, increased sales and optimized vehicle redesigns allowing and 

fuel consumption-reducing technologies to be applied to as many vehicles as possible. In 

today’s globally competitive market, and certainly for the U.S., market share and 

competitiveness is strongly influenced by a manufacturer’s ability to turn over their product 

line-up. Merrill Lynch’s Car Wars Report116 shows that replacement rate is speeding up and 

showroom age is dropping as manufacturers are striving to be more competitive in the market. 

Increased model turn-over creates more opportunity for manufacturers to deploy new 

technologies faster than in the past. 

Zoepf, cited above, reports that the developmental time, from first production 

application to maximum growth rate, has been declining exponentially as manufacturers bring 

innovations to market progressively faster. Ellison et al. (1995)117 indicate that U.S. and 

European automakers reduced overall product development time by more than a year in the 

1990s. Ellison et al. point to the increased role that suppliers have had in product 

development process during the same time, potentially commoditizing innovations more 

quickly. 

Vehicle platforms are the basic underpinnings of vehicles and are often shared across 

several unique products. By reducing the number of platforms, and making these platforms 

flexible, manufacturers can better deploy resources to serve a wider market with more 

products. Utilizing a modern, flexible platform architecture, a manufacturer can produce a 

sedan, wagon, minivan, and a crossover, or SUV on a single platform and all of these 

products can be assembled in a single vehicle assembly plant. Basic components can be 

developed and purchased at high volumes, while enabling the manufacturer to exploit what 

would otherwise be niche markets. This commonization of platforms does have the potential 

to increase the mass for lighter vehicle models within the platform because the platform needs 

to be designed for the more severe duty cycle of the SUV and/or larger engine. Volkswagen 

has recently launched a new platform called MQB, which will be used world-wide by up to 60 

unique models from VW, Audi, Seat, and Skoda. This structure will replace 18 “engine 

mounting architectures” with just two. 

It gives us the possibility to produce models from different segments and in 

varying sizes using the same basic front-end architecture,” .... “We can go from a 

typical hatchback to a saloon, cabriolet and SUV with only detailed changes to the 
 Technologies Considered in the Agencies’ Analysis

3-256 

size of the wheel carriers.” ... it will be used on every model from the new Lupo all the 

way through to the next-generation Sharan.118

One of the key enablers of this drive to reduce platforms and increase model turn-over 

is increased manufacturing flexibility.119 For example, in 2004, Ford invested in flexible 

manufacturing technology for their Cleveland No. 1 engine plant. Although the plant was 

shut down for two years after this investment, Ford was able to retool and reopen the plant at 

a low cost to produce their new 3.5L EcoBoost turbocharged, direct injection engine as well 

as their 3.7L V6.120 In their December, 2008 business plan submitted to Congress,121 Ford 

further stated, 

...nearly all of our U.S. assembly plants will have flexible body shops by 2012 

to enable quick response to changing consumer demands and nearly half of our 

transmission and engine plants will be flexible, capable of manufacturing various 

combinations of transmission and engine families. 

Like VW, Ford is also striving to reduce their platforms and complexity. In Ford’s 

2008 business plan submitted to Congress, they stated that in addition to divesting themselves 

from certain luxury brands like Jaguar, Land Rover, Volvo, and Aston Martin, they were 

working to consolidate their vehicle platforms from 25 in 2005 to 9 by 2012. Having more 

vehicles per platform frees up resources to deploy new technologies across a greater number 

of vehicles more quickly and increases the rate at which new technologies can be introduced. 

We believe GM’s recent restructuring will also enable faster vehicle redesigns and more rapid 

penetration rates in the 2010-plus time frame compared to the 1990s and 2000s. In the past 

seven years, GM has eliminated five brands (Saturn, Hummer, Saab, Pontiac, and 

Oldsmobile), significantly reducing the number of unique products and platforms the 

company needed to devote engineering resources to. GM has set a goal to halve its number of 

vehicle platforms by 2018 and boost manufacturing efficiency by 40%.122

3.5.1.3 Phase-in Rates Used in the Analysis 

Table 3-130 below shows phase-in rates for the technologies used in the OMEGA 

model. OMEGA calculations are based on five year intervals, so phase-in caps are derived 

for model years 2016, 2021 and 2025. Table 3-131 shows phase-in rates for the technologies 

used in the CAFE model. The CAFE model calculations are annual, so phase in rates are 

derived for every year of the program. Where possible, phase-in rates for OMEGA and 

CAFE were harmonized, but there are some differences mainly where technologies differ 

between the agencies. 

Most technologies are available at a rate of either 85% or 100% beginning in 2016. 

Some advanced technologies expected to enter the market in the near future such as EGR 

Boost follow a 3% annual cap increase from 2016 to 2021, then, approximately 10% from 

2021 to 2025. Diesels follow an annual 3% increase in phase-in cap through 2025. Hybrids 

follow a 3% annual increase from 2016 to 2012, then 5% from 2021 to 2015. PHEVs and EVs 

follow a 1% annual cap increase. 
 Technologies Considered in the Agencies’ Analysis

3-257 

Lower phase-in caps for Alternate Fueled Vehicles (AFVs) reflect additional 

investment in infrastructure that is required to achieve high levels of conversion to a new fuel 

type. These limited phase-in caps also reflect as yet unknown consumer responses to HEVs, 

PHEVs and BEVs. 

Table 3-130 Phase-In Caps used in the OMEGA model 

Technology 2016 2021 2025

Low Friction Lubricants 100% 100% 100%

Engine Friction Reduction - level 1 100% 100% 100%

Early Torque Converter lockup 100% 100% 100%

Aggressive Shift Logic - Level 1 100% 100% 100%

Improved Accessories - Level 1 100% 100% 100%

Low Rolling Resistance Tires - Level 1 100% 100% 100%

Low Drag Brakes 100% 100% 100%

VVT - Intake Cam Phasing 85% 100% 100%

VVT - Coupled Cam Phasing 85% 100% 100%

VVT - Dual Cam Phasing 85% 100% 100%

Cylinder Deactivation 85% 100% 100%

Variable Valve Lift - Discrete 85% 100% 100%

Variable Valve Lift - Continuous 85% 100% 100%

Conversion to DOHC 85% 100% 100%

Stoichiometric Gasoline Direct Injection (GDI) 85% 100% 100%

Turbocharging (18 bar BMEP) and Downsizing 85% 100% 100%

Continuously Variable Transmission 85% 100% 100%

6-speed Automatic Transmission 85% 100% 100%

6-speed Dual Clutch Transmission - dry & wet clutch 85% 100% 100%

Electric & Electric/Hydraulic Power Steering 85% 100% 100%

12V Stop-Start 85% 100% 100%

Secondary Axle Disconnect 85% 100% 100%

Aero Drag Reduction - Level 1 85% 100% 100%

Aggressive Shift logic - Level 2 (Shift Optimizer) 0% 100% 100%

8-speed Automatic Transmission 30% 80% 100%

8-speed Dual Clutch Transmission - dry & wet clutch 30% 80% 100%

Improved Accessories - Level 2 30% 80% 100%

Aero Drag Reduction - Level 2 30% 80% 100%

Low Rolling Resistance Tires - Level 2 0% 75% 100%

Engine Friction Reduction - level 2 (inc. low friction lubes - level 2) 0% 60% 100%

High Effiency Gearbox 0% 60% 100%

Turbocharging (24 bar BMEP) and Downsizing 15% 30% 75%

Cooled EGR 15% 30% 75%

P2 Hybrid Electric Vehicle (HEV) and Mild Hybrid (MHEV) 15% 30% 50%

Turbocharging (27 bar BMEP) and Downsizing 0% 15% 50%
 Technologies Considered in the Agencies’ Analysis

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Conversion to Advanced Diesel 15% 30% 42%

Full Electric Vehicle (EV) 6% 11% 15%

Plug-in HEV 5% 10% 14%

Table 3-131 Phase-In Caps used in the CAFE Model 

3.6 How are the technologies applied in the agencies' respective models? 

To estimate potential technology application in response to potential CAFE standards, 

and accompanying costs, effects, and benefits of potential CAFE standards, NHTSA uses the 

CAFE Compliance and Effects Modeling System, which was developed specifically for that 

Technology Abbr.

MY 

2009

MY 

2010

MY 

2011

MY 

2012

MY 

2013

MY 

2014

MY 

2015

MY 

2016

MY 

2017

MY 

2018

MY 

2019

MY 

2020

MY 

2021

MY 

2022

MY 

2023

MY 

2024

MY 

2025

Low Friction Lubricants - Level 1 LUB1 30% 40% 50% 60% 70% 80% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Engine Friction Reduction - Level 1 EFR1 30% 40% 50% 60% 70% 80% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Low Friction Lubricants and Engine Friction Reduction - Level 2 LUB2_EFR2 0% 0% 0% 0% 0% 0% 0% 0% 12% 24% 36% 48% 60% 72% 84% 96% 100%

Variable Valve Timing (VVT) - Coupled Cam Phasing (CCP) on SOHC CCPS 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Cylinder Deactivation on SOHC DEACS 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Variable Valve Timing (VVT) - Intake Cam Phasing (ICP) ICP 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Variable Valve Timing (VVT) - Dual Cam Phasing (DCP) DCP 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Continuously Variable Valve Lift (CVVL) CVVL 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Cylinder Deactivation on DOHC DEACD 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Stoichiometric Gasoline Direct Injection (GDI) SGDI 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Cylinder Deactivation on OHV DEACO 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Variable Valve Actuation - CCP and DVVL on OHV VVA 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Stoichiometric Gasoline Direct Injection (GDI) on OHV SGDIO 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Turbocharging and Downsizing - Level 1 (18 bar BMEP) TRBDS1_SD 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Turbocharging and Downsizing - Level 2 (24 bar BMEP) TRBDS2_SD 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 45% 60% 75% 75%

Cooled Exhaust Gas Recirculation (EGR) - Level 1 (24 bar BMEP) CEGR1_SD 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 45% 60% 75% 75%

Cooled Exhaust Gas Recirculation (EGR) - Level 2 (27 bar BMEP) CEGR2_LD 0% 0% 0% 0% 0% 0% 0% 0% 3% 6% 9% 12% 15% 25% 35% 45% 50%

Advanced Diesel ADSL_LD 0% 0% 0% 0% 3% 6% 6% 6% 6% 6% 6% 6% 6% 6% 6% 6% 6%

6-Speed Manual/Improved Internals 6MAN 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

High Efficiency Gearbox (Manual) HETRANSM 0% 0% 0% 0% 0% 0% 0% 0% 12% 24% 36% 48% 60% 72% 84% 96% 100%

Improved Auto. Trans. Controls/Externals IATC 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

6-Speed Trans with Improved Internals (Auto) NAUTO 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

6-speed DCT DCT 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

8-Speed Trans (Auto or DCT) 8SPD 0% 0% 0% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 100% 100%

High Efficiency Gearbox (Auto or DCT) HETRANS 0% 0% 0% 0% 0% 0% 0% 0% 12% 24% 36% 48% 60% 72% 84% 96% 100%

Shift Optimizer SHFTOPT 0% 0% 0% 0% 0% 0% 0% 0% 20% 40% 60% 80% 100% 100% 100% 100% 100%

Electric Power Steering EPS 5% 20% 35% 50% 65% 80% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Improved Accessories - Level 1 IACC1 5% 20% 35% 50% 65% 80% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Improved Accessories - Level 2 IACC2 0% 0% 0% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 100% 100%

12V Micro-Hybrid (Stop-Start) MHEV 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Integrated Starter Generator ISG 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 35% 40% 45% 50%

Strong Hybrid - Level 1 SHEV1 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 35% 40% 45% 50%

Conversion from SHEV1 to SHEV2 SHEV1_2 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 35% 40% 45% 50%

Strong Hybrid - Level 2 SHEV2 0% 0% 0% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30% 35% 40% 45% 50%

Plug-in Hybrid - 30 mi range PHEV1 0% 0% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14%

Plug-in Hybrid PHEV2 0% 0% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14%

Electric Vehicle (Early Adopter) - 75 mile range EV1 0% 0% 0% 1% 2% 3% 4% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5%

Electric Vehicle (Broad Market) - 150 mile range EV4 0% 0% 0% 0% 0% 0% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

Fuel Cell Vehicle FCV 0% 0% 0% 0% 0% 0% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

Mass Reduction - Level 1 MR1 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Mass Reduction - Level 2 MR2 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Mass Reduction - Level 3 MR3 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Mass Reduction - Level 4 MR4 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 100% 100% 100% 100% 100%

Mass Reduction - Level 5 MR5 0% 0% 0% 0% 0% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Low Rolling Resistance Tires - Level 1 ROLL1 20% 35% 50% 65% 80% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Low Rolling Resistance Tires - Level 2 ROLL2 0% 0% 0% 0% 0% 0% 0% 0% 15% 30% 45% 60% 75% 90% 100% 100% 100%

Low Drag Brakes LDB 20% 35% 50% 65% 80% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Secondary Axle Disconnect SAX 15% 25% 35% 45% 55% 65% 75% 85% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Aero Drag Reduction, Level 1 AERO1 30% 40% 50% 60% 70% 80% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Aero Drag Reduction, Level 2 AERO2 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 100% 100% 100% 100% 100%
 Technologies Considered in the Agencies’ Analysis

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purpose by DOT’s Volpe National Transportation Systems Center (Volpe Center). To 

estimate potential technology application in response to potential CAFE standards, and 

accompanying costs, EPA uses the OMEGA model, which EPA staff developed specifically 

for that purpose. The models apply different but related methods to estimate and account for 

potential applications of technology. The models and methods are discussed in the agencies’ 

respective RIAs and preamble sections, and in detail in documentation. The agencies have 

each developed modeling system inputs reflecting estimates that have been agreed to and 

presented above. 

3.7 Maintenance and Repair Costs Associated with New Technologies 

In the proposal, we requested comment on maintenance, repair, and other operatingcosts and whether these might increase or decrease with the new technologies (See 76 FR 

74925) We received comments on this topic from NADA. These comments stated that the 

agencies should include maintenance and repair costs in estimates of total cost of ownership 

(i.e., in our payback analyses).qqq

 NADA proffered their website 

(http://www.nadaguides.com/Cars/Cost-to-Own) as a place to find useful information on 

operating costs that might be used in our final analyses. This website tool is meant to help 

consumers quantify the cost of ownership of a new vehicle. The tool includes estimates for 

depreciation, fees, financing, insurance, fuel maintenance, opportunity costs and repairs for 

the first five years of ownership. The agencies acknowledge that the tool may be useful for 

consumers; however, there is no information provided on how these estimates were 

determined. Without documentation of the basis for estimates, the website information is of 

limited use in this rulemaking where the agencies document the source and basis for each 

factual assertion. Also, the costs do not extend beyond five years, which the agencies require 

for purposes of estimating social costs and costs of ownership throughout vehicles’ useful 

lives. There are also evident substantive anomalies in the website information.rrr For these 

reasons, the agencies have performed an independent analysis to quantify maintenance costs. 

Here we summarize what we have done for the final rule with respect to maintenance 

and repair costs. We distinguish maintenance from repair costs as follows: maintenance costs 

are those costs that are required to keep a vehicle properly maintained and, as such, are 

usually recommended to occur by auto makers on a regular schedule. Examples of 

maintenance are oil and air filter changes, tire replacements, brake pad replacement, etc. 

Repair costs are those costs that are unexpected and, as such, occur randomly and uniquely for 

every vehicle owner, if at all. Examples of repair would be parts replacement following an 

 

qqq

 See NADA (EPA-HQ-OAR-2010-0799-9575, p.10).

rrr For example, comparing the 2012 Hyundai Sonata showed the same cost for fuel ($11,024) regardless of 

whether it is a hybrid option or not. The HEV fuel economy rating is 35/40 mpg City/Highway for the HEV and 

2.4L non HEV rating is 24/35. Another example is the 2012 Ford Fusion SEL: the front wheel drive and the allwheel drive versions have identical fuel cost despite having different fuel economies. 
 Technologies Considered in the Agencies’ Analysis

3-260 

accident, light bulb replacement, turbocharger replacement following a mechanical failure, 

etc. 

How each agency has folded the costs presented here into their respective final 

analyses is presented in each agency’s respective preamble sections (section III for EPA, 

section IV for NHTSA) and RIAs. 

The agencies have also evaluated ownership costs that include financing, sales tax, 

and insurance costs, and discuss those costs in TSD 4 and in each agency’s respective 

preamble sections (section III for EPA, section IV for NHTSA) and RIAs. 

3.7.1 Maintenance Costs 

To estimate maintenance costs that could reasonably be attributed to these rules, the 

agencies have looked at vehicle models for which there exists a version with a fuel efficiency 

and GHG emissions improving technology and a version with the corresponding baseline 

technology. The difference between maintenance costs for the two models represent a cost 

which the agencies are attributing to this rulemaking. For example, the Ford Escape Hybrid 

versus the Ford Escape V6 was considered when estimating the types of maintenance cost 

differences that might be present for a hybrid vehicle versus a non-hybrid, and a Ford F150 

with EcoBoost versus the Ford F150 5.0L was considered when estimating the types of 

maintenance cost differences that might be present for a turbocharged and downsized versus a 

naturally aspirated engine. In the case of low rolling resistance tires, we have looked at 

specific parts rather than specific vehicle models. 

By comparing the manufacturer recommended maintenance schedule of the items 

being compared, we were able to estimate the differences in maintenance intervals for the 

two. With estimates of the costs per maintenance event, we are able to put together a picture 

of the maintenance cost differences associated with the “new” technology. 

The technologies considered, maintenance interval comparisons, costs per 

maintenance event are shown in Table 3-132. 

Table 3-132 Maintenance Interval and Maintenance Cost Differences for 2017-2025 Enabling 

Technologies (dollar values in 2010$)a

2017-2025 

Technology

Reference 

Case

Control 

Case

Mainte

nance Interval 

Difference

Main

tenance 

Event Cost 

Difference

Low Rolling 

Resistance Tires -

Level 1

Michelin Harmony Michelin Energy 

Saver A/S Identical +$6.44 every 40K miles

Low Rolling 

Resistance Tires -

Level 2

Michelin Energy Saver 

A/S does not exist Identical +$43.52 every 40K miles

Stoichiometric 

Gasoline Direct 

Injection (GDI)

2010 Hyundai Sonata 

2.4L

2011 Hyundai Sonata 

2.4L Identical +$0.00

Turbocharging (18 

bar BMEP) and 

Downsizing

2011 F-150 5.0L 2011 F-150 EcoBoost Identical +$0.00
 Technologies Considered in the Agencies’ Analysis

3-261 

6-speed Dual Clutch 

Transmission - dry & 

wet clutch

2005 VW Jetta Auto (6-

spd '09G')

2005 VW Jetta DSG 

(6-spd '02E' Identical +$0.00

Electric & 

Electric/Hydraulic 

Power Steering

2009 Ford Fusion 2010 Ford Fusion Identical +$0.00

12V Stop-Start -- 2013 Volvo V40 N/A --

8-speed Automatic 

Transmission 2010 BMW 750i 6-spd 2010 BMW 760Li 8-spd Identical +$0.00

Cooled EGR -- 2013 Volvo V40 N/A --

Conversion to 

Advanced Diesel 2011 VW Jetta SE 2.5L 2011 VW Jetta TDI Identical +$49.25 every 20K miles

Hybrid Electric 

Vehicle (HEV)

2012 Ford Escape V6 

2012 Hyundai Sonata I4 

2012 Toyota Camry V6 

2012 Chevy Silverado 

5.3L

2012 Ford Escape 

Hybrid 2012 

Hyundai Sonata 

Hybrid 2012 

Toyota Camry Hybrid 

2012 Silverado 2-

Mode Hybrid

Identical +$0.00

P2 HEV 2012 Sonata V6 2013 Sonata Hybrid N/A --

Plug-in HEV 2012 Chevrolet Cruze 2012 Chevrolet Volt

Identical (for 

common service 

items)

+$0.00

Full Electric Vehicle 

(EV) – oil change 2011 Nissan Versa 2011 Nissan Leaf No interval for EV

-$38.67 every 

7.5K miles

EV – air filter change 2011 Nissan Versa 2011 Nissan Leaf No interval for 

EV

-$28.60 every 

30K miles

EV – spark plugs 2011 Nissan Versa 2011 Nissan Leaf No interval for 

EV

-$83.00 every 

105K miles

EV – brake fluid 2011 Nissan Versa 2011 Nissan Leaf Identical +$0.00

EV – engine coolant 2012 Ford Focus 2012 Ford Focus EV No interval for 

EV

-$59.00 every 

100K milesb

EV/PHEVc– battery 

coolant 2011 Ford Focus 2011 Ford Focus EV No interval for 

Focus (gasoline)

+$117.00 

every 150K 

milesb

EV – battery health 

check 2011 Nissan Versa 2011 Nissan Leaf No interval for Versa

+$38.67 every 

15K miles

a All maintenance interval, hours required, and part(s) cost differentials between reference and control cases 

were sourced from the ALLDATA subscription database (www.alldatapro.com) in January through February of 

2012, unless noted otherwise in the text.

b These are the values the agencies used when conducting analyses. However, as newer information became 

available the agencies concluded these revised values (cost and interval) resided within an appropriate range 

given the uncertainty in how future systems will be designed. Additional information is available in bulleted text 

below.

c EPA also applied this maintenance cost adjustment to PHEVs; NHTSA did not.

Further comments and details with respect to Table 3-132: 

Low Rolling Resistance Tires – Level 1: Current Uniform Tire Quality Grading 

ratings (treadwear, traction, temperature) for “LRR” tires do not give a clear indication 

of tire life vs. conventional tires (e.g. 225/50R17 Michelin Harmony = 740 A B and 

Michelin Energy Saver A/S = 480 A A; whereas Goodyear Assurance Fuel Max = 580 

A A and Goodyear Assurance TripleTred = 540 A A). The $6 value per maintenance 
 Technologies Considered in the Agencies’ Analysis

3-262 

event in based on the 2025MY LRRT1 incremental cost presented in Table 3-100 of 

this Joint TSD. 

Low Rolling Resistance Tires – Level 2: The $44 value per maintenance event in 

based on the 2025MY LRRT2 incremental cost presented in Table 3-100 of this Joint 

TSD. 

Stoichiometric GDI: 36,000 mile fuel filter interval for reference case (filter cost = 

$25.99), 37,500 mile interval for control case (filter cost = $34.68, or +$8.69 for every 

36,000 miles). However, BMW does not require any fuel filter changes for their 

turbocharged GDI engines. 

Turbocharging (18 bar BMEP) and Downsizing: Oil change interval, oil type, and 

labor hours are identical between the reference and control cases. The reference case 

takes more oil - 7.7 quarts vs 6.2 quarts - but they take the exact same type of oil (5W20 synthetic blend, Ford specification WSS-M2C9302-A). The control case uses a 

larger oil filter with higher filtration efficiency compared to the reference case and the 

cost is $13.89 vs. $9.76 or +$4.13 for the control case every 5,000 miles. However, 

BMW uses the exact same oil filter and oil specification in naturally aspirated and 

turbocharged GDI applications. 

6 speed Dual Clutch Transmission (dry & wet): Control case requires fluid & filter 

change every 40,000 miles; reference case is “fill-for-life”. However, the 2012 Ford 

Fiesta dual clutch transmission requires fluid and filter replacement at 150,000 miles; 

the dual clutch transmission requires 2.2 quarts of fluid and the automatic transmission 

requires 6.9 quarts. 

Electric & Electric/Hydraulic Power Steering: No power steering fluid changes are 

required in either the reference or control cases – both are “fill-for-life”. 

12 Volt Stop-Start: No information available. 

8 speed Automatic Transmission: Replace fluid and filter at 150,000 miles for both 

reference and control cases. 

Cooled EGR: No information available. 

Conversion to Advanced Diesel (from gasoline): Identical oil change and air filter 

maintenance intervals, but different oil capacity (4.8 liters for reference case and 4.3 

liters for control case), oil type (VW 502 00 for reference case and VW 507 00 for 

control case), and oil filter (06D115562 for reference case @ $14.00 and 071115562C 

for control case @ $9.00). According to VW service bulletin, “502” and “507” oil 

specs have converged into a single list of approved oils for the North American 

market, so oil change cost is assumed to be equal between gasoline and diesel engines. 

However, the control case requires a fuel filter change every 20,000 miles: fuel filter 

part number 1K0127434A @ $37.25 + 0.4 hrs labor @ $30/hr, or +$49.25 every 

20,000 miles.

Hybrid Electric Vehicle (HEV): Ford Escape in the control case has longer oil change 

interval (10,000 vs. 7,500 miles) compared to its reference case. However, Sonata, 

Camry, and Silverado reference and control cases have identical engine oil change 
 Technologies Considered in the Agencies’ Analysis

3-263 

intervals, oil types, and labor hours, as well as identical transmission fluid change 

intervals and fluid types. 

P2 HEV: No information available. 

Plug-in HEV: Oil change interval, oil type, and labor hours are identical for the 

reference and control cases. 

Full Electric Vehicle (EV) – Oil Change: Reference case has an oil change interval 

every 7,500 miles: the cost is 4.4 quarts of 5W-30 @ $3.50/quart + $8.27 oil filter 

(part number 1520865F0C) + 0.5 hours labor @ $30/hr = -$38.67 (i.e., savings) for 

control case every 7,500 miles. 

Full Electric Vehicle (EV) – Air Filter Change: Reference case replacement interval is 

every 30,000 miles; cost is $19.60 for the part + 0.3 hours labor @ $30/hr = -$28.60 

(savings) for control case every 30,000 miles. 

Full Electric Vehicle (EV) – Spark Plug Replacement: Reference case replacement 

interval is every 105,000 miles; cost is $32.00 for parts (4 spark plugs @ estimated 

$8/plug), and 1.7 hours labor @ $30/hr = -$83.00 (savings) in control case every 

105,000 miles.

Full Electric Vehicle (EV) – Brake Fluid Replacement: Interval and fluid 

specifications are identical in reference and control cases. 

Full Electric Vehicle (EV) – Engine Coolant Replacement: Reference case 

replacement interval is every 100,000 miles; cost is $29.00 for parts (7.25 quarts @ 

estimated $4/quart), and 1.0 hour labor (estimated) @ $30/hr = -$59.00 (savings) for 

control case every 100,000 miles. More recent information suggests a reference case 

replacement interval every 100,000 miles; cost is $21.20 for parts (5.3 quarts @ 

estimated $4/quart), and 1.0 hour labor (estimated) @ $30/hr = -$51.20 (savings) for 

control case every 100,000 miles. 

Full Electric Vehicle (EV) – Battery Coolant Replacement: Control case has a 

recommended battery coolant replacement 150,000 miles; uses same coolant as a 

gasoline engine but approximately three times the amount ($29.00 x 3 = $87.00); 

assume labor is the same as the gasoline engine coolant changes ($30.00) for a total 

cost of +$117.00 for control case every 150,000 miles. More recent information 

suggests that perhaps the control case should have used a recommended battery 

coolant replacement at 150,000 miles; uses same coolant as a gasoline engine but 

approximately three times the amount for a parts cost of $63.20 (15.8 quarts @ est. 

$4/quart); assume labor is the same as the gasoline engine coolant changes ($30.00) 

for a total cost of +$93.20 for control case every 150,000 miles. Also, EPA applied 

this cost to PHEVs as well, whereas NHTSA did not. 

Full Electric Vehicle (EV) – Battery Health Check: Two auto makers recommend 

periodic battery/electrical checks to run in-depth diagnostics and visual inspection; no 

information available on costs or interval; assume cost = cost of oil change and 

interval is double that for oil change; +$38.67 every 15,000 miles in control case. 
 Technologies Considered in the Agencies’ Analysis

3-264 

There is evidence supporting that brake maintenance costs are lower in hybrid electric 

vehicles that are equipped with regenerative braking. The electric regeneration reduces the 

amount of energy that the brake system dissipates which causes less wear on the brake pads 

and rotors. However, the maintenance schedules do not reflect a lower frequency of 

maintenance; therefore, the agencies have attempted to remain consistent with the current 

methodology and have assumed no difference in cost (i.e., no savings). 

For the first time in CAFE and GHG rulemaking, both agencies now include 

maintenance costs in their benefit-cost analyses and in their respective payback analyses. As 

noted above, please refer to each agency’s preamble sections and final RIAs (Chapter 5 of 

EPA’s RIA, Chapter VIII of NHTSA’s RIA) for details of how the maintenance costs 

presented above are accounted for in each agency’s respective analysis. 

3.7.2 Repair Costs 

Although NADA is correct that the agencies’ NPRM analyses did not account for 

repair costs to equipment added as a result of these rules and incurred throughout a vehicle’s 

useful life, the agencies’ NPRM analysis did account for the costs of repairs covered by 

manufacturers’ warranties. (See 76 FR 74925 and 74927) The indirect cost multipliers 

(ICMs) applied in the agencies’ analyses include a component representing manufacturers’ 

warranty costs. For the cost of repairs not covered by OEMs’ warranties, the agencies 

evaluated the potential to apply an approach similar to that described above for maintenance 

costs. As for specific scheduled maintenance items, the AllData subscription database applied 

above provides estimates of labor and part costs for specific repairs to specific vehicle 

models. However, although AllData also provides service intervals for scheduled 

maintenance items, it does not provide estimates of the frequency at which specific failures 

may be expected to occur over a vehicle’s useful life. The agencies have not yet been able to 

develop an alternative method to estimate the frequencies of different types of repairs, and are 

therefore unable to apply these AllData estimates in order to quantify the cost of repairs 

throughout vehicles’ useful lives. Moreover, the frequency of repair of technologies that do 

not yet exist in the fleet, or are only emerging today provides insufficient representation of 

what they will be in the future with wider penetration of those technologies. Therefore, the 

agencies’ central analyses supporting the final rule does not include these potential costs. 

However, as at proposal, our analyses do include estimated warranty costs and, therefore, the 

costs of repairs covered by OEMs’ warranties. Repair costs are discussed further in each 

agency’s Regulatory Impact Analysis and preamble sections. 
 Technologies Considered in the Agencies’ Analysis

3-265 

References:

1 U.S. EPA, “Project Report: Computer Simulation of Light-Duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe”, Contract No. EP-C-11-

007, Work Assignment 0-12, 

2 U.S. EPA, “Light-Duty Technology Cost Analysis Pilot Study,” Contract No. EP-C-07-069, 

Work Assignment 1-3, December 2009, EPA-420-R-09-020, Docket EPA-HQ-OAR-2009-

0472- 11282

3 U.S. EPA, “Light-Duty Technology Cost Analysis Pilot Study Peer Review Report —

Response to Comments Document”, December 21, 2009, EPA-HQ-OAR-2009-0472-11285 

4 U.S. EPA, “Light-duty Technology Cost Analysis – Report on Additional Case Studies,” 

EPA-HQ-OAR-2009-0472-11604 

5

FEV, Inc., “Light-Duty Technology Cost Analysis, Report on Additional Transmission, 

Mild Hybrid, and Valvetrain Technology Case Studies", Contract No. EP-C-07-069, Work 

Assignment 3-3. November 2011. 

6

FEV, Inc., “Light-Duty Technology Cost Analysis, Power-Split and P2 HEV Case Studies”, 

Contract No. EP-C-07-069, Work Assignment 3-3, EPA-420-R-11-015, November 2011. 

Docket ID: EPA-HQ-OAR-2010-0799; Document ID: EPA-HQ-OAR-2010-0799-1101. 

7 ANL BatPaC model can be found in Docket ID EPA-HQ-OAR-2010-0799. 

8 ANL BatPaC model peer review report can be found in Docket ID EPA-HQ-OAR-2010-

0799 

9 ANL BatPaC model can be found in Docket ID EPA-HQ-OAR-2010-0799. 

10 EPA-420-R-10-901, April 2010. 

11 “Interim Joint Technical Assessment Report: Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards for Model Years 2017-

2025,” September 2010. 

12 75 FR 76337. 

13 RTI International. Automobile Industry Retail Price Equivalent and Indirect Cost 

Multipliers. February 2009. http://www.epa.gov/otaq/ld-hwy/420r09003.pdf ; Rogozhin, 

A.,et al., “Using indirect cost multipliers to estimate the total cost of adding new technology 

in the automobile industry,” International Journal of Production Economics (2009), 

doi:10.1016/j.ijpe.2009.11.031. The peer review for the RTI report is at 

http://www.epa.gov/otaq/ld-hwy/420r09004.pdf . 
 Technologies Considered in the Agencies’ Analysis

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14 Rogozhin, A.,et al., “Using indirect cost multipliers to estimate the total cost of adding new 

technology in the automobile industry,” International Journal of Production Economics 

(2009), doi:10.1016/j.ijpe.2009.11.031. 

15 Helfand, Gloria, and Todd Sherwood, “Documentation of the Development of Indirect Cost 

Multipliers for Three Automotive Technologies,” August 2009. 

16 Rogozhin, A.,et al., “Using indirect cost multipliers to estimate the total cost of adding new 

technology in the automobile industry,” International Journal of Production Economics 

(2009), doi:10.1016/j.ijpe.2009.11.031. 

17 Rogozhin, A., Gallaher, M., McManus, W., February 2009. Automobile industry retail 

price equivalent and indirect cost multipliers. EPA-420-R-09-003. Available at: 

/http://www.epa.gov/otaq/ld-hwy/420r09003.pdf. 

18 Rogozhin, A., Gallaher, M., McManus, W., February 2009. Automobile industry retail 

price equivalent and indirect cost multipliers. EPA-420-R-09-003, Table 4-3.

19 U.S. Environmental Protection Agency and National Highway Traffic Safety 

Administration (November 2011). Draft Joint Technical Support Document: Proposed 

Rulemaking for 2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and 

Corporate Average Fuel Economy Standards.” EPA-420-D-11_901, available at 

http://www.epa.gov/otaq/climate/documents/420d11901.pdf, p. 3-12. 

20 National Research Council. Assessment of Fuel Economy Technologies for Light-Duty 

Vehicles. Washington, D.C.: National Academies Press, 2010. 

21 NRC, ibid, pp. 3-22, 6-16. 

22 FEV, Inc., “Potential Stranded Capital Analysis on EPA Light-Duty Technology Cost 

Analysis”, Contract No. EP-C-07-069 Work Assignment 3-3, November 2011. 

23 76 FR 57106 (September 15, 2011). 

24 U.S. EPA, “A Study of Potential Effectiveness of Carbon Dioxide Reducing Vehicle 

Technologies”, Contract No. EP-C-06-003, Work Assignment 1-14, , June 2008, Report# 

EPA420-R-08-004, available in the EPA docket EPA-HQ-OAR-2009-0472 and on the 

internet at http://www.epa.gov/otaq/technology/420r08004a.pdf 

25 76 FR 57106 (September 15, 2011) 

26 Woldring, D., Landenfeld, T., Christie, M.J., 2007, “DI Boost: Application of a High 

Performance Gasoline Direct Injection Concept.” SAE Technical Paper Series No. 2007-01-

1410. 

27 Kapus, P.E., Fraidl, G.K., Prevedel, K., Fuerhapter, A., 2007, “GDI Turbo – The Next 

Steps.” JSAE Technical Paper No. 20075355. 
 Technologies Considered in the Agencies’ Analysis

3-267 

28 Hancock, D., Fraser, N., Jeremy, M., Sykes, R., Blaxill, H., 2008, “A New 3 Cylinder 1.2l 

Advanced Downsizing Technology Demonstrator Engine.” SAE Technical Paper Series No. 

2008-01-0611. 

29 Lumsden, G., OudeNijeweme, D., Fraser, N. Blaxill, H., 2009, “Development of a 

Turbocharged Direct Injection Downsizing Demonstrator Engine.” SAE Technical Paper 

Series No. 2009-01-1503. 

30 Cruff, L., Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010, “EBDI® - 

Application of a Fully Flexible High Bmep Downsized Spark Ignited Engine.” SAE Technical 

Paper Series No. 2010-01-0587.

31 Turner, J.W.G., Pearson, R.J., Curtis, R., Holland, B., 2009, “Sabre: A Cost Effective 

Engine Technology Combination for High Efficiency, High Performance and Low CO2 

Emissions.” Low Carbon Vehicles 2009: IMechE Conference Proceedings. 

32 Taylor, J., Fraser, N., Wieske, P., 2010, “Water Cooled Exhaust Manifold and Full Load 

EGR Technology Applied to a Downsized Direct Injection Spark Ignition Engine.” SAE 

Technical Paper Series No. 2010-01-0356. 

33 Roth, D.B., Keller, P, Becker, M., 2010, “Requirements of External EGRSystems for Dual 

Cam Phaser Turbo GDI Engines.” SAE Technical Paper Series No. 2010-01-0588. 

34 Burress, T.A., C.L. Coomer, S.L. Campbell, L.E. Seiber, R.H. Staunton, and J.P. 

Cunningham, 2008, "Evaluation of the 2007 Toyota Camry Hybrid Synergy Drive System." 

ORNL technical report TM-2007/190. 

35 ICF International, 2011. “Peer Review of Ricardo, Inc. Draft Report, ‘Computer 

Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas Emission Reduction in 

the 2020-2025 Timeframe’”. Contract No. EP-C-06-094, Work Assignment 4-04, Docket 

EPA-HQ-OAR-2010-0799, September 30, 2011. 

36 Ricardo Inc., “Project Report Computer Simulation of Light-duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020–2025 Timeframe,” 2011, Contract No. EPC-11-007, Work Assignment 0-12, Docket EPA-HQ-OAR-2010-0799, November, 2011. 

37 Systems Research and Applications Corporation (SRA), “Peer Draft Response to Peer 

Review of: Ricardo, Inc. Draft Report, ‘Computer Simulation of Light-Duty Vehicle 

Technologies for Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe’” EPA 

Contract No. EP-C-11-007, Work Assignment 0-12, Docket ID EPA-HQ-OAR-2010-0799, 

November, 2011. 

38 Moawad, A. and Rousseau, A., “Impact of Electric Drive Vehicle Technologies on Fuel 

Efficiency,” Energy Systems Division, Argonne National Laboratory, ANL/ESD/12-7, 

August 2012. 
 Technologies Considered in the Agencies’ Analysis

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39 “Impact of Friction Reduction Technologies on Fuel Economy,” Fenske, G. Presented at 

the March 2009 Chicago Chapter Meeting of the ‘Society of Tribologists and Lubricated 

Engineers’ Meeting, March 18th, 2009. Available at: 

http://www.chicagostle.org/program/2008-

2009/Impact%20of%20Friction%20Reduction%20Technologies%20on%20Fuel%20Econom

y%20-%20with%20VGs%20removed.pdf (last accessed July 9, 2009). 

40 “Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy 

Trends: 1975 Through 2011”, EPA-420-R-12-001a, March 2012,. Available at 

http://www.epa.gov/otaq/cert/mpg/fetrends/2012/420r12001a.pdf (last accessed August 7, 

2012).

41 Paul Whitaker, Ricardo, Inc., “Gasoline Engine Performance And Emissions – Future 

Technologies and Optimization,” ERC Symposium, Low Emission Combustion Technologies 

for Future IC Engines, Madison, WI, June 8-9, 2005, Docket EPA-HQ-OAR-2009-0472-

0155. Available at 

http://www.erc.wisc.edu/symposiums/2005_Symposium/June%208%20PM/Whitaker_Ricard

o.pdf (last accessed Nov. 9, 2008). 

42 “Development and Optimization of the Ford 3.5L V6 EcoBoost Combustion System,” 

Yi,J., Wooldridge, S., Coulson, G., Hilditch, J. Iyer, C.O., Moilanen, P., Papaioannou, G., 

Reiche, D. Shelby, M., VanDerWege, B., Weaver, C. Xu, Z., Davis, G., Hinds, B. Schamel, 

A. SAE Technical Paper No. 2009-01-1494, 2009, Docket EPA-HQ-OAR-2009-0472-2860. 

43 David Woldring and Tilo Landenfeld of Bosch, and Mark J. Christie of Ricardo, “DI Boost: 

Application of a High Performance Gasoline Direct Injection Concept,” SAE 2007-01-1410. 

Available at http://www.sae.org/technical/papers/2007-01-1410 (last accessed Nov. 9, 2008) 

44 Yves Boccadoro, Loïc Kermanac’h, Laurent Siauve, and Jean-Michel Vincent, Renault 

Powertrain Division, “The New Renault TCE 1.2L Turbocharged Gasoline Engine,” 28th 

Vienna Motor Symposium, April 2007. 

45 Tobias Heiter, Matthias Philipp, Robert Bosch, “Gasoline Direct Injection: Is There a 

Simplified, Cost-Optimal System Approach for an Attractive Future of Gasoline Engines?” 

AVL Engine & Environment Conference, September 2005. 

46 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost 

Analysis Pilot Study,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, 

Docket EPA-HQ-OAR-2009-0472-0149. 

47 Kaiser, M., Krueger, U., Harris, R., Cruff, L. “Doing More with Less - The Fuel Economy 

Benefits of Cooled EGR on a Direct Injected Spark Ignited Boosted Engine,” SAE Technical 

Paper Series, No. 2010-01-0589.

48 Kapus, P.E., Fraidl, G.K., Prevedel, K., Fuerhapter, A. “GDI Turbo – The Next Steps,” 

JSAE Technical Paper No. 20075355, 2007. 
 Technologies Considered in the Agencies’ Analysis

3-269 

49 “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to 

Reduce Light-duty Vehicle Carbon Dioxide Emissions,” EPA420-R-08-008, March 2008, 

Docket EPA-HQ-OAR-2009-0472-0132. 

50 Tim Lake, John Stokes, Richard Murphy, and Richard Osborne of Ricardo and Andreas 

Schamel of Ford-Werke, “Turbocharging Concepts for Downsized DI Gasoline Engines,” 

VKA/ika Aachen Colloquium 2003. Available at

http://cat.inist.fr/?aModele=afficheN&cpsidt=16973598 (last accessed Nov. 9, 2008). 

51http://media.gm.com/content/media/us/en/gm/news.detail.html/content/Pages/news/us/en/20

11/Jul/0722_cruze_diesel (last accessed: October 21, 2011) 

52 http://www.mazda.com/publicity/release/2010/201010/101020a.html (last accessed: 

October 21, 2011) 

53 U.S. EPA, “Computer Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas 

Emission Reduction in the 2020-2025 Timeframe”, Contract No. EP-C-11-007, Work 

Assignment 0-12, Docket EPA-HQ-OAR-2010-0799, November, 2011. 

54 General Motors, news release, “From Hybrids to Six-Speeds, Direct Injection And More, 

GM’s 2008 Global Powertrain Lineup Provides More Miles with Less Fuel” (released Mar. 6, 

2007). Available at 

http://www.gm.com/experience/fuel_economy/news/2007/adv_engines/2008-powertrainlineup-082707.jsp (last accessed Sept. 18, 2008). 

55 “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to 

Reduce Light-duty Vehicle Carbon Dioxide Emissions” Environmental Protection Agency, 

EPA420-R-08-008, March 2008, at page 17, Docket EPA-HQ-OAR-2009-0472-0132. 

56 FEV, Inc., “Light-Duty Technology Cost Analysis, Report on Additional Transmission, 

Mild Hybrid, and Valvetrain Technology Case Studies", Contract No. EP-C-07-069, Work 

Assignment 3-3, Docket EPA-HQ-OAR-2010-0799, November 2011. 

57 FEV, Inc., “Light-Duty Technology Cost Analysis, Report on Additional Transmission, 

Mild Hybrid, and Valvetrain Technology Case Studies", Contract No. EP-C-07-069, Work 

Assignment 3-3, Docket EPA-HQ-OAR-2010-0799, November 2011. 

58

 http://automobiles.honda.com/insight-hybrid/features.aspx?Feature=ima (last accessed on 

March 29, 2010; web page printout is contained in Docket EPA-HQ-OAR-2009-0472 as 

“Honda IMA webpage.pdf”). 

59

 “Latest Chevrolet Volt Battery Pack and Generator Details and Clarifications.” Lyle 

Dennis interview of Rob Peterson (GM) regarding the all-electric drive range of the GM Volt, 

August 29, 2007. Accessed on the Internet on June 30, 2009 at: http://gmvolt.com/2007/08/29/latest-Chevrolet-volt-battery-pack-and-generator-details-andclarifications/ 
 Technologies Considered in the Agencies’ Analysis

3-270 

60

 “Active Combination of Ultracapacitors and Batteries for PHEV ESS.” Bohn, T. U.S. 

Department of Energy 2009 Vehicle Technologies Merit Review, May 20, 2009, Docket 

EPA-HQ-OAR-2009-0472-0163. 

61 Nelson, P.A., Santini, D.J., Barnes, J. “Factors Determining the Manufacturing Costs of 

Lithium-Ion Batteries for PHEVs,” 24th World Battery, Hybrid and Fuel Cell Electric Vehicle 

Symposium and Exposition EVS-24, Stavenger, Norway, May 13-16, 2009 (www.evs24.org). 

62 Santini, D.J., Gallagher, K.G., and Nelson, P.A. “Modeling of Manufacturing Costs of 

Lithium-Ion Batteries for HEVs, PHEVs, and EVs,” Paper to be presented at the 25th World 

Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exposition, EVS-25, 

Shenzhen, China, November 5-9, 2010 (www.evs25.org). Advance draft provided by D.J. 

Santini, Argonne National Laboratory, August 24, 2010. 

63 Argonne National Laboratories, “Modeling the Performance and Cost of Lithium-Ion 

Batteries for Electric-Drive Vehicles”, Docket ID EPA-HQ-OAR-2010-0799 or NHTSA2010-0131-0101. 

64 Peer review of report: Argonne National Laboratories, “Modeling the Performance and 

Cost of Lithium-Ion Batteries for Electric-Drive Vehicles”, 2011 can be found in Docket ID 

EPA-HQ-OAR-2010-0799 or NHTSA-2010-0131-0101. 

65 Argonne National Laboratories BatPac model can be found in Docket ID EPA-HQ-OAR2010-0799 or NHTSA-2010-0131-0102. 

66 Nelson, P., Gallagher, K., Bloom, I. Dees, D.W. “BatPac Model Beta.” Microsoft ExcelTM-

based spreadsheet model with example generic inputs and outputs. The model is available in 

docket EPA-HQ-OAR-2010-0799 or NHTSA-2010-0131-0102. 

67 Anderman, M. (2010) Feedback on ARB’s Zero-Emission Vehicle Staff Technical Report 

of 11/25/2009 including attachment A: Status of EV Technology Commercialization, 

Advanced Automotive Batteries, January 6, 2010 

68 Frost & Sullivan (2009b) World Hybrid Electric and Electric Vehicle Lithium-ion Battery 

Market, N6BF-27, Sep 2009 

69 Barnett, B. (2009) “PHEV Battery Cost Assessment” TIAX LLC presentation at U.S. 

DOE/EERE 2009 Vehicle Technologies Program Annual Merit Review, May 19, 2009. 

Accessed on the Internet on November 14, 2011 at: 

http://www1.eere.energy.gov/vehiclesandfuels/pdfs/merit_review_2009/energy_storage/es_02

_barnett.pdf 

70 Boston Consulting Group (2010) Batteries for Electric Cars – Challenges, Opportunities, 

and the Outlook to 2020 

71 National Research Council (2010) Transitions to Alternative Transportation Technologies--

Plug-in Hybrid Electric Vehicles. 
 Technologies Considered in the Agencies’ Analysis

3-271 

72 K. G. Gallagher, P. A. Nelson, (2010) “An Initial BatPac Variation Study” Docket ID 

EPA-HQ-OAR-2010-0799 

73 “Hyundai ups tech ante with Sonata Hybrid,” Automotive News, August 2, 2010. 

74 “Chevrolet Stands Behind Volt With Standard Eight-Year, 100,000-Mile Battery 

Warranty,” GM Press release 

(http://media.gm.com/content/media/us/en/news/news_detail.brand_gm.html/content/Pages/n

ews/us/en/2010/July/0714_volt_battery) 

75 “Nissan’s new 2012 hybrid system aims for 1.8-L efficiency with a 3.5-L V6,” SAE 

Automotive Engineering Online, February 15, 2010. 

76 “Lithium-ion Battery,” Nissan Technological Development Activities (http://www.nissanglobal.com/EN/TECHNOLOGY/INTRODUCTION/DETAILS/LI-ION-EV/), 2009. 

77 Plug-in Hybrid Electric Vehicle Charging Infrastructure Review (November 2008) 

INL/EXT-08-15058, U.S. Department of Energy Vehicle Technologies Program – Advanced 

Vehicle Testing Activity. Last accessed on the Internet on November 14, 2011 at the 

following URL: http://avt.inl.gov/pdf/phev/phevInfrastructureReport08.pdf 

78 May, J.W., Mattilla, M. “Plugging In: A Stakeholder Investment Guide for Public ElectricVehicle Charging Infranstructure.” Rocky Mountain Institute – Project Get Ready, July 2009. 

Last accessed on the Internet on November 14, 2011 at the following URL: 

http://projectgetready.com/docs/Plugging%20In%20-

%20A%20Stakeholder%20Investment%20Guide.pdf 

79 “Electric Vehicle Charging Infrastructure Deployment Guidelines - British Columbia.” 

Report by the Electric Transportation Engineering Corporation for Natural Resources Canada 

and BCHydro. Last accessed on the Internet on November 14, 2011 at the following URL: 

http://www.bchydro.com/etc/medialib/internet/documents/environment/EVcharging_infrastru

cture_guidelines09.Par.0001.File.EV%20Charging%20Infrastructure%20Guidelines-BCAug09.pdf 

80 “Electrification Roadmap – Revolutionizing Transportation and Achieving Energy 

Security.” Electrification Coalition, November 2009. Accessed on the Internet on November 

14, 2011 at: http://www.electrificationcoalition.org/sites/default/files/SAF_1213_ECRoadmap_v12_Online.pdf 

81 “The Disappearing Spare Tire” Edmunds.com, May 11, 2011; EPA-HQ-OAR-2010-0799. 

http://www.edmunds.com/car-buying/the-disappearing-spare-tire.html (last accessed 

9/6/2011) 

82 see U.S Patent 5,227,425, Rauline to Michelin, July 13, 1993
 Technologies Considered in the Agencies’ Analysis

3-272 

83 “Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy 

Trends: 1975 Through 2011”, EPA420-R-12-001a, U.S. Environmental Protection Agency 

Office of Transportation and Air Quality, March 2012 

84 Lutsey, “Review of technical literature and trends related to automobile mass-reduction 

technology”, UCD-ITS-RR-10-10, May 2010 . EPA-HQ-OAR-2010-0799-0722. Available 

at http://pubs.its.ucdavis.edu/publication_detail.php?id=1390 (last accessed Jun. 10, 2012). 

85 Adrian Lund, IIHS, “The Relative Safety of Large and Small Passenger Vehicles.” EPAHQ-OAR-2010-0799. Available at 

http://www.nhtsa.gov/staticfiles/rulemaking/pdf/MSS/MSSworkshop-Lund.pdf (last accessed 

Jun. 10, 2012). 

86 Lutsey, “Review of technical literature and trends related to automobile mass-reduction 

technology”, UCD-ITS-RR-10-10, May 2010 . EPA-HQ-OAR-2010-2009-0722. Available 

at http://pubs.its.ucdavis.edu/publication_detail.php?id=1390 (last accessed Jun. 10, 2012). 

87 Lotus Engineering, Inc. “An Assessment of Mass Reduction Opportunities for a 2017-2020 

Model Year Vehicle program”, March 2010. 

88 Committee on the Assessment of Technologies for Improving Light-Duty Vehicle Fuel 

Economy; National Research Council, “Assessment of Fuel Economy Technologies for LightDuty Vehicles”, 2011. Available at http://www.nap.edu/catalog.php?record_id=12924 (last 

accessed Jun 27, 2012). 

89 Ford Sustainability Report 2010/11, http://corporate.ford.com/microsites/sustainabilityreport-2010-11/issues-climate-plan-economy (last accessed Aug. 26, 2011) EPA-HQ-OAR2010-0799. 

90 ETA, US Steel, Tata Steel, “The ACP ProcessTM as Applied to the Future Steel Vehicle” , 

Docket No. EPA-HQ-OAR-2010-0799 or NHTSA-2010-0131. Available at 

http://www.eta.com/index.php/engineering/product-design-development/the-acpprocess/success-stories/181-the-acp-process-as-applied-to-the-future-steel-vehicle- (last 

accessed Aug. 2, 2012). 

91 SAE World Congress, “Focus B-pillar ‘tailor rolled’ to 8 different thicknesses,” Feb. 24, 

2010. EPA-HQ-OAR-2010-0799. Available at http://www.sae.org/mags/AEI/7695 (last 

accessed Jun. 10, 2012). 

92 VW comments, Docket No. EPA-HQ-OAR-2010-0799 and NHTSA-2010-0131-0247, at 

18.

93 Committee on the Assessment of Technologies for Improving Light-Duty Vehicle Fuel 

Economy; National Research Council, “Assessment of Fuel Economy Technologies for LightDuty Vehicles”, June 3,2011. Available at http://www.nap.edu/catalog.php?record_id=12924 

(last accessed Jun 27, 2012). 
 Technologies Considered in the Agencies’ Analysis

3-273 

94 Malen, E. and K. Reddy, “Preliminary Vehicle Mass Estimation Using Empirical 

Subsystem Influence Coefficients,” Auto-Steel Partnership Report, May 2007. EPA-HQOAR-2010-0799-1193. Available at http://www.asp.org/~/media/Files/Autosteel/Research/Lightweighting/mass_compoundingpdf.ashx (last 

accessed Jun. 27, 2012). 

95 Bull, M., R. Chavali, A. Mascarin, “Benefit Analysis: Use of Aluminum Structures in 

Conjunction with Alternative Powertrain Technologies in Automobiles,” Aluminum 

Association Research Report, May 2008. Docket No. NHTSA-2010-0131-0097. Available at 

http://aluminumintransportation.org/downloads/IBIS-Powertrain-Study.pdf (last accessed 

Aug. 17, 2011). 

96 Bjelkengren, C, “The Impact of Mass Decompounding on Assessing the Value of Vehicle 

Lightweighting”, Docket NHTSA-2010-0131, Available at 

http://msl.mit.edu/theses/Bjelkengren_C-thesis.pdf (last accessed Aug 3, 2012). 

97 American Iron and Steel Institute (AISI), 2009. “New Study Finds Increased Use of 

Advanced High-Strength Steels Helps Decrease Overall Vehicle Weight.” EPA-HQ-OAR2010-0799-1149. Also Available at

http://www.steel.org/en/sitecore/content/Global/Document%20Types/News/2009/Auto%20-

%20New%20Study%20Finds%20Increased%20Use%20of%20Advanced%20HighStrength%20Steels.aspx (last accessed on June 11, 2012). . 

98 Ford, 2010. “The 5.0 Liter is Back: 2011 Ford Mustang GT Leads Class with 412 HP, Fuel 

Efficiency, Chassis Dynamics.” EPA-HQ-OAR-2010-0799. Available at 

http://media.ford.com/article_display.cfm?article_id=31645 (last accessed Jun. 10, 2012). 

99 Information copied from http://www.mazda.com/csr/environment/making_car/ 

weight_reduction.html and docketed. EPA Docket: EPA-HQ-OAR-2010-0799.

100 The New York Times, “Automakers Resolve to Drop a Few pounds”, Sept 2011. 

http://www.nytimes.com/2011/09/18/automobiles/autoshow/in-frankfurt-automakers-vow-todrop-a-few-pounds.html?_r=1&smid=tw-nytimeswheels&seid=auto EPA Docket EPA-HQOAR-2010-0799. 

101

 “Survey of vehicle mass-reduction technology trends and prospects” May 18 2010. EPAHQ-OAR-2010-0799. Also available at 

http://www.arb.ca.gov/msprog/levprog/leviii/meetings/051810/lutsey_its_may18_final.pdf 

102 NAS 2010, “Assessment of Fuel Economy Technologies for Light-Duty Vehicles”. June 

2010, page 7-14 

103 Frank Field, Randolph Kirchain and Richard Roth, Process cost modeling: Strategic 

engineering and economic evaluation of materials technologies, JOM Journal of the Minerals, 

Metals and Materials Society, Volume 59, Number 10, 21-32. Available at 
 Technologies Considered in the Agencies’ Analysis

3-274 

http://msl.mit.edu/publications/Field_KirchainCM_StratEvalMatls.pdf (last accessed Jun. 10, 

2012). 

104 Electricore/EDAG/GWU, “Mass Reduction for Light-Duty Vehicles for Model Years 2017-

2025”, NHTSA Docket NHTSA-2010-0131. 

105 “Peer Review for ‘Mass Reduction for Light-Duty Vehicles for Model Year 2017-2025’”, 

NHTSA Docket NHTSA-2010-0131. 

106 FEV, “ Light-Duty Vehicle Mass-Reduction and Cost Analysis – Midsize Crossover Utility 

Vehicle “. August 2012, EPA Docket: EPA-HQ-OAR-2010-0799. 

107 Systems Research and Application Corporation, “Peer Review of Demonstrating the 

Safety and Crashworthiness of a 2020 Model-Year, Mass-Reduced Crossover Vehicle (Lotus 

Phase 2 Report)”, February 2012, EPA docket: EPA-HQ-OAR-2010-0799. 

108 RTI International, “Peer Review of Lotus Engineering Vehicle Mass Reduction Study” 

EPA-HQ-OAR-2010-0799-0710, November 2010. 

109 RTI International, “Peer Review of Lotus Engineering Vehicle Mass Reduction Study” 

EPA-HQ-OAR-2010-0799-0710, November 2010. 

110 Frank Field, Randolph Kirchain and Richard Roth, Process cost modeling: Strategic 

engineering and economic evaluation of materials technologies, JOM Journal of the Minerals, 

Metals and Materials Society, Volume 59, Number 10, 21-32. Available at 

http://msl.mit.edu/pubs/docs/Field_KirchainCM_StratEvalMatls.pdf (last accessed Aug. 22, 

2011). EPA-HQ-OAR-2010-0799. 

111 U.S. EPA, Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel 

Economy Trends: 1975 Through 2011, http://epa.gov/otaq/fetrends.htm

112 EPA; Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy 

Trends: 1975 Through 2010; Figure 28, pg 69 EPA-HQ-OAR-2010-0799-0813.

113 EPA; Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy 

Trends: 1975 Through 2010; Table 13, pg 50. EPA-HQ-OAR-2010-0799-0813.

114 DeCicco, J. M. (2010). A fuel efficiency horizon for U.S. automo- biles. Technical report, 

University of Michigan, School of Natural Resources and Environment. Report prepared for 

The Energy Foundation. EPA-HQ-OAR-2010-0799-0816. Available online at 

http://energy.umich.edu/info/pdfs/Fuel%20Efficiency%20Horizon%20FINAL.pdf. 

115 Zoepf, Stephen. (2011) "Automotive Features: Mass Impact and Deployment 

Characterization" Masters thesis. Massachusetts Institute of Technology, Technology and 

Policy Program, Engineering Systems Division. EPA-HQ-OAR-2010-0799. Also available 

online: http://web.mit.edu/sloan-auto-lab/research/beforeh2/files/Zoepf_MS_Thesis.pdf
 Technologies Considered in the Agencies’ Analysis

3-275 

116 Murphy, John, Bank of America Merrill Lynch, “Car Wars 2010-2013,” July 15, 2009. 

EPA-HQ-OAR-2010-0799-0818 

117 Ellison, D. J., Clark, K. B., Fujimoto, T., and suk Hyun, Y. (1995). Product development 

performance in the auto industry: 1990s update. Technical report, International Motor Vehicle 

Program. 

118 Autocar, “VW’s New Platform for 60 Models,” November 12, 2009, 

http://www.autocar.co.uk/News/NewsArticle.aspx?AR=244881 EPA Docket EPA-HQOAR-2010-070 

119 Mayne, Eric, “Aligning Capacity, Demand Poses Ultimate Brain Teaser,” Wards Auto, 

July 29, 2008. EPA Docket EPA-HQ-OAR-2010-0799-0811 

120 Pope, Byron, “Ford’s Cleveland Engine No. 1 to Build 3.7L V-6,” Wards Auto, March 6, 

2009. EPA Docket EPA-HQ-OAR-2010-0799-0815 

121 Ford Motor Company, Ford Motor Company Business Plan Submitted to the House 

Financial Services Committee, December 2, 2008. EPA Docket EPA-HQ-OAR-2010-0799-

0814 

122 Rogers, Christina “GM to halve number of platforms globally,” Detroit News, August 10, 

2011. EPA Docket EPA-HQ-OAR-2010-0799-0817. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-1 

Chapter 4: Economic and Other Assumptions Used in the 

Agencies’ Analysis 

4.1 How the Agencies use the economic and other assumptions in their analyses 

Improving new vehicles’ fuel efficiency and reducing greenhouse gas (GHG) 

emissions provides direct benefits to their buyers and users by reducing fuel consumption and 

fuel costs throughout those vehicles’ lifetimes, stimulating increased vehicle miles traveled 

(VMT) through the fuel economy rebound effect, and often increasing vehicles’ driving range 

leading to less frequent refueling. At the same time, the reduction in fuel use that results 

from requiring higher fuel economy and reducing GHG emissions also produces wider 

benefits to the U.S. economy by lowering the cost of economic externalities that result from 

U.S. petroleum consumption and imports. This occurs because reducing U.S. oil consumption 

and imports lowers the potential costs from disruptions in the flow of oil imports, reduces the 

sensitivity of the U.S. economy to oil price shocks, and has the potential to reduce the global 

price of petroleum. Reducing fuel consumption and GHGs also lowers the economic costs of 

environmental externalities resulting from fuel production and use, including reducing 

potential future human health and economic damages from changes in the global climate 

caused by greenhouse gas emissions, and reducing the impacts on human health from 

emissions of criteria air pollutants. 

These social benefits are partly offset by the increase in fuel use that results from 

added vehicle use due to the fuel economy rebound effect, as well as by added costs from the 

increased congestion, crashes, and noise caused by increased vehicle use. They would also be 

offset by any loss in the utility that new vehicles provide to their buyers (and subsequent 

owners) if manufacturers include reductions in vehicles’ performance, carrying capacity, or 

comfort as part of their strategies to comply with higher fuel economy requirements and GHG 

standards. However, the agencies’ analyses supporting the final standards do not anticipate 

any such reductions in utility as being necessary, and the analysis includes the costs to 

manufacturers of preserving vehicle capabilities.a

 (For example, the costs of engine 

downsizing include the costs of turbocharging the engine to maintain its performance). The 

total economic benefits from requiring higher fuel economy and reducing GHG emissions are 

likely to be substantial, and EPA and NHTSA have developed detailed estimates of the 

economic benefits from adopting the final standards. 

This chapter discusses the common economic and other values used by both NHTSA 

and EPA in their rulemaking analyses. These inputs incorporate a range of forecast 

information, economic estimates, and input parameters. This chapter describes the sources 

that EPA and NHTSA have relied upon for this information, the rationale underlying each 

assumption, and the agencies’ estimates of specific parameter values. These common values 

are then used as inputs into each agency’s respective modeling and other analyses of the 

 

a

 Two exceptions – hybrid vehicles that may have some limited towing capacity, and electric vehicles – are 

discussed elsewhere. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-2 

economic benefits and costs of the EPA and NHTSA programs. While the underlying input 

values are common to both agencies, programmatic differences, and differences in the way 

each agency assesses its program result in differing benefit and cost estimates. This issue is 

discussed further in Section I.C of the preamble to the joint rulemaking. Unless otherwise 

noted, a summary of the public comments received on the topics described in this chapter and 

the agencies responses are included in the preamble Section II.E, Section III.H, and Section 

IV.X. 

4.2 What assumptions do the agencies use in the impact analyses? 

4.2.1 The on-road fuel economy “gap” 

4.2.1.1 Definition and past use by EPA and NHTSA 

In aggregate, actual fuel economy levels achieved by vehicles in on-road driving fall 

significantly short of their levels measured in the laboratory-like test conditions and two-cycle 

tests used under the CAFE program to determine the fuel economy ratings for different 

models for purposes of compliance with the CAFE and CO2 standards. The test procedure 

used to determine compliance is highly controlled, and does not reflect real-world driving in a 

variety of ways – real-world driving tends to be more aggressive than the Federal Test 

Procedure (FTP) and Highway Fuel Economy Test (HFET) test cycles used to establish 

compliance with the GHG and CAFE regulations. Real world driving tends to include more 

stops and starts and more rapid acceleration/deceleration, and may include the use of 

technologies like air-conditioning that reduce fuel economy but that are not exercised on these

test cycles.1 There are also a number of environmental elements that affect real-world 

achieved fuel economy which are not measured on the two cycle compliance test, such as 

wind resistance, road roughness, grade, temperature, and fuel energy content. The agencies’ 

analyses for this final rulemaking recognize this gap between compliance results and real 

world performance, and account for it by adjusting the fuel economy downward from its rated 

value. 

In December 2006, EPA adopted changes to its regulations on fuel economy labeling, 

which were intended to bring vehicles’ label fuel economy levels seen by consumers shopping 

for new vehicles closer to their actual on-road fuel economy levels.2 Comparisons of on-road 

and CAFE fuel economy levels developed by EPA as part of its 2006 Final Rule 

implementing new fuel economy labeling requirements for new vehicles indicated that actual 

on-road fuel economy for light-duty vehicles average about 20 percent lower than compliance 

fuel economy ratings.3 While there is great heterogeneity among individual drivers, as 

discussed in the referenced material, the 20 percent figure appears to represent an accurate 

average for modeling a fleet. For example, if the overall EPA fuel economy rating of a light 

truck is 20 MPG, the on-road fuel economy actually achieved by a typical driver of that 

vehicle is expected to be 16 mpg (20*.80). In its analysis supporting the Final Rule 

establishing CAFE standards for MY 2011, NHTSA employed EPA’s revised estimate of this 

on-road fuel economy gap in its analysis of the fuel savings resulting from alternative fuel 

efficiency standards. EPA and NHTSA likewise employed this fuel economy gap for 

estimating fuel savings in the MYs 2012-2016 rulemaking in the Interim Joint Technical 

Assessment Report (TAR) analysis for MYs 2017 and later, the proposal, and in this 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-3 

rulemaking establishing GHG and fuel economy standards for MY 2017 and later light duty 

vehicles.b

An analysis conducted by NHTSA confirmed that EPA’s estimate of a 20 percent gap 

between test and on-road fuel economy for the majority of vehicles is well-founded. NHTSA 

used data on the number of passenger cars and light trucks of each model year that were in 

service (registered for use) during each calendar year from 2000 through 2006; average fuel 

economy for passenger cars and light trucks produced during each model year; and estimates 

of average miles driven per year by cars and light trucks of different ages during each 

calendar year over that period. These data were combined to develop estimates of the usageweighted average fuel economy that the U.S. passenger car and light truck fleets would have 

achieved during each year from 2000 through 2006 under test conditions. 

Table 4-1 compares NHTSA’s estimates of fleet-wide average fuel economy under 

test conditions for 2000 through 2006 to the Federal Highway Administration’s (FHWA) 

published estimates of on-road fuel economy achieved by passenger cars and light trucks 

during each of those years. As it shows, FHWA’s estimates of fuel economy for passenger 

cars ranged from 21-23 percent lower than NHTSA’s estimates of its fleet-wide average value 

under test conditions over this period, and FHWA’s estimates of fuel economy for light trucks 

ranged from 16-18 percent lower than NHTSA’s estimates of its fleet-wide average value 

under test conditions. Thus, these results appear to confirm that the 20 percent on-road fuel 

economy gap represents a reasonable estimate for use in evaluating the fuel savings likely to 

result from more stringent fuel economy and CO2 standards in MYs 2017-2025. 

Table 4-1 Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks 

Compared to Reported Fuel Economy 

YEAR

PASSENGER CARS LIGHT-DUTY TRUCKS

NHTSA 

Estimated 

Test MPG

FHWA 

Reported 

MPG

Percent 

Difference

NHTSA 

Estimated 

Test MPG

FHWA 

Reported 

MPG

Percent 

Difference

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3%

2001 28.2 22.1 -21.7% 20.8 17.6 -15.5%

2002 28.3 22.0 -22.3% 20.9 17.5 -16.2%

2003 28.4 22.2 -21.9% 21.0 17.2 -18.0%

2004 28.5 22.5 -21.1% 21.0 17.2 -18.3%

2005 28.6 22.1 -22.8% 21.1 17.7 -16.3%

 

b

 The agencies did not adopt this approach in assessing benefits of the GHG emission standards and fuel 

consumption standards for heavy duty vehicles (76 FR 57106 (Sept. 15, 2011)) since compliance with those 

rules is assessed using test procedures that necessitate different modeling assumptions. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-4 

2006 28.8 22.5 -21.8% 21.2 17.8 -16.2%

Avg., 

2000-

2006

28.4 22.2 -22.0% 21.0 17.5 -16.7%

We are aware of two potential issues involved in these estimates. One, the estimates 

of total annual car and truck VMT are developed by the states and submitted to FHWA. Each 

state uses its own definition of a car and a truck. For example, some states classify minivans 

as cars and some as trucks. Thus, there are known inconsistencies with these estimates when 

evaluated separately for cars and trucks. Also, total gasoline consumption can be reasonably 

estimated from excise tax receipts, but separate estimates for cars and trucks are not available. 

For this reason, NHTSA and EPA are not using distinct on-road fuel economy gaps for cars 

and trucks, but one common value of 20 percent for both vehicle classes for purposes of 

estimating the fuel savings of the standards. This figure lies between the separate estimated 

for cars and light trucks reported in Table 4-1. 

Consistent with the MYs 2012-2016 rulemaking, the TAR, and the proposal, in this 

final rulemaking the agencies are assuming that the on-road fuel economy gap for liquid fuel 

is 20 percent. As in the TAR and proposal, the agencies assume that the overall energy 

shortfall for the electric drivetrain (for vehicles that have those instead of or in addition to 

gasoline engines) is 30 percent when driven on wall electricity. The 30 percent value was 

derived from the agencies’ engineering judgment based on several data points. Foremost 

among these, during the stakeholder meetings conducted prior to the Interim Joint TAR, 

confidential business information (CBI) was supplied by several manufacturers which 

indicated that electrically powered vehicles had greater variability in their on-road energy 

consumption than vehicles powered by internal combustion engines. Second, data from 

EPA’s 2006 analysis of the “five cycle” fuel economy label as part of the rulemaking 

discussed above potentially supported a larger on-road shortfall for vehicles with hybridelectric drivetrains4

 And third, heavy accessory load, extreme (both high and low) 

temperatures, and aggressive driving have deleterious impacts of unknown magnitudes on 

battery performance. As a counterpoint, CBI provided by several other manufacturers 

suggested that the on-road/laboratory differential attributable to electric operation should 

approach that of liquid fuel operation in the future. Consequently, 30 percent was judged by 

the agencies to be a reasonable estimate for the interim Joint TAR, and was carried into the 

analysis supporting the final rules. 

The recent 2011 Fuel Economy labeling rule similarly employs a 30% on-road 

shortfall for electric vehicles. 5

 Under the labeling program, for gasoline vehicles, there are 

two methods for getting label values: full 5-cycle or derived 5-cycle. Full 5-cycle means all 

five cycles are tested, and bag MPG results are used in a set of formulae to determine label 

MPG. Derived 5-cycle involves testing on the FTP and Highway tests and adjusting those 

values using regression-based formulae, to get label MPG values. The derived 5-cycle 

adjustment results in an ever-increasing adjustment in percentage terms. However, the data 

on which the derived 5-cycle formulae are based ends at roughly 70 MPG, where the 

adjustment is about 70% or an on-road gap of 30% (assuming that the five cycle formula 

represents the real world). For labeling purposes, lacking any EVs or PHEVs (or any vehicles 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-5 

beyond 70 MPG) in the database at the time this adjustment was derived, the adjustment was 

set at 70% for MPG values beyond 70 MPG. 

Electric vehicles are allowed and expected to use the derived 5-cycle method, which 

suggests that their on-road gap will be approximately 30% during the near future. Individual 

EVs may vary, and as additional data becomes available the agencies will consider whether 

the 30% average gap remains appropriate. 

The U.S. Coalition for Advanced Diesel Cars suggested that the on-road gap used in 

the proposal was overly conservative, and that advanced technology vehicles may have onroad gaps larger than 20%. The agencies recognize this potential issue – future changes in 

driver behavior or vehicle technology may change the on-road gap. The Coalition states that 

the EPA 2012 Trends Report shows that the gap for gasoline vehicles grew from 20% in 2005 

to 20.5% in 2010, and that therefore the 20% value used by the agencies is understated. We 

note that in recognition of the potentially greater gap for electrification technologies, the 

agencies are using a 30% adjustment for wall electricity; but more broadly, to the extent that 

the Coalition is suggesting that the agencies extrapolate the growth trend in the gap into the 

future, the agencies do not agree that the estimate of the future on road gap would be 

appropriately estimated by extrapolating the historical relationship between the test procedure 

and real world fuel consumption and emissions. That historical rate of change occurred as a 

result of the specific technological changes in vehicles over that timeframe. In the future, 

different technologies will be employed, that are likely to affect the gap differently. As an 

example, while some technologies such as electrification may increase the on-road gap, other 

off-cycle technologies such as tire pressure management systems, air conditioning 

improvements and aerodynamic improvements may decrease it. Thus, the agencies are 

continuing to use the same on-road gap methodology as in the proposal for this final 

rulemaking, but will monitor the EPA fuel economy database as these vehicles enter the fleet. 

Considerations in Future Years 

Looking forward to MYs 2017-2025, while the agencies do not forecast changes in 

most of the factors discussed above that contribute to the on-road gap in ways that would 

change our estimates, the agencies expect that two specific factors will change somewhat that 

could affect this analysis. Specifically, we anticipate changes in the energy content of fuels 

sold at retail as a result of the recent EPA Renewable Fuel Standard 2 (RFS2) rulemaking and 

E15 waiver decision,6

as well as a change in reference air conditioning efficiency as a result 

of the recent MYs 2012-2016 EPA Light Duty Greenhouse Gas rulemaking. 

4.2.1.2 Air Conditioning 

Air conditioning is a significant contributor to the on-road efficiency gap. While the 

air conditioner is turned off during the FTP and HFET tests, in real world use drivers often 

use air conditioning in warm, humid conditions. The air conditioning compressor can also be 

engaged during “defrost” operation of the heating system.7

 In the MYs 2012-2016 

rulemaking, the agencies estimated the average impact of an air conditioning system at 

approximately 14.3 grams over an SCO3 test for an average vehicle without any of the 

improved air conditioning technologies discussed in that rulemaking. For a 27 MPG (330 g 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-6 

CO2/mile) vehicle, this is approximately 20 percent of the total estimated on-road gap, or 

about 4 percent of total fuel consumption. 

In the MYs 2012-2016 rule, EPA estimated that 85 percent of MY 2016 vehicles 

would reduce their air conditioning-related CO2 emissions by 40 percent through the use of 

advanced air conditioning efficiency technologies.8 Incorporating this change would reduce 

the average on-road gap by about 2 percent in the reference case.c

 However, as shown in 

Chapter 5 of the joint TSD air conditioning-related fuel consumption does not proportionally 

decrease as overall engine efficiency improves. Unlike most technologies in this rulemaking, 

which have a multiplicative reduction on fuel consumption and CO2 emissions, the load due 

to air conditioning operation is relatively constant across engine efficiency and technology. 

As a consequence, as engine efficiency increases, air conditioning operation represents an 

increasing percentage of vehicular fuel consumption.d To some extent, these factors are 

expected to counterbalance, so the agencies therefore chose not to make an air conditioningrelated adjustment to the on-road gap for this rule. 

4.2.1.3 Fuel Energy Content 

Differences in fuel energy content between test conditions and real-world driving is 

another contributor to the on-road fuel economy gap. Two-cycle testing for CAFE and CO2

compliance is based on “certification fuel” which contains no ethanol (also known as E0). The 

on-road fuel economy gap is estimated with reference to the difference in fuel energy content 

between certification fuel and 2004 retail gallons,e

but this rule produces a reduction in 

petroleum based fuel consumption only.f Volumes of renewable fuels are statutorily fixed 

by the Renewable Fuel Standard, so the entirety of the energy savings will take place as 

reduced oil consumption. To estimate the petroleum fuel savings, we modify the on-road gap 

by the average difference in energy content between CY 2004 retail fuel used in the five cycle 

analysis and certification fuel. This results in an approximately 1% higher fuel economy than 

if no additional adjustment was made for fuel energy content, and corresponds to the greater 

energy content of certification gasoline as compared to 2004 retail gasoline. 

Where: 

Gap= 20% 

 

c

 A 330 gram (27 MPG) vehicle has an estimated gap of about 80 grams/mile (330/0.8). Under the EPA MY 

2016 rulemaking we assume a reduction of about 5 grams/mile from indirect air conditioning improvements, 

This A/C improvement is about 1-2% of total fuel consumption 

d

 As an example, the air conditioning load of 14.3 g/mile of CO2 is a smaller percentage (4.3%) of 330 g/mile 

than of 260 g/mile (5.4%). 

e

 The five cycle formula analysis is based on CY 2004 data. 

f

 Ethanol contains approximately 76,000 British Thermal Units (Btu) per gallon as compared to petroleum 

gasoline (Indolene), which contains approximately 115,000 Btu. Thus, a 10 percent ethanol (E10) blend 

contains approximately 3.3 percent less energy than a gallon of E0, and an E15 blend contains approximately 5.1 

percent less energy than a gallon of E0. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-7 

E0 BTU/Gallon = 115,000 

2004 BTU/Gallon = 113,912 (3.14% ethanol, 96.86% petroleum gasoline) 

A related adjustment in fuel energy was made in order to “match” fuel savings to the 

fuel prices used in this analysis. As discussed below, the agencies use liquid fuel prices from 

the Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) 2012 Early 

Release reference case.g By contrast, and as shown above, the gasoline savings from this rule 

are calculated as gallons of certification fuel, which is more energy dense than ethanol 

blended market fuel. To appropriately apply the AEO prices on a dollar per btu basis, we 

adjust our certification fuel savings upwards by approximately 5% (the difference between the 

energy content of E15 retail fuel and certification) when monetizing the fuel savings. This 

adjustment more appropriately reflects AEO projections of motor gasoline energy prices. 

4.2.2 Fuel prices and the value of saving fuel 

Projected future fuel prices are a critical input into the preliminary economic analysis 

of alternative fuel economy and GHG standards, because they determine the value of fuel 

savings both to new vehicle buyers and to society. For the final rule, EPA and NHTSA relied 

on the most recent fuel price projections from the U.S. Energy Information Administration’s 

(EIA) Annual Energy Outlook (AEO) for this analysis, the AEO 2012 Early Release 

Reference Case. The Reference Case forecasts inflation-adjusted (constant-dollar) retail 

gasoline and diesel fuel prices and represents the EIA’s most up-to-date estimate of future 

prices for petroleum products. In the Executive Summary to AEO 2012 Early Release, the 

Energy Information Administration describes the reference case. They state that: 

“Projections in the Annual Energy Outlook 2012 (AEO2012) Reference case focus on the 

factors that shape U.S. energy markets in the long term, under the assumption that current 

laws and regulations remain generally unchanged throughout the projection period. The 

AEO2012 Reference case provides the basis for examination and discussion of energy 

market trends and serves as a starting point for analysis of potential changes in U.S. 

energy policies, rules, or regulations or potential technology breakthroughs.”h

The Reference case projection is a business-as-usual trend estimate, given known 

technology and technological and demographic trends. The agency has published annual 

projections of energy prices and consumption levels for the U.S. economy since 1982 in its 

Annual Energy Outlooks. These projections have been widely relied upon by federal 

agencies for use in regulatory analysis and for other purposes. Since 1994, EIA’s annual 

forecasts have been based upon the agency’s National Energy Modeling System (NEMS), 

which includes detailed representation of supply pathways, sources of demand, and their 

interaction to determine prices for different forms of energy. 

 

g

 EIA projects that the average gallon of retail motor gasoline contains 5.040 mmbtu/barrel (Higher Heating 

Value), as compared to 5.253 mmbtu/barrel for pure motor gasoline, which is a difference of approximately 

4.5% (AEO 2012 Early Release table 147). 

h

 AEO 2012 ER overview - http://www.eia.gov/forecasts/aeo/er/pdf/0383er(2012).pdf 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-8 

Several commenters (Volkwagen, Consumer Federation of America, Environmental 

Defense Fund, Consumer’s Union, National Resources Defense Council, Union of Concerned 

Scientists) noted that the EIA future fuel price projections used in the proposal were similar to 

current prices, “modest,” or lower than expected. Other commenters noted the uncertainty 

projecting during this extended time period (National Automobile Dealers’ Association). No 

commenters offered alternative sources for fuel price projection, and in this final rulemaking, 

the agencies continue to rely upon EIA projections of future gasoline and diesel prices. 

As compared to the gasoline prices used in the proposal-, which relied on projections 

from AEO 2011, the AEO 2012 Early Release Reference Case fuel prices are somewhat 

higher. A comparison is presented below in Table 4-2. 

Table 4-2 Gasoline Prices for Selected Years in AEO 2011 and 2012 

(Presented in constant 2010$ and including all taxes) 

2015 2020 2030

AEO 2011 $3.17 $3.42 $3.68

AEO 2012 (ER) $3.53 $3.76 $4.04

The retail fuel price forecasts presented in AEO 2012 Early Release span the period 

from 2009 through 2035. Measured in constant 2010 dollars, the AEO 2012 Early Release 

Reference Case projections of retail gasoline prices during calendar year 2017 is $3.63 per 

gallon, rising gradually to $4.09 by the year 2035 (these values include federal and state 

taxes). However, valuing fuel savings over the full lifetimes of passenger cars and light 

trucks affected by the standards for MYs 2017-25 requires fuel price forecasts that extend 

through approximately 2060, approximately the last year during which a significant number 

of MY 2025 vehicles will remain in service.i Due to the difficulty in accurately projecting 

fuel prices over this long time span, the agencies have used a simple method for extrapolation 

over the out years. To obtain fuel price forecasts for the years 2036 and later, the agencies 

assume that retail fuel prices will continue to increase after 2035 at the average annual rate 

(0.8%) projected for 2017-2035 in the AEO 2012 Early Release Reference Case. The years 

between 2008 and 2016 were not included in the extrapolation due to the high volatility in the 

AEO projection for those years This assumption results in a projected retail price of gasoline 

that reaches $4.57 in 2050. 

The value of fuel savings resulting from improved fuel economy and reduced GHG 

emissions to buyers of light-duty vehicles is determined by the retail price of fuel, which 

includes federal, state, and any local taxes imposed on fuel sales. Total taxes on gasoline, 

including federal, state, and local levies, averaged $0.43 per gallon during 2008, while those 

 

i

 NHTSA defines the maximum lifetime of vehicles as the highest age at which more than 2 percent of those 

originally produced during a model year remain in service. In the case of light trucks, for example, this age has 

been assumed at 37 years for this analysis, see section 4.2.3. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-9 

levied on diesel averaged $0.46. Because fuel taxes represent transfers of resources from fuel 

buyers to government agencies, rather than real resources that are consumed in the process of 

supplying or using fuel, their value must be deducted from retail fuel prices to determine the 

value of fuel savings resulting from more stringent fuel efficiency and GHG standards to the 

U.S. economy.9 When calculating the value of fuel saved by an individual driver, however, 

these taxes are included as part of the value of realized fuel savings. Over the entire period 

spanned by the agencies’ analysis, this difference causes each gallon of fuel saved to be 

valued by about $0.36 (in constant 2010 dollars) more from the perspective of an individual 

vehicle buyer than from the overall perspective of the U.S. economy.j 

In the estimates of costs and benefits presented in the preamble and in the agencies’ 

RIAs, the agencies have included the full fuel savings over vehicles’ expected lifetimes, 

discounted to their present values using both 3 and 7 percent discount rates. Additional 

discussion of this approach can be found in preamble Sections III.H and IV.C.3. 

4.2.3 Vehicle Lifetimes and Survival Rates 

The agencies’ analyses of fuel savings and related benefits from adopting more 

stringent fuel economy and GHG standards for MYs 2017-2025 passenger cars and light 

trucks begin by estimating the resulting changes in fuel use over the entire lifetimes of 

affected cars and light trucks. The change in total fuel consumption by vehicles produced 

during each of these model years is calculated as the difference in their total lifetime fuel use 

over the entire lifetimes of these vehicles as compared to a reference case. 

The first step in estimating lifetime fuel consumption by vehicles produced during a 

model year is to calculate the number of those vehicles expected to remain in service during 

each future calendar year after they are produced and sold.k This number is calculated by 

multiplying the number of vehicles originally produced during a model year by the proportion 

expected to remain in service at the age they will have reached during each subsequent 

calendar year, often referred to as a “survival rate.” 

 

j

 For society, the fuel taxes represent a transfer payment. By contrast, an individual realizes savings from not 

paying the additional money. 

k

 Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are 

produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, 

age 2 during calendar year 2001, and to reach their maximum age of 30 years during calendar year 2029. 

NHTSA considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles 

originally produced during a model year remain in service. Applying these conventions to vehicle registration 

data indicates that passenger cars have a maximum age of 30 years, while light trucks have a maximum lifetime 

of 37 years. See Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and 

Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://wwwnrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed Sept. 9, 2011). For the Final Rule, the survivability schedules 

developed by Lu were updated using national vehicle registration data collected by R.L. Polk for calendar years 

2006 – 2010. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-10 

The proportions of passenger cars and light trucks expected to remain in service at 

each age are estimated from R.L. Polk vehicle registration data for calendar years 1970-2010, 

and are shown in Table 4-3. 10 Note that these survival rates were calculated against the preMY 2011 definitions of cars and light trucks, and are not projected to change over time in the 

analysis. The rates are applied to vehicles based on their regulatory class (passenger car or 

light truck) regardless of fuel type or level of technology. 

The survival and annual mileage estimates reported in this section’s tables reflect the 

convention that vehicles are defined to be of age 1 during the calendar year that coincides 

with their model year. Thus for example, model year 2017 vehicles will be considered to be 

of age 1 during calendar year 2017. This convention is used in order to account for the fact 

that vehicles produced during a model year typically are first offered for sale in June through 

September of the preceding calendar year (for example, sales of a model year typically begin 

in June through September of the previous calendar year, depending on manufacturer). Thus, 

virtually all of the vehicles produced during a model year will be in use for some or all of the 

calendar year coinciding with their model year, and they are considered to be of age 1 during 

that year.l

 

l

 A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for a 

small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to 

various ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the DOT’s Center 

for Statistical Analysis. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-11 

Table 4-3 Survival Rates 

VEHICLE AGE

ESTIMATED 

SURVIVAL 

FRACTION

CARS

ESTIMATED

SURVIVAL 

FRACTION

LIGHT TRUCKS

1 1.0000 1.0000

2 0.9878 0.9776

3 0.9766 0.9630

4 0.9614 0.9428

5 0.9450 0.9311

6 0.9298 0.9152

7 0.9113 0.8933

8 0.8912 0.8700

9 0.8689 0.8411

10 0.8397 0.7963

11 0.7999 0.7423

12 0.7556 0.6916

13 0.7055 0.6410

14 0.6527 0.5833

15 0.5946 0.5350

16 0.5311 0.4861

17 0.4585 0.4422

18 0.3832 0.3976

19 0.3077 0.3520

20 0.2414 0.3092

21 0.1833 0.2666

22 0.1388 0.2278

23 0.1066 0.2019

24 0.0820 0.1750

25 0.0629 0.1584

26 0.0514 0.1452

27 0.0420 0.1390

28 0.0337 0.1250

29 0.0281 0.1112

30 0.0235 0.1028

31 0.0000 0.0933

32 0.0000 0.0835

33 0.0000 0.0731

34 0.0000 0.0619

35 0.0000 0.0502

36 0.0000 0.0384

37 0.0000 0.0273
Economic and Other Assumptions Used in the Agencies' Analysis 

4-12 

4.2.4 VMT 

The second step in estimating lifetime fuel use by the cars or light trucks produced 

during a future model year is to calculate the total number of miles that they will be driven 

during each year of their expected lifetimes. To estimate total miles driven, the number of 

cars and light trucks projected to remain in use during each future calendar year is multiplied 

by the average number of miles a surviving car or light truck is expected to be driven at the 

age it will have reached in that year. Estimates of average annual miles driven by cars and 

light trucks of various ages were developed by NHTSA from the Federal Highway 

Administration’s 2009 National Household Travel Survey. This updates the schedules of 

annual miles driven that were used in the NPRM, which were based on the previous National 

Household Travel Survey, conducted in 2001. Additionally, the agencies have accounted for 

the higher usage of fleet vehicles, which include rental vehicles as well as those owned by 

corporations and government agencies. These represent about 20% of new vehicle sales, are 

not represented in the NHTS, and are driven much more intensively (on average) than 

household vehicles for the first several years of their lives before being absorbed into the 

household vehicle population.m The updated mileage schedules are reported in Table 4-4. 

These estimates represent the average number of miles driven by a surviving light duty 

vehicle at each age over its estimated full lifetime. To determine the number of miles a 

typical vehicle produced during a given model year is expected to be driven at a specific age, 

the average annual mileage for a vehicle of that model year and age is multiplied by the 

corresponding survival rate for vehicles of that age. NHTSA has documented these analyses 

in a memo to the docket. 

 

m

 Using the Annual Energy Outlook 2012, early release version of the National Energy Modeling System, 

developed and maintained by the U.S. Energy Information Administration, the proportion of fleet vehicles and 

their typical usage were calculated and then averaged into the household mileage accumulation schedules 

developed using the 2009 NHTS. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-13 

Table 4-4 CY 2009 Mileage Schedules based on NHTS Data 

VEHICLE AGE

ESTIMATED 

VEHICLE MILES 

TRAVELED

CARS

ESTIMATED

VEHICLE MILES 

TRAVELED 

LIGHT TRUCKS

1 14,700 15,974

2 14,252 15,404

3 14,025 14,841

4 13,593 14,435

5 13,324 14,038

6 13,064 13,650

7 12,809 12,590

8 11,378 12,192

9 11,087 11,810

10 10,806 11,443

11 10,535 11,091

12 10,273 10,755

13 10,021 10,434

14 9,779 10,129

15 9,547 9,839

16 9,324 9,564

17 9,111 9,305

18 8,908 9,061

19 8,714 8,833

20 8,530 8,620

21 8,356 8,423

22 8,192 8,241

23 8,037 8,075

24 7,892 7,923

25 7,757 7,788

26 7,632 7,668

27 7,516 7,563

28 7,410 7,473

29 7,314 7,399

30 7,227 7,341

31 7,151 7,298

32 7,083 7,270

33 7,026 7,258

34 6,979 7,246

35 6,941 7,233

36 6,912 7,221

37 6,894 7,209
Economic and Other Assumptions Used in the Agencies' Analysis 

4-14 

Projecting Vehicle Use in Future Years 

The estimates of annual miles driven by passenger cars and light trucks at each age 

were also adjusted to reflect projected future growth in average use for vehicles of all ages. 

Increases in the average number of miles cars and trucks are driven each year have been an 

important source of historical growth in total car and light truck use, and are expected to be a 

continued source of future growth in total light-duty vehicle travel as well. As an illustration 

of the importance of growth in average vehicle use, the total number of miles driven by 

passenger cars increased 35 percent from 1985 through 2005, equivalent to a compound 

annual growth rate of 1.5 percent.11 During that same time, however, the total number of 

passenger cars registered in the U.S. grew by only about 0.3 percent annually.nThus growth 

in the average number of miles automobiles are driven each year accounted for the remaining 

1.2 percent (= 1.5 percent - 0.3 percent) annual growth in total automobile use.o

 

In the U.S., overall change in VMT is attributable to factors such as employment rate, 

vehicle ownership rates, demographic trends, the cost of driving, and other macroeconomic 

factors. Rather than independently developing estimates of these factors, the agencies have 

used the DOT Volpe Center NEMSp

run which considers many of these factors, as a 

benchmark of total VMT levels in each future year. The VMT projections produced by this 

NEMS run are highly similar to those shown in AEO 2012 Early Release. The AEO 2012 

Early Release Reference Case projection of total car and light truck use and of the number of 

cars and light trucks in use suggest that their average annual use will continue to increase 

from 2010 through 2035, although at a slower rate of increase than shown in AEO 2011.q In 

calendar year 2030, total VMT projected in AEO 2012 Early Release is 10% lower than that 

projected in AEO 2011. 

 In order to develop reasonable estimates of future growth in the average number of 

miles driven by cars and light trucks of all ages, the agencies calculated the average rate of 

growth in the mileage schedules necessary for total car and light truck travel to closely 

correspond to AEO 2012 Early Release Reference Case. The growth rate in average annual 

 

n

 A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for a 

small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to 

various ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the agency’s 

Center for Statistical Analysis. 

o

See supra note k below. 

p

 This is the version of NEMS that is used in AEO 2012 Early Release, and modified by the Volpe center to hold 

new vehicle fuel economy constant after 2016. See TSD 1 for additional details. This version produces VMT 

estimates that are highly similar to those in the AEO 2012 Early Release 

q

 The agencies note that VMT growth has slowed, and because the impact of VMT is an important element in 

our benefit estimates, we will continue to monitor this trend to see whether this is a reversal in trend or 

temporary slowdown. See the 2009 National Household Travel Survey (http://nhts.ornl.gov/2009/pub/stt.pdf) 

and National Transportation Statistics 

(http://www.bts.gov/publications/national_transportation_statistics/html/table_04_09.html) 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-15 

car and light truck use produced by this calculation is approximately 0.6 percent per year.r 

When the 0.6% annual growth rate is combined with the MY 2010 base sales projection (TSD 

1), as well as the VMT, and survival schedules derived for this rule (previously discussed in 

sections 4.2.3 and 4.2.4) the estimated total vehicle usage closely approximates that contained 

in AEO 2012 ER (section 4.2.4.2). In the agencies’ respective modeling, a growth rate is

applied to the mileage figures reported in Table 4-4 (after adjusting vehicle populations for 

expected vehicle survival rates) to estimate average annual mileage during each calendar year 

analyzed and during the expected lifetimes of model year 2017-25 cars and light truckss

While EPA used this aggregate approach, accounting for all factors that influence 

reference case VMT in a single annual growth factor of 0.6%, NHTSA separated the changing 

cost of driving into a second factor, and therefore used a secular growth rate of 0.5%. We 

discuss the agencies’ two approaches in more detail below. 

 In the NHTSA analysis, the elasticity of annual vehicle use with respect to fuel cost 

per mile was applied to the percentage change in cost-per-mile travel between each future 

year’s vehicle and the cost per mile of a vehicle that was the same age in calendar year 2008. 

Thus, the NHTSA method of modeling the rebound effect captures changes both in fuel cost 

relative to calendar year 2008 and in the fuel consumption rates relative to that year, and 

inherently assumes the same response to changes in fuel price and fuel efficiency. 

Percent difference in VMT = (rebound effect * (FCPM2008– FCPMCAFE Alternative)/FCPM2008) 

 Where FCPM = fuel cost per mile

EPA developed the reference case VMT using the single growth factor discussed 

above; this single growth factor reflects driver responsiveness to changes in projected fuel 

prices and fuel efficiency, and other factors consistent with the AEO 2012 ER Reference 

Case. To develop EPA’s policy case VMT, EPA applied the elasticity of annual vehicle use 

with respect to fuel cost per mile corresponding to the 10 percent fuel economy rebound effect 

used in this analysis (i.e., an elasticity of annual vehicle use with respect to fuel cost per mile 

driven of -0.10; see Section 4.2.5) was applied to the percentage change in cost-per-mile 

travel between each future year’s vehicle under a policy case and a reference case in the same 

year. In other words, if the per mile fuel cost of a MY 2025 vehicle under the policy case was 

30% less than its counterpart under the reference case, the change in VMT would be 3%.t

 

 

r

 It was not possible to estimate separate growth rates in average annual use for cars and light trucks, because of 

the significant reclassification of light truck models as passenger cars discussed previously. 

s

 As indicated previously, a vehicle’s age during any future calendar year is uniquely determined by the 

difference between that calendar year and the model year when it was produced. 

t

Under the equation: percent difference in VMT = (rebound effect * (FCPMreference case – FCPMpolicy 

case)/FCPMreference case) and the rebound effect = 10%. A 30% change in fuel costs, multiplied by a 10% rebound 

effect would result in 3% additional driving.
Economic and Other Assumptions Used in the Agencies' Analysis 

4-16 

Thus, in the EPA analysis, the rebound effect only captures the impact of the EPA program 

relative to the reference case standards. Reference case changes in the cost of fuel or fuel 

consumption rates are handled separately. In other words, the change in VMT relative to the 

reference case is proportional to 

Percent difference in VMT = (rebound effect * (FCPMreference case – FCPMpolicy case)/FCPMreference case) 

Where FCPM = fuel cost per mile

As a result of the difference between the two approaches of capturing the future 

impact of fuel prices and fuel efficiency on VMT, the agencies also differ on how they 

ensured consistency with growth in total vehicle use across the entire fleet (including older 

vehicles already in the population that are not impacted by this rule). EPA uses the 0.6% 

annual estimate of secular VMT growth directly in the OMEGA model. By contrast, since the 

NHTSA model considers the effects of changes in fuel cost per mile since the 2009 NHTS as 

the reference point of the fuel economy rebound effect, in order to avoid double counting the 

effect of changes in fuel cost per mile, NHTSA uses a growth factor of 0.5%. NHTSA 

separated the growth rate because of its need for consistent results among the alternative 

scenarios and baselines it considered in this rulemaking. For the primary case, these 

approaches yield highly similar estimates of VMT schedules, and consequently of total VMT 

(see Table 4-5).

Thus, the agencies each made adjustments to vehicle use to account for projected 

changes in future fuel prices, fuel efficiency, and other factors that influence growth in 

average vehicle use during each future calendar year. Because the effects of fuel prices and 

other factors influencing growth in average vehicle use differ for each year, these adjustments 

result in different VMT schedules for each future model year. The net impact resulting from 

these adjustments is continued growth over time in the average number of miles that vehicles 

of each age are driven, although at slower rates than those observed from 1985 – 2005. u

4.2.4.1 VMT equationv

The following equation summarizes in mathematical form the adjustments that are 

made to the values of average miles driven by vehicle age derived from the 2009 NHTS to 

derive the estimates of average miles driven by vehicles of each model year during future 

 

u

 Observed aggregate VMT in recent years has actually declined (about 0.4% per year over the past decade), but 

it is unclear if the underlying cause is general shift in behavior or a response to a set of temporary economic 

conditions. 

v

 While both agencies applied the VMT calculation described above in the NPRM, for the final rule, in the EPA 

baseline calculation, the rebound effect is in effect embedded in the growth rate. Under the regulatory 

alternatives, the rebound effect is based solely on the percentage increase in fuel economy over the relevant 

baseline model year. NHTSA continued to follow the NPRM approach because of its requirement to produce an 

Environmental Impact Statement for the rule, and the need for consistent results among the alternative scenarios 

it considers. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-17 

calendar years that are used in this analysis. 

Where: 

Vy = Average miles driven in the base calendar year (from NHTSA analysis of 2009 NHTS data) by a 

vehicle of age y during the base calendar year 

G1 = Growth Rate 

YS = Years since the base calendar year 

R= Elasticity of VMT with respect to FCPM (-0.10). Note that, for EPA, this value is zero in the 

reference case since EPA’s G1 already incorporates impacts on VMT due to changes in FCPM. 

FCPMx,y = Fuel cost per mile of a vehicle of age y in calendar year x 

FCPMt,y = For NHTSA, the fuel cost per mile of a vehicle of age y in calendar year 2008. For EPA, this 

variable is identical to FCPMx,y in the reference case, and in the policy case this variable represents the 

fuel cost per mile of a reference case vehicle of age y in calendar year t 

For NHTSA, the base calendar year is 2008, for EPA 2009. 

In turn, fuel cost per mile of an age y vehicle in calendar year x is determined by the 

following equation, which can be extended for any number of fuels: 

Where: 

ECy= Electricity consumption of age y vehicle (in KWh) per mile 

EPx = Electricity Price (in $ per KWh) during calendar year x

GCy = Gasoline Consumption of age y vehicle (in gallons) per mile 

GPx = Gasoline Price (in $ per gallon) during calendar year x

DCy = Diesel Consumption of age y vehicle (in gallons) per mile 

DPx = Diesel Price (in $ per gallon) during calendar year x 

The NHTSA and EPA models project slightly different fuel costs per mile for vehicles 

affected by the proposed standards, because of the different structures of the respective 

agencies’ programs and the different technologies projected by each agency’s model to be 

used by vehicle manufacturers to comply with each program. Over the entire lifetimes of 

those vehicles, however, the agencies’ estimates of the number of miles they are expected to 

be driven differ by about 1% for cars and less than 1% for light trucks. For comparison,

Table 4-5 presents the agencies’ estimates of the average number of miles driven by model 

year 2021 and 2025 cars and light trucks at over their estimated average lifetimes.

Table 4-5 Survival Weighted Per-Vehicle Reference VMT used in the Agencies’ Analyses 

MY 2021 MY 2025

Cars Light 

Trucks

Cars Light 

Trucks

EPA 204,161 218,399 209,037 223,688

NHTSA 206,768 218,812 211,795 223,865
Economic and Other Assumptions Used in the Agencies' Analysis 

4-18 

4.2.4.2 Comparison to other VMT Projections 

As a check on their estimates of vehicle use, the agencies compared the forecasts of 

aggregate car and light truck VMT derived using the procedure described in preceding 

sections to the AEO 2012 Early Release reference case forecast of light duty VMT (see Figure 

4-1). Using the MY 2010 baseline, which includes the AEO 2012 ER fleet projection, the 

aggregate VMT projected in this analysis is within 1% of the AEO 2012 Light Duty 

projections over the time period 2017-2035.12 If AEO VMT is linearly extrapolated at the 

average growth rate of the period 2017-2035, the agencies’ estimates remain within 2% of this 

projection through 2050. EPA’s VMT estimates are compared to the AEO projection in the 

chart below, but based on the similarity of VMT schedules, is indicative of both agencies’ 

analysis.w

Figure 4-1 Comparison of AEO and Projected VMT 

4.2.5 Accounting for the fuel economy rebound effect 

The rebound effect refers to the increase in vehicle use that results when an increase in 

fuel efficiency lowers the cost per mile of driving, which can encourage people to drive more. 

Because this additional driving results in some fuel consumption and emissions, it results in 

smaller fuel savings and emissions reductions than would otherwise have resulted from the 

final standards. Thus the magnitude of the rebound effect is one determinant of the actual fuel 

savings and emission reductions that are likely to result from adopting stricter fuel economy 

 

w

See note p above. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-19 

or GHG emissions standards, and is an important parameter affecting EPA’s and NHTSA’s 

evaluation of standards for future model years.x 

The fuel economy rebound effect is measured directly by estimating the change in 

vehicle use, often expressed in terms of vehicle miles traveled (VMT), that results from a 

change in vehicle fuel efficiency.y However, analysts commonly measure the rebound effect 

indirectly, by estimating the change in vehicle use that results from a change in fuel cost per 

mile driven, which depends on both vehicle fuel efficiency and fuel prices.z

 When expressed 

as positive percentages, the elasticities of vehicle use with respect to fuel efficiency or permile fuel costs give the percentage increase in vehicle use that results from a one percent 

increase in fuel efficiency, or a one percent reduction in fuel cost per mile. For example, a 10 

percent rebound effect means that a 10 percent increase in fuel efficiency or a 10 percent 

decrease in fuel cost per mile is expected to result in a 1 percent increase in vehicle use. 

The fuel economy rebound effect for light-duty vehicles has been the subject of a large 

number of studies since the early 1980s. Although these studies have reported a wide range 

of estimates of its exact magnitude, they generally conclude that a significant rebound effect 

occurs when the cost per mile of driving decreases.aa The most common approach to 

estimating its magnitude has been to analyze household survey data on vehicle use, fuel 

consumption, fuel prices (often obtained from external sources), and other variables that 

influence travel demand. Other studies have relied on annual aggregate U.S. data. Finally, 

more recent studies have used annual data from individual states.bb

 

 

x

 The rebound effect discussed in this section refers solely to the effect of increased fuel efficiency on vehicle 

use, which has traditionally been referred to as the “fuel economy rebound effect.” More recently, some authors 

have referred to the fuel economy rebound effect as the “VMT rebound effect,” which helps distinguish it from 

other rebound effects that could potentially impact the fuel savings and emissions reductions from our standards 

such as the “indirect rebound effect,” which occurs when buyers of vehicles with improved fuel economy spend 

money they save on fuel purchases to buy other products and services that consume or use energy. The 

discussion in this section exclusively addresses the fuel economy rebound effect as traditionally defined, and 

uses this term throughout. The agencies received one comment on the proposed rulemaking suggesting that the 

agencies should attempt to quantify the indirect rebound effect; see preamble III.H.4 for a discussion of this 

topic. 

y

 Vehicle fuel efficiency is more often measured in terms of fuel consumption (gallons per mile) rather than fuel 

economy (miles per gallon) in rebound estimates. 

z

 Fuel cost per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per gallon 

(or multiplied by fuel consumption in gallons per mile), so this figure declines when a vehicle’s fuel efficiency 

increases. 

aa Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to 

increased fuel efficiency. Although their estimates of the adjustment period required for the rebound effect to 

reach its long-run magnitude vary, this long-run effect could be more appropriate for evaluating the fuel savings 

and emissions reductions resulting from stricter standards that would apply throughout the lifetime of future 

model year vehicles. 

bb In effect, these studies treat U.S. states as a data “panel” by applying appropriate estimation procedures to data 

consisting of each year’s average values of these variables for the separate states. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-20 

This section surveys these previous studies, summarizes recent work on the rebound 

effect,13 and explains the basis for the 10 percent rebound effect EPA and NHTSA are using 

in this rulemaking. 

4.2.5.1 Summary of historical literature on rebound effect 

It is important to note that a majority of the studies previously conducted on the 

rebound effect rely on data from the 1950-1990s. While these older studies provide valuable 

information on the potential magnitude of the rebound effect, studies that include more recent 

information (e.g., data within the last decade) may provide more reliable estimates of how this 

rule will affect future driving behavior. Therefore, the more recent studies have been 

described in more detail in Section 4.2.5.2 below. 

Estimates based on aggregate U.S. vehicle travel data published by the U.S. 

Department of Transportation, Federal Highway Administration, covering the period from 

roughly 1950 to 1990, have found long-run rebound effects on the order of 10-30 percent. 

Some of these studies are summarized in the following table. 

Table 4-6 Estimates of the Rebound Effect Using U.S. Aggregate Time-Series Data on Vehicle Travel1 

AUTHOR

(YEAR)

SHORT-RUN LONG-RUN TIME PERIOD

Mayo & Mathis 

(1988)

22% 26% 1958-84

Gately (1992) 9% 9% 1966-88

Greene (1992) Linear 5-19%

Log-linear 13%

Linear 5-19%

Log-linear 13%

1957-89

Jones (1992) 13% 30% 1957-89

Schimek (1996) 5-7% 21-29% 1950-94

1 Source: Sorrell and Dimitropolous (2007) table 4.6. 

Table 4-7 Estimates of the Rebound Effect Using U.S. State Level Data1 

AUTHOR

(YEAR)

SHORT-RUN LONG-RUN TIME PERIOD

Haughton & Sarkar 

(1996)

9-16% 22% 1973-1992

Small and Van Dender 

(2005 and 2007a)

4.5%

2.2%

22.2%

10.7%

1966-2001 

1997-2001 

Hymel, Small and Van 

Dender (2010)

4.7%

4.8%

24.1%

15.9%

1966-2004

1984-2004

1 Source: Sorrell and Dimitropolous (2007) table 4.7 and the agencies’ addition of recent work by Small and Van 

Dender (2007a) and Hymel, Small, and Van Dender (2010) discussed in section 4.2.5.2. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-21 

While studies using national (Table 4-6) and state level (Table 4-7) data have found 

relatively consistent long-run estimates of the rebound effect, household surveys display more 

variability (Table 4-8). One explanation is that these studies consistently find that the 

magnitude of the rebound effect differs according to the number of vehicles a household 

owns, and the average number of vehicles owned per household differs among the surveys 

used to derive these estimates. Still another possibility is that it is difficult to distinguish the 

impact of residential density on vehicle use from that of fuel prices, since households in urban 

areas are likely to face higher fuel prices.14 

Table 4-8 Estimates of the Rebound Effect Using U.S. Survey Data1

Author

(year)

Short-Run Long-Run Time Period

Goldberg (1996) 0% CES 1984-90

Greene, Kahn, and 

Gibson (1999a)

23% EIA RTECS

1979-1994

Pickrell & Schimek 

(1999)

4-34% NPTS 1995 

Single year

Puller & Greening (1999) 49% CES 1980-90

Single year, cross-sectional

West (2004) 87% CES 1997

Single year

1 Source: Sorrell and Dimitropolous (2007) table 4.8 and the agencies’ addition of Pickrell & Schimek (1999). 

It is important to note that some of these studies actually quantify the price elasticity 

of gasoline demand (e.g., Puller & Greening15) or the elasticity of VMT with respect to the 

price of gasoline (e.g., Pickrell & Schimek), rather than the elasticity of VMT with respect to 

fuel efficiency or the fuel cost per mile of driving. These latter measures more closely match 

the definition of the fuel economy rebound effect. In fact, most studies cited above do not 

estimate the direct measure of the fuel economy rebound effect (i.e., the increase in VMT 

attributable to an increase in fuel efficiency). This topic is discussed in more detail in Section 

4.2.5.2. 

Another important distinction among studies of the rebound effect is whether they 

assume that the effect is constant, or varies over time in response to the absolute levels of fuel 

costs, personal income, or household vehicle ownership. Most studies using aggregate annual 

data for the U.S. assume a constant rebound effect, although some of these studies test 

whether the effect can vary as changes in retail fuel prices or average fuel efficiency alter fuel 

cost per mile driven. Many studies using household survey data estimate significantly 

different rebound effects for households owning varying numbers of vehicles, with most 

finding that the rebound effect is larger among households that own more vehicles.cc Finally, 

 

cc Six of the household survey studies evaluated in Table 4-9 found that the rebound effect varies in relation to 

the number of household vehicles. Of those six studies, four found that the rebound effect rises with higher 

vehicle ownership, and two found that it declines. The four studies with rebound estimates that increase with 

higher household vehicle ownership are: Greene, David L., and Patricia S. Hu, “The Influence of the Price of 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-22 

one recent study using state-level data concludes that the rebound effect varies directly in 

response to changes in personal income and the degree of urbanization of U.S. cities, as well 

as fuel costs. 

In order to provide a more comprehensive overview of previous estimates of the 

rebound effect, NHTSA reviewed 22 studies of the rebound effect conducted from 1983 

through 2005. The agency then performed a detailed analysis of the 66 separate estimates of 

the long-run rebound effect reported in these studies, which is summarized in Table 4-9

below.dd As the table indicates, these 66 estimates of the long-run rebound effect range from 

as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. Limiting the 

sample to 50 estimates reported in the 17 published studies of the rebound effect yields the 

same range, but a slightly higher mean estimate (24 percent). 

The type of data used and authors’ assumption about whether the rebound effect varies 

over time have important effects on its estimated magnitude. The 34 estimates derived from 

analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the longrun rebound effect, while the mean of 23 estimates based on household survey data is 

considerably larger (31 percent), and the mean of 9 estimates based on state data (25 percent) 

is close to that for the entire sample. The 37 estimates assuming a constant rebound effect 

produce a mean of 23 percent, identical to the mean of the 29 estimates reported in studies 

that allowed the rebound effect to vary in response to fuel prices, vehicle ownership, or 

household income.

Table 4-9 Summary Statistics for Estimates of the Rebound Effect 

 

Gasoline on Vehicle Use in Multivehicle Households,” Transportation Research Record 988, pp. 19-24 (Docket 

EPA-HQ-OAR-2010-0799); Hensher, David A., Frank W. Milthorpe, and Nariida C. Smith, “The Demand for 

Vehicle Use in the Urban Household Sector: Theory and Empirical Evidence,” Journal of Transport Economics 

and Policy, 24:2 (1990), pp. 119-137 (Docket EPA-HQ-OAR-2010-0799); Walls, Margaret A, Alan J. Krupnick, 

and H. S. Hood, “Estimating the Demand for Vehicle-Miles Traveled Using Household Survey Data: Results 

from the 1990 Nationwide Personal Transportation Survey,” Discussion Paper ENR 93-25, Energy and Natural 

Resources Division, Resources for the Future, Washington, D.C., 1993; and West, Rachel, and Don Pickrell, 

“Factors Affecting Vehicle Use in Multiple-Vehicle Households,” 2009 National Household Travel Survey 

Workshop, June 2011, http://onlinepubs.trb.org/onlinepubs/conferences/2011/NHTS1/West.pdf (Docket EPAHQ-OAR-2010-0799). The two studies with rebound estimates that decrease with higher household vehicle 

ownership are Mannering, Fred L. and Clifford Winston, “A Dynamic Empirical Analysis of Household Vehicle 

Ownership and Utilization, Rand Journal of Economics 16:2 (1985), pp. 215-236 (Docket EPA-HQ-OAR-2010-

0799), and Greene, David L., James R. Kahn, and Robert C. Gibson, “Fuel Economy Rebound Effect for 

Household Vehicles,” The Energy Journal, 20:3 (1999), 1-21 (Docket EPA-HQ-OAR-2010-0799) (note that the 

latter showed virtually no difference in the rebound effect as households went from 1 to 2, a moderate decline 

from 2 to 3 vehicles, and a slight increase from 3 to 4 vehicles; on balance, the rebound estimate for households 

with 4 vehicles was slightly lower than for households with 1 or 2 vehicles). 

dd In some cases, NHTSA derived estimates of the overall rebound effect from more detailed results reported in 

the studies. For example, where studies estimated different rebound effects for households owning different 

numbers of vehicles but did not report an overall value, the agency computed a weighted average of the reported 

values using the distribution of households among vehicle ownership categories. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-23 

4.2.5.2 Summary of recent studies and analyses of the rebound effect 

More recent studies published between 2007 and 2010 indicate that the rebound effect 

has decreased over time as incomes have risen and, until recently, fuel costs as a share of total 

monetary travel costs have generally decreased.ee

 One theoretical argument for why the 

rebound effect should vary over time is that the responsiveness to the fuel cost of driving will 

be larger when it is a larger proportion of the total cost of driving. For example, as incomes 

rise, the responsiveness to the fuel cost per mile of driving will decrease if people view the 

time cost of driving – which is likely to be related to their income levels – as a larger 

component of the total cost. 

Small and Van Dender combined time series data for each of the 50 States and the 

District of Columbia to estimate the rebound effect, allowing the magnitude of the rebound to 

vary over time.16

 For the time period from 1966-2001, their study found a long-run rebound 

effect of 22.2 percent, which is consistent with previously published studies. But for the most 

recent five year period (1997-2001), the long-run rebound effect decreased to 10.7 percent. 

Furthermore, when the authors updated their estimates with data through 2004, the long-run 

rebound effect for the most recent five year period (2000-2004) dropped to 6 percent.17

 

Finally, when the Small methodology was used to project the future rebound effect, estimates 

of the rebound effect throughout 2010-2030 were below 6 percent given a range of future 

gasoline price and income projections.18

 

In 2010, Hymel, Small and Van Dender extended the Small and Van Dender model by 

adding congestion as an endogenous variable.19

 Although controlling for congestion 

significantly increased their estimates of the rebound effect, Hymel, Small and Van Dender 

 

ee While real gasoline prices have varied over time, fuel costs (which reflect both fuel prices and fuel efficiency) 

as a share of total vehicle operating costs declined substantially from the mid-1970s until the mid-2000s when 

the share increased modestly (see Greene (2012)). Note that two studies discussed in this section, Small and Van 

Dender (2007) and Hymel, Small, and Van Dender (2010), find that the rebound effect is more strongly 

dependent on income than on fuel costs. A third study, Greene (2012), did not directly test the effect of fuel 

costs on the rebound effect, but found evidence supporting the effect of income. Several other studies have 

shown that the rebound effect rises with household vehicle ownership (see section 4.2.5.1), which has generally 

increased with income. 

Range Distribution

Category of Estimates Number of

Studies

Number of

Estimates Low High Median Mean Std. Dev.

All Estimates 22 66 7% 75% 22% 23% 14%

Published Estimates 17 50 7% 75% 22% 24% 14%

U.S. Time-Series Data 7 34 7% 45% 14% 18% 9%

Household Survey Data 13 23 9% 75% 31% 31% 16%

Pooled U.S. State Data 2 9 8% 58% 22% 25% 14%

Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16%

Variable Rebound Effect: (1) 10 29 10% 45% 23% 23% 10%
Economic and Other Assumptions Used in the Agencies' Analysis 

4-24 

also found that the rebound effect was declining over time. For the time period from 1966-

2004, they estimated a long-run rebound effect of 24 percent, while for 2004 they estimated a 

long-run rebound effect of 13 percent. 

Research conducted by David Greene in 2008-2009 under contract with EPA further 

appears to support the theory that the magnitude of the rebound effect is declining over time 

and may be as low as zero.20

 Over the entire time period analyzed (1966-2007), Greene found 

that fuel prices had a statistically significant impact on VMT, while fuel efficiency did not, 

which is similar to Small and Van Dender’s prior finding. When Small and Van Dender 

tested whether the elasticity of vehicle travel with respect to the price of fuel was equal to the 

elasticity with respect to the rate of fuel consumption (gallons per mile), they found that the 

data could not reject this hypothesis. Therefore, Small and Van Dender estimated the rebound 

effect as the elasticity of travel with respect to fuel cost per mile. In contrast, Greene’s 

research showed that the hypothesis of equal elasticities for gasoline prices and fuel efficiency 

can be rejected. In spite of this result, Greene also tested Small and Van Dender’s 

formulation which allows the elasticity of fuel cost per mile to decrease with increasing per 

capita income. The results of estimation using national time series data confirmed the results 

obtained by Small and Van Dender using a time series of state level data. When using 

Greene’s preferred functional form, the projected rebound effect is approximately 12 percent 

in 2008, and drops to 10 percent in 2020 and to 9 percent in 2030. 

Since there has been little variation in fuel efficiency in the data over time, isolating 

the impact of fuel efficiency on VMT can be difficult using econometric analysis of historical 

data. Therefore, studies that estimate the rebound effect using time-series data often examine 

the impact of gasoline prices on VMT, or the combined impact of both gasoline prices and 

fuel efficiency on VMT, as discussed above. However, these studies may overstate the 

potential impact of the rebound effect resulting from this rule, if people are more responsive 

to changes in gasoline prices than to changes in fuel efficiency itself. Recent work conducted 

by Kenneth Gillingham included an estimate of the elasticity of VMT with respect to the price 

of gasoline of -0.17, while his corresponding estimate of the elasticity of VMT with respect to 

fuel economy was only 0.05.21

 While this research pertains specifically to California, this 

finding suggests that the common assumption that consumers respond similarly to changes in 

gasoline prices and changes in fuel efficiency may overstate the magnitude of the rebound 

effect. The agencies stated in the draft TSD accompanying the NPRM that more research is 

needed in this area, and sought comment on this aspect of the rebound effect. No comments 

were received on this specific issue. 

Another question discussed by Gillingham is whether consumers actually respond the 

same way to an increase in the cost of driving compared to a decrease in the cost of driving. 

There is some evidence in the literature that consumers are more responsive to an increase in 

prices than to a decrease in prices. At the aggregate level, Dargay & Gately and SentenacChemin have shown that demand for transportation fuel is asymmetric.22,23 In other words, 

given the same size change in prices, the response to a decrease in gasoline price is smaller 

than the response to an increase in gasoline price. Gately has shown that the response to an 

increase in oil prices can be on the order of five times larger than the response to a price 

decrease.24

 Furthermore, Dargay & Gately and Sentenac-Chemin find evidence that 

consumers respond more to a large shock than a small, gradual change in fuel prices. Since 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-25 

these standards would decrease the cost of driving gradually over time, it is possible that the 

rebound effect would be much smaller than some of the historical estimates included in the 

literature. Although these types of asymmetric responses have been noted at the aggregate 

level on oil and gasoline consumption, little research has been done on these same phenomena 

in the context of changes in vehicle fuel efficiency and the resulting rebound effect. The 

agencies stated in the draft TSD accompanying the NPRM that more research in this area is 

also important, and sought comment on this aspect of the rebound effect. No comments were 

received on this specific issue. 

Other recent studies came to our attention after we finalized our estimate of the 

rebound effect used in the analysis for our final rules.25 We will examine these and other new 

studies on this topic for future rulemakings. 

4.2.5.3 Basis for rebound effect used by EPA and NHTSA in this rule 

As the preceding discussion indicates, there is a wide range of estimates for both the 

historical magnitude of the rebound effect and its projected future value, and there is some 

evidence that the magnitude of the rebound effect appears to be declining over time. 

Nevertheless, NHTSA requires a single point estimate for the rebound effect as an input to its 

analysis, although a range of estimates can be used to test the sensitivity to uncertainty about 

its exact magnitude. Based on a combination of historical estimates of the rebound effect and 

more recent analyses, an estimate of 10 percent for the rebound effect was used for this final 

rule (i.e., we assume a 10 percent decrease in fuel cost per mile from our standards would 

result in a 1 percent increase in VMT). EPA uses a range of 0–20 percent for sensitivity 

testing, while NHTSA uses 5-20 percent. 

As Table 4-6, Table 4-7, Table 4-8, and Table 4-9 indicate, the 10 percent figure is on 

the low end of the range reported in previous research. However, some recent research – 

particularly that conducted by Hymel, Small and Van Dender, Small and Van Dender, and 

Greene – reports evidence that the magnitude of the rebound effect is likely to be declining 

over time. Furthermore, for the reasons described in section 4.2.5.2, historical estimates of 

the rebound effect may overstate the effect of a gradual decrease in the cost of driving due to 

our standards. 

As a consequence, the agencies concluded that a value on the low end of the historical 

estimates reported in Table 4-6, Table 4-7, Table 4-8, and Table 4-9 is likely to provide a 

more reliable estimate of its magnitude during the future period spanned by the agencies’ 

analyses of the impacts of this rulemaking. The 10 percent estimate lies within the 10-30 

percent range of estimates for the historical rebound effect reported in most research, and at 

the upper end of the 5-10 percent range of estimates for the future rebound effect reported in 

the recent studies by Small and Greene. In summary, the 10 percent value was not derived 

from a single point estimate from a particular study, but instead represents a reasonable 

compromise between historical estimates of the rebound effect and forecasts of its projected 

future value. 

In their proposed rules, the agencies sought comment and new data on alternative 

methods for estimating the rebound effect over the period that our rulemaking would go into 

effect. In particular, the agencies sought comment and data on the potential that the rebound 

effect could be lower than the estimates in the literature if drivers respond more to changes in 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-26 

fuel prices than fuel efficiency, price rises than price decreases, and price shocks than gradual 

price changes (as discussed in section 4.2.5.2). EPA also sought comment and data on the 

rebound effect for consumers driving vehicles powered by grid electricity. We believe more 

research on these topics is important. During the public comment period, the agencies did not 

receive any comments on these topics and the few comments we did receive on the fuel 

economy rebound effect did not provide persuasive new evidence. Hence, EPA and NHTSA 

have elected to continue to use the 10 percent estimate of the rebound effect in the analyses 

supporting this final rulemaking. The agencies will review the estimate of the rebound effect 

again for any future rulemakings based on the best available information at that time. 

4.2.6 Benefits from additional driving 

The increase in travel associated with the rebound effect produces additional benefits 

to vehicle owners, which reflect the value to drivers and other vehicle occupants of the added 

(or more desirable) social and economic opportunities that become accessible with additional 

travel. The analysis estimates the economic benefits from increased rebound-effect driving as 

the sum of fuel costs drivers incur plus the consumer surplus they receive from the additional 

accessibility it provides. As evidenced by the fact that drivers elect to make more frequent or 

longer trips when the cost of driving declines, the benefits from this added travel exceed 

drivers’ added outlays for the fuel consumed. The amount by which the benefits from this 

increased driving travel exceed its increased fuel costs measures the net benefits drivers 

receive from the additional travel, usually are referred to as increased consumer surplus. 

The agencies’ analysis estimates the economic value of the increased consumer 

surplus provided by added driving using the conventional approximation, which is one half of 

the product of the decline in vehicle operating costs per vehicle-mile and the resulting 

increase in the annual number of miles driven. Because it depends on the extent of 

improvement in fuel economy, the value of benefits from increased vehicle use changes by 

model year and varies among alternative standards. Under even those alternatives that would 

impose the highest standards, however, the magnitude of the consumer surplus from 

additional vehicle use represents a small fraction of this benefit. 

4.2.7 Added costs from increased vehicle use 

While it provides some benefits to drivers, increased vehicle use associated with the 

rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and 

highway noise. Depending on how the additional travel is distributed over the day and on 

where it takes place, additional vehicle use can contribute to traffic congestion and delays by 

increasing traffic volumes on facilities that are already heavily traveled. These added delays 

impose higher costs on drivers and other vehicle occupants in the form of increased travel 

time and operating expenses. Because drivers do not take these added costs into account in 

deciding when and where to travel, they must be accounted for separately as a cost of the 

added driving associated with the rebound effect. 

Increased vehicle use due to the rebound effect may also increase the costs associated 

with traffic accidents. Drivers may take account of the potential costs they (and their 

passengers) face from the possibility of being involved in an accident when they decide to 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-27 

make additional trips. However, they probably do not consider all of the potential costs they 

impose on occupants of other vehicles and on pedestrians when accidents occur. Thus any 

increase in these “external” accident costs must be considered as another cost of additional 

rebound-effect driving. Like increased delay costs, any increase in these external accident 

costs caused by added driving is likely to depend on the traffic conditions under which it takes 

place, since accidents are more frequent in heavier traffic (although their severity may be 

reduced by the slower speeds at which heavier traffic typically moves). 

Finally, added vehicle use from the rebound effect may also increase traffic noise. 

Noise generated by vehicles causes inconvenience, irritation, and potentially even discomfort 

to occupants of other vehicles, to pedestrians and other bystanders, and to residents or 

occupants of surrounding property. Because these effects are unlikely to be taken into 

account by the drivers whose vehicles contribute to traffic noise, they represent additional 

externalities associated with motor vehicle use. Although there is considerable uncertainty in 

measuring their value, any increase in the economic costs of traffic noise resulting from added 

vehicle use must be included together with other increased external costs from the rebound 

effect. To estimate the increased external costs caused by added driving due to the rebound 

effect, EPA and NHTSA rely on estimates of congestion, accident, and noise costs caused by 

automobiles and light trucks developed previously by the Federal Highway Administration.26 

NHTSA employed these estimates previously in its analysis accompanying the MY 2011 final 

rule, and the agencies jointly applied them in the MYs 2012-2016 rulemaking and in the 

proposal to this rulemaking, and the agencies continue to find them appropriate for this final 

rule. The values are intended to measure the increases in costs (or “marginal” external costs) 

from added congestion, property damages and injuries in traffic accidents, and noise levels 

caused by automobiles and light trucks that are borne by persons other than their drivers and 

occupants. 

Updated to 2010 dollars, FHWA’s “Middle” estimates for marginal congestion, 

accident, and noise costs caused by automobile use amount to 5.6 cents, 2.4 cents, and 0.1 

cents per vehicle-mile (for a total of 8.1 cents per mile), while those for pickup trucks and 

vans are 5.1 cents, 2.7 cents, and 0.1 cents per vehicle-mile (for a total of 7.8 cents per 

mile).27, ff

 These costs are multiplied by the mileage increases attributable to the rebound 

effect to yield the estimated increases in congestion, accident, and noise externality costs 

during future years. 

 

ff The Federal Highway Administration’s estimates of these costs agree closely with some other recent estimates. 

For example, recent published research conducted by Resources for the Future (RFF) estimates marginal 

congestion and external accident costs for increased light-duty vehicle use in the U.S. to be 3.5 and 3.0 cents per 

vehicle-mile in year-2002 dollars. See Ian W.H. Parry and Kenneth A. Small, “Does Britain or the U.S. Have 

the Right Gasoline Tax?” Discussion Paper 02-12, Resources for the Future, 19 and Table 1 (March 2002). 

Available at http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed May 30, 2012).
Economic and Other Assumptions Used in the Agencies' Analysis 

4-28 

4.2.8 Petroleum and energy security impacts 

The final standards for MYs 2017-2025 will reduce fuel consumption and GHG 

emissions in light-duty vehicles, which will result in improved fuel efficiency and, in turn, 

help to reduce U.S. petroleum imports. A reduction of U.S. petroleum imports reduces both 

financial and strategic risks caused by potential sudden disruptions in the supply of imported 

petroleum to the U.S. This reduction in the expected future economic costs associated with 

these risks provides a measure of value of improved U.S. energy security resulting from lower 

petroleum imports. This section summarizes the agencies’ estimates of U.S. oil import 

reductions and energy security benefits of the program. Additional discussion of this issue 

can be found in Section III.H and Section IV.C.3 of the preamble. 

4.2.8.1 Impact on U.S. petroleum imports 

In 2011, U.S. petroleum import expenditures represented 16 percent of total U.S. 

imports of all goods and services.28,29 In 2011, the United States imported 45 percent of the 

petroleum it consumed30, while the transportation sector accounted for 70 percent of total U.S. 

petroleum consumption.31

 These figures compare to approximately 37 percent of U.S. 

petroleum supplied by imports and 55 percent of total petroleum consumed by the nation’s 

transportation sector during 1975.32 Requiring improved fuel economy and lower-GHG 

vehicle technology in the U.S. is expected to lower U.S. petroleum imports. 

Based on analysis of historical and projected future variation in U.S. petroleum 

consumption and imports, EPA and NHTSA estimate that approximately 50 percent of the 

reduction in fuel consumption resulting from adopting improved GHG emission and fuel 

efficiency standards is likely to be reflected in lower U.S. imports of refined fuel, while the 

remaining 50 percent is expected to be reflected in reduced domestic fuel refining.gg

 Of this 

latter figure, 90 percent is anticipated to reduce U.S. imports of crude petroleum for use as a 

refinery feedstock, while the remaining 10 percent is expected to reduce U.S. domestic 

production of crude petroleum.hh Thus, on balance, each gallon of fuel saved as a 

consequence of our final standards is anticipated to reduce total U.S. imports of petroleum by 

0.95 gallons.ii 

 

gg Differences in forecasted annual U.S. imports of crude petroleum and refined products among the Reference, 

High Oil Price, and Low Oil Price scenarios analyzed in EIA’s Annual Energy Outlook 2011 range from 35-74 

percent of differences in projected annual gasoline and diesel fuel consumption in the U.S. These differences 

average 53 percent over the forecast period spanned by AEO 2011. 

hh Differences in forecasted annual U.S. imports of crude petroleum among the Reference, High Oil Price, and 

Low Oil Price scenarios analyzed in EIA’s Annual Energy Outlook 2011 range from 67-104 percent of 

differences in total U.S. refining of crude petroleum, and average 90 percent over the forecast period spanned by 

AEO 2011. 

ii This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-29 

4.2.8.2 Background on U.S. energy security 

U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs or 

interruptions in energy supplies. Most discussions of U.S. energy security focus on the 

economic costs of U.S. dependence on oil imports, and particularly on U.S. reliance on oil 

imported from potentially unstable sources. In addition, oil exporters have the ability to raise 

the price of oil by exerting monopoly power through the mechanism of a cartel, the 

Organization of Petroleum Exporting Countries (OPEC). These factors contribute to the 

vulnerability of the U.S. economy to episodic oil supply shocks and price spikes. In 2010, 

total U.S. imports of crude oil, including those from OPEC nations as well as other sources, 

were $271 billion (in 2010$)33 (see Figure 4-2).

Figure 4-2 U.S. Expenditures on Crude Oil from 1970 through 2010jj

 A significant effect of the MY 2017-2025 fuel economy and GHG standards (as well 

as the MY 2012-2016 light-duty vehicle standards and the MY 2014-2018 standards for 

 

Source for historical data: EIA Annual Energy Review, various editions. For recent historical and forecasted 

data: EIA Annual Energy Outlook (AEO) 2011 Reference Case. 

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005 2010

(2009$ Billions)

Year

U.S. Expenditures on Crude Oil

Domestic

Imported
Economic and Other Assumptions Used in the Agencies' Analysis 

4-30 

medium- and heavy-duty vehicles) will be to reduce consumption of transportation fuels in 

the U.S. This will in turn reduce U.S. oil imports, which lowers both financial and strategic 

risks associated with potential disruptions in supply or sudden increases in the price of 

petroleum. For this rulemaking, an “oil import premium” approach is utilized to estimate 

energy security-related costs of importing petroleum into the U.S. Specifically, the oil import 

premium measures the expected economic value of costs that are not reflected in the market 

price of petroleum, and that are expected to change in response to an incremental change in 

the level of U.S. oil imports. 

4.2.8.3 Methodology used to estimate U.S. energy security benefits 

In order to understand the energy security implications of reducing U.S. oil imports, 

EPA has worked with Oak Ridge National Laboratory (ORNL), which has developed 

approaches for evaluating the social costs and energy security implications of oil use. The 

energy security estimates provided below are based upon a methodology developed in a peerreviewed study entitled, “The Energy Security Benefits of Reduced Oil Use, 2006-2015,” 

completed in March 2008. This study is included as part of the docket for this rulemaking.34

 

When conducting the analysis for EPA and NHTSA for purposes of analyzing our 

final standards, ORNL considered the full cost of importing petroleum into the U.S. The full 

economic cost is defined to include two components in addition to the purchase price of 

petroleum itself. These are: (1) the higher costs for oil imports resulting from the effect of 

U.S. import demand on the world oil price and on OPEC market power (i.e., the “demand” or 

“monopsony” costs); and (2) the risk of reductions in U.S. economic output and disruption of 

the U.S. economy caused by sudden disruptions in the supply of imported oil to the U.S. (i.e., 

“macroeconomic disruption and adjustment costs”). Costs associated with U.S. military 

expenditures to help secure stable oil supply from volatile regions of the world were not 

included in this analysis, because attributing costs for military operations to specific missions 

or activities is difficult and the majority of the literature indicates that it is uncertain if merely 

reducing (rather than entirely eliminating) reliance on imported oil would lead to measurable 

changes in U.S. military expenditures (as discussed further). 

For this analysis, ORNL estimated energy security premiums by incorporating the 

AEO 2012 Early Release oil price forecasts and market trends, which was the most recent 

data available at the time the analyses for the final rules were conducted. Energy security 

premiums for the years 2020, 2025, 2030, and 2035 and beyond are presented in Table 4-11, 

as well as a breakdown of the components of the energy security premiums for each of these 

years.kk AEO 2012 Early Release projects energy market trends and values out only to 2035. 

The agencies assume that energy security premium estimates post-2035 will remain constant, 

consistent with a flat extrapolation of oil prices from AEO 2012 after 2035. 

 

kk AEO 2012 Early Release forecasts energy market trends and values only to 2035. The energy security 

premium estimates post-2035 were assumed to be the 2035 estimate. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-31 

Based on the ORNL analysis, the total oil security premium initially declines slightly 

through 2020, and then gradually rises over the future as a result of changing factors such as 

the world oil price, global supply/demand balances, U.S. oil imports and consumption, and 

U.S. GDP (i.e., the size of economy at risk to oil shocks). The principal factors are steadily 

rising world oil prices and a growing economy, but other effects interact. From 2020 to 2030, 

the macroeconomic disruption and adjustment component rises by 14 percent. This is over a 

period where projected average real world oil prices rise 10 per cent and U.S. GDP, the size 

of the economy potentially at risk to oil shocks, grows 28 percent. U.S. oil import quantities 

decline through 2020, but are steady thereafter through 2035, while total domestic oil 

consumption still rises modestly (by 3 percent) despite higher prices. The value share of oil in 

GDP stays fairly high; it is still at 3.9 percent by 2030 (vs. 4.5 percent in 2020). 

The components of the energy security premiums and their values are discussed 

below, as well as how we generally applied them in our respective analyses of the final 

standards. Section III.H and Section IV.C.3 of the preamble contains a detailed discussion of 

how the monopsony and macroeconomic disruption/adjustment components were treated in

the analysis of our final standards. 

Table 4-10 Energy Security Premiums in Selected Years (2010$/Barrel)1

Monopsony Macroeconomic 

Disruption/Adjustment 

Costs

 Total 

2020

$10.02

($3.35 - $17.09)

$7.63

($3.71 - $11.00)

$17.64

($9.83 - $25.00)

2025

$9.77

($3.25 - $16.69)

$8.26

($4.03 - $11.92)

$18.03

($10.15 - $25.47)

2030

$9.28

($3.10 - $18.03)

$8.77

($4.33 - $12.60)

$18.05

($10.29 - $25.20)

2035+

$9.73

($3.24 - $16.68)

$9.46

($4.72 - $13.61)

$19.19

($10.94 - $26.78)

1The main values represent the mid-point of the ranges of the values presented in the parentheses. 

The Defour Group commented that there is no relationship between the energy 

security benefits of the U.S. and reduced oil consumption by the U.S., since the world 

economies are all tied together, thus calling into question estimates of the energy security 

benefits of these rules. Moreover, the Defour Group believes there is too much uncertainty in 

generating energy security premiums, and asserted that the energy security premiums are not 

a credible approach to providing estimates of energy security benefits of the rules. 

The EPA sponsored an extensive peer review of the methodology on which the energy 

security benefits for these rules is based.35 The methodology of estimating the monopsony 

and macroeconomic effects for estimating the energy security benefits of particular actions, 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-32 

policies, and rules has been well documented and is well accepted by the energy security 

community.36 Thus, the agencies continue to use the current methodology for estimating the 

monopsony and macroeconomic effects for estimating the energy security benefits of our 

rules. 

4.2.8.4 Monopsony Effect 

The first component of the full economic costs of importing petroleum into the U.S. 

follows from the effect of U.S. import demand on the world oil price over the long-run. 

Because the U.S. purchases a sufficiently large percentage of foreign oil supplies, it exercises 

“monopsony power” in the global petroleum market. This means that increases in U.S. 

petroleum demand can cause the world price of crude oil to rise, and conversely, that reduced 

U.S. petroleum demand can reduce the world price of crude oil. 

Thus, one benefit to the U.S. of reducing U.S. oil purchases, due both to reductions in 

overall energy consumption in transportation and substitution of transportation fuels derived 

from non-petroleum sources is the potential decrease in the total dollar value of U.S. crude oil 

purchases. Because lower U.S. oil purchases reduce the price paid for each barrel, the decline 

in the total cost of U.S. petroleum purchases exceeds the savings that would result if the 

global price for oil remained unchanged. The amount by which it does so – which reflects the 

effect of U.S. monopsony power over the world oil price – represents the “monopsony” effect 

of reduced U.S. petroleum consumption. 

This “monopsony” effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for 

oil imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes 

the world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 

million (9 million barrels times $49 per barrel). While the world oil price declines by only $1, 

the resulting decrease in oil purchases equals $59 million per day ($500 million minus $441 

million). This is equivalent to an incremental savings of $59 for each barrel by which U.S. oil 

imports decline ($59 million per day divided by 1 million barrels per day), or $10 more than 

the newly-decreased world price of $49 per barrel. 

This additional $10 per barrel reduction in the “monopsony premium” represents the 

incremental external benefits to the U.S. associated with the reduction in import payments 

beyond the savings that would occur if prices remained unchanged. Of course, this additional 

benefit arises only to the extent that reduction in U.S. oil imports actually affects the world oil 

price. ORNL estimates this component of the energy security benefit in 2025 to be $9.77 

/barrel by which U.S. petroleum imports are reduced, with a range of $3.25 - $16.69/barrel.ll

Notwithstanding the discussion above, the agencies do not, in fact, include this component of 

the energy security premium as part of the benefit estimates of our final rules, since it is a 

 

ll "Estimating the U.S. Oil Security Premium for the Proposed 2017-2025 Light -Duty Vehicle GHG/Fuel 

Economy Rule", Paul N. Leiby, Oak Ridge National Laboratory (ORNL), 2012 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-33 

transfer between the U.S. and oil exporting countries, whose potential climate change 

damages are accounted for in the agencies’ estimate of the social cost of carbon, as explained 

further in section 4.2.8.7 below and in the preamble Sections III.H.8 and IV.C.3. 

4.2.8.5 Macroeconomic Disruption and Adjustment Effect 

The second component of the oil import premium, the “macroeconomic disruption and 

adjustment cost premium”, arises from the effect of U.S. oil imports on the expected cost of 

disruptions in oil supply and resulting increases in oil prices. A sudden increase in oil prices 

triggered by a disruption in world oil supplies has two main effects: (1) it increases the costs 

of oil imports in the short run, further expanding the transfer of U.S. wealth to foreign 

producers, and (2) it can lead to macroeconomic contraction, dislocation and losses in Gross 

Domestic Product (GDP). ORNL estimates the composite estimate of these two factors that 

comprise the macroeconomic disruption/adjustment costs premium to be $8.26 /barrel in 

2025, with a range of $4.03–11.92/barrel of imported oil reduced. 37 This component of the 

energy security premium is included in the agencies’ estimate of the benefits of the final 

standards. See more discussion of how the agencies account for the energy security benefits of 

the rules in Section III.H.8, and Section IV.C.3. 

During oil price shocks, the higher price of imported oil causes increased payments for 

imports from the U.S to oil exporters. This increased claim on U.S. economic output is a loss 

to the U.S. that is separate from and additional to any reduction in economic output due to the 

shock. The increased oil payments during shocks are counted as a loss to the degree that the 

expected price increase is not anticipated and internalized by oil consumers. 

Secondly, macroeconomic losses during price shocks reflect both losses in aggregate 

economic output and “allocative” losses. The former are reductions in the level of output that 

the U.S. economy can produce by fully utilizing its available resources, while the latter stem 

from temporary dislocation and underutilization of available resources due to the shock, such 

as labor unemployment and idle plant capacity. The aggregate output effect, a reduction in 

“potential” economic output, will persist as long as the price for oil remains elevated. Thus 

its magnitude depends on the extent and duration of any disruption in the world supply of oil, 

since these factors determine the extent of the resulting increase in prices for petroleum 

products, as well as whether and how rapidly these prices return to their pre-disruption levels. 

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs 

associated with dislocations in energy markets. Because supply disruptions and resulting 

price increases occur suddenly, empirical evidence shows they also impose additional costs on 

businesses and households for adjusting their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Dislocation effects 

include the unemployment of workers and other resources during the time period required for 

their inter-industry or interregional reallocation, as well as pauses in capital investment due to 

uncertainty. These adjustments temporarily reduce the level of economic output that can be 

achieved even below the “potential” output level that would ultimately be reached once the 

economy’s adaptation to higher petroleum prices was complete. The additional costs imposed 

on businesses and households for making these adjustments reflect their limited ability to 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-34 

adjust prices, output levels, and their use of energy, labor and other inputs quickly and 

smoothly in response to rapid changes in prices for petroleum products. 

Since future disruptions in foreign oil supplies are an uncertain prospect, each of the 

disruption cost components must be weighted by the probability that the supply of petroleum 

to the U.S. will actually be disrupted. Thus, the “expected value” of these costs – the product 

of the probability that a supply disruption will occur and the sum of costs from reduced 

economic output and the economy’s abrupt adjustment to sharply higher petroleum prices – is 

the relevant measure of their magnitude. Further, when assessing the energy security value of 

a policy to reduce oil use, only the change in these expected costs from potential disruptions 

that results from the policy is relevant. The expected costs of disruption may change from 

lowering the normal (i.e., pre-disruption) level of domestic petroleum use and imports, from 

any induced alteration in the likelihood or size of disruption, or from altering the short-run 

flexibility in substituting other energy sources or inputs for petroleum use. 

In summary, the steps needed to calculate the disruption or security premium are: (1) 

determine the likelihood of an oil supply disruption in the future; (2) assess the likely impacts 

of a potential oil supply disruption on the world oil price; (3) assess the impact of the oil price 

shock on the U.S. economy (in terms of import costs and macroeconomic losses); and (4) 

determine how these costs are likely to change with the level of U.S. oil imports. The 

reduction in the expected value of costs and other macroeconomic losses that results from 

lower oil imports represents the macroeconomic and adjustment cost portion of the oil import 

premium. 

4.2.8.6 Cost of existing U.S. energy security policies 

The last often-identified component of the full economic costs of U.S. oil imports is 

the costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary 

components of this cost are (1) the expenses associated with maintaining a U.S. military 

presence – in part to help secure a stable oil supply – in potentially unstable regions of the 

world; and (2) costs for maintaining the U.S. Strategic Petroleum Reserve (SPR). The SPR is 

the largest stockpile of government-owned emergency crude oil in the world. Established in 

the aftermath of the 1973-74 oil embargo, the SPR provides the U.S. a response option should 

price increases triggered by a disruption in commercial oil supplies threaten the U.S. 

economy. It also allows the U.S. to meet part of its International Energy Agency obligation to 

maintain emergency oil stocks, and it provides a national defense fuel reserve. 

The agencies recognize that potential national and energy security risks exist due to 

the possibility of tension over oil supplies. Much of the world’s oil and gas supplies are 

located in countries facing social, economic, and demographic challenges, thus making them 

even more vulnerable to potential local instability. For example, in 2010 just over 40 percent 

of world oil supply came from OPEC nations, and the AEO 2012 projections through 2030 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-35 

show no decline in this share.mm Approximately 28 percent of global supply is from Persian 

Gulf countries alone. As another measure of concentration, of the 137 countries/principalities 

that export either crude oil or refined petroleum product, the top 12 have recently accounted 

for over 55 percent of exports.38 Eight of these countries are members of OPEC, and Russia is 

ninth.nn In a market where even a 1-2 percent supply loss raises prices noticeably, and where 

a 10 percent supply loss could lead to a significant price shock, this regional concentration is 

of concern. Historically, the countries of the Middle East have been the source of eight of the 

ten major world oil disruptions39 with the ninth originating in Venezuela, an OPEC member. 

Because of U.S. dependence on oil, the military could be called on to protect energy 

resources through such measures as securing shipping lanes from foreign oil fields to avoid 

economic losses in the U.S. As Lieutenant General (Ret.) Richard Zilmer, commander of 

U.S. coalition forces in Anbar province in Iraq in 2006-2007, testified at the Philadelphia 

public hearing in support of the proposed standards: “better gas mileage is simply a matter of 

national security.”40 Lt. Gen. (Ret.) Zilmer contributed to a report of the Center for Naval 

Analyses (CNA) that discussed the implications of oil import reductions and energy 

security.41 The report focused on changes in the American transportation sector, in terms of 

fuel efficiency, alternative fuels, and transportation habits that would be needed in order for 

the U.S. economy to have enough resilience to sustain a drastic disruption in oil supply. 

Among its findings and recommendations, the report states that “[t]he federal government fuel 

economy standards have proven to be effective at increasing efficiency and reducing the use 

of oil...These standards should be supported and strengthened as a means of making our 

nation more secure.” The report states that “[t]he benefits of efficiency are so obvious and 

sizeable that it is amazing to consider how or why our country has failed to insist on (or at 

least incentivize) it up to now. ̈ Finally, the report states “[w]hile our study focuses on 

alternative fuels, we repeatedly found the best and most strategically promising alternative to 

be efficiency.” 

Part of the goals of a U.S. military presence in the Persian Gulf is to avoid the impacts 

oil price shocks from a supply cut-off on the U.S. economy. Although CNA did not conduct 

an economy-wide analysis of an oil supply shock, it did consider the impact of such a shock 

on one industrial sector that is heavily dependent on petroleum: the trucking transportation 

industry. CNA then considered a 100% disruption in the flow of oil, lasting 30 days in the 

Strait of Hormuz. They estimated that such a disruption would have caused losses of $3.3 

billion or 2.9 percent of the U.S. trucking industry’s output in 2009. According to CNA, this 

disruption would have caused 37,500 truckers to lose their jobs. This analysis concludes with 

“[i]f the U.S. – and this industry in particular – could reduce its use of petroleum by 30 

percent, the effect of such supply disruptions would be nearly zero.” Although CNA’s report 

focused on the trucking sector, the agencies believe that these findings are relevant to this rule 

since both the heavy-duty and light-duty vehicles in the U.S. are highly dependent upon 

petroleum. 

 

mm "DOE/EIA AEO2012, Table 21. International Liquids Supply and Disposition Summary". 

nn The other three are Norway, Canada, and the EU, an exporter of product. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-36 

It is CNA’s view that there are several other strategically important reasons for 

maintaining a significant military presence in the Middle East beyond protecting oil routes. 

Therefore, CNA does not necessarily believe that reduced oil consumption would 

automatically lead to the return of troops stationed in the region. 

Moreover, the military itself is heavily dependent on oil. To maintain such military 

effectiveness and flexibility, the Department of Defense identified in the Quadrennial Defense 

Review that it is “increasing its use of renewable energy supplies and reducing energy 

demand to improve operational effectiveness, reduce greenhouse gas emissions in support of 

U.S. climate change initiatives, and protect the Department from energy price fluctuations.”42

The Department of the Navy has also stated that the Navy and Marine Corps rely far too much 

on petroleum, which “degrades the strategic position of our country and the tactical 

performance of our forces. The global supply of oil is finite, it is becoming increasingly 

difficult to find and exploit, and over time cost continues to rise.”43 In remarks given to the 

White House Energy Security Summit on April 26, 2011, Deputy Security of Defense 

William J. Lynn, III noted the direct impact of energy security on military readiness and 

flexibility. According to Deputy Security Lynn, “Today, energy technology remains a critical 

element of our military superiority. Addressing energy needs must be a fundamental part of 

our military planning.”44

 

The agencies’ analysis of energy security benefits from reducing U.S. oil imports did 

not include an estimate of potential reductions in costs for maintaining a U.S. military 

presence to help secure stable oil supply from potentially vulnerable regions of the world 

because attributing military spending to particular missions or activities is difficult. Many 

commenters in both written comments and at the agencies’ public hearings expressed their 

belief that these standards will have significant benefits for U.S. energy and national security. 

A number of commenters, including consumer advocacy and environmental organizations, 

organizations representing labor, and state and local governments, as well as energy security 

advocates and numerous private individuals, felt that the agencies should quantify, to the 

extent possible, a military component of the energy security benefits associated with this 

rulemaking. These commenters felt that although they understand that the agencies would 

have difficulties in determining a point estimate of the energy security benefits from reduced 

military costs as a result of the rule, that even ranges would be useful. The American 

Petroleum Institute commented that military expenditures will not likely change with a 

reduction in U.S. oil imports, and therefore should not be included in the assessment of this 

rulemaking. 

However, the agencies have examined methodologies for estimating the military 

component of the energy security benefits of our rules and have faced two major challenges: 

“attribution” and “incremental” analysis. The attribution analysis challenge is to determine 

which military programs and expenditures can properly be attributed to oil supply protection, 

rather than to some other objective. The incremental analysis challenge is to estimate how 

much the supply protection costs might vary if U.S. oil use is reduced or eliminated. 

However, the agencies have reviewed a number of newer studies that attempt to overcome 

these challenges.45

 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-37 

Most commonly, analysts in recent studies estimate substantial military costs 

associated with the missions of oil supply security and associated contingencies, but avoid 

estimating specific cost reductions from partial reductions in oil use. Some recent studies 

seek to update, and in some cases significantly improve, the rigor of analysis. At the low end 

of the range, the Council on Foreign Relations takes the view that substantial foreign policyrelated military missions will remain over the next 20 years, even without the oil security 

mission. Alternatively, Delucchi and Murphy46 sought to deduct from the cost of Persian 

Gulf military programs the costs associated with defending other U.S. interests (that is, 

interests other than providing more stable domestic oil supply and price to the U.S. economy). 

Excluding an estimate of cost for missions in the Persian Gulf unrelated to oil, and excluding 

costs for providing military protection for other countries’ oil import security, Delucchi and 

Murphy estimated military costs for all U.S. domestic oil interests of between $18 and $59 

billion in 2004. 

In another recent study, RAND47 considered force reductions and cost savings that 

could be achieved if oil security were no longer a consideration. Taking two approaches, and 

guided by post Cold-War force draw downs and by a top-down look at the current U.S. 

allocation of defense resources, RAND concluded that $75–$91 billion, or 12–15 per cent of 

the U.S. defense budget in 2009 could be reduced if U.S. dependence on imported oil were 

eliminated entirely. However, the study also concludes that the reduction in military costs 

from a partial reduction in the U.S. dependence on imported oil would be minimal. In another 

study, Stern48 presents an estimate of military cost for Persian Gulf force projection, 

addressing the challenge of cost allocation with an activity-based cost method. He used 

information on actual naval force deployments rather than budgets, focusing on the costs of 

aircraft carrier deployment. For the 1976–2007 time frame, Stern estimated an average 

military cost of $212 billion per year and $500 billion for 2007 alone that could be potentially 

reduced with lower oil imports. 

Although these recent studies provide significant, useful insights into the military 

components of U.S. energy security, they do not provide enough substantive analysis to 

develop a robust methodology for quantifying the military components of energy security for 

this rulemaking. Even for studies that provide insight into the attribution of specific missions 

to the objective of securing international oil production and distribution, they provide little 

guidance on the degree to which incremental reductions in the U.S. dependence on imported 

oil would reduce or eliminate those missions or programs. Thus, while the agencies plan to 

continue to review newer studies and literature to better estimate the military components of 

U.S. energy security benefits, for this rulemaking the agencies continue to exclude military 

cost components in our quantified energy security benefits. To summarize, the agencies have 

been unable to calculate the monetary benefit that the United States will receive from the 

improvements in national security expected to result from our standards. 

An additional potential component of the full economic costs of oil imports is the 

costs of building and maintaining the SPR. The SPR is clearly related to U.S. oil use and 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-38 

imports. Indeed, a stated purpose of the Energy Policy Conservation Act is “to provide for 

the creation of a Strategic Petroleum Reserve capable of reducing the impact of severe energy 

supply interruptions,” a provision enacted following the 1973-74 Arab oil embargo.oo

However, these costs have not varied historically in response to changes in U.S. oil import 

levels. Thus although the influence of the SPR on oil price increases resulting from a 

disruption of U.S oil imports is reflected in the ORNL estimate of the macroeconomic and 

adjustment cost component of the oil import premium, potential changes in the cost of 

maintaining the SPR associated with variation in U.S petroleum imports are excluded. 

4.2.8.7 Total Energy Security Benefits 

Much of the literature on the energy security for the last two decades has routinely 

combined the monopsony and the macroeconomic disruption components when calculating 

the total value of the energy security premium. However, in the context of using a global 

social cost of carbon (SCC) value, the question arises: how should the energy security 

premium be measured from a global perspective? Monopsony benefits largely represent a 

reduction in payments by consumers of petroleum products in the United States to foreign oil 

producers that result from a decrease in the world oil price as the U.S. decreases its petroleum 

consumption. 

Although a reduction in these payments clearly represents a benefit to the U.S. when 

considered from a domestic perspective, it represents an exactly offsetting loss to petroleumproducing countries. Given the purely redistributive nature of this monopsony effect when 

viewed from a global perspective, it is excluded in the energy security benefits calculations 

for this program. The Union of Concerned Scientists recommended that the monopsony 

benefits of the rules be included in the agencies’ overall estimates of the energy security 

benefits of their respective rules, since it is a benefit to the U.S. The agencies continue to 

view energy security from a global perspective, and therefore exclude monopsony benefits to 

the U.S. since these benefits are offset by losses to foreign oil producers. 

In contrast, the other portion of the energy security premium, the U.S. macroeconomic 

disruption and adjustment cost that arises from U.S. petroleum imports, does not have 

offsetting impacts outside of the U.S., and is thus included in the energy security benefits 

estimated for this program. Thus, the agencies have included only the macroeconomic 

disruption portion of the energy security benefits to estimate the monetary value of the total 

energy security benefits of this program. The agencies have calculated energy security in very 

specific terms, as the reduction of both financial and strategic risks caused by potential sudden 

disruptions in the supply of imported petroleum to the U.S. Reducing the amount of oil 

imported reduces those risks, and thus increases the nation’s energy security. 

 The energy security analysis conducted for this rulemaking estimates that the world 

price of oil will fall modestly in response to lower U.S. demand for refined fuel. One 

 

oo See 42 U.S.C section 6201 (2) and Center for Auto Safety v. NHTSA, 739 F. 2d 1322, 1324 (D.C. Cir. 1986). 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-39 

potential result of this decline in the world price of oil would be an increase in the 

consumption of petroleum products, particularly outside the U.S. In addition, other fuels 

could be displaced from the increasing use of oil worldwide. For example, if a decline in the 

world oil price causes an increase in oil use in China, India, or another country’s industrial 

sector, this increase in oil consumption may displace natural gas usage. Alternatively, the 

increased oil use could result in a decrease in coal used to produce electricity. An increase in 

the consumption of petroleum products particularly outside the U.S., could lead to a modest 

increase in emissions of greenhouse gases, criteria air pollutants, and airborne toxics from 

their refining and use. However, lower usage of, for example, displaced coal would result in a 

decrease in greenhouse gas emissions. Therefore, any assessment of the impacts on GHG 

emissions from a potential increase in world oil demand would need to take into account the 

impacts on all portions of the global energy sector. The agencies’ analyses have not 

attempted to estimate these effects. 

4.2.9 Air pollutant emissions 

Car and light truck use, fuel refining, and fuel distribution and retailing also generate 

emissions of certain criteria air pollutants, including carbon monoxide (CO), hydrocarbon 

compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides 

(NOx), fine particulate matter (PM2.5), and sulfur dioxide (SO2). Due to regulatory structure, 

emissions of most of these pollutants are associated with the number of vehicle miles driven, 

rather than with the quantity of fuel consumed. Sulfur dioxide is an exception, as described 

below. 

While reductions in fuel refining and distribution that result from lower fuel 

consumption will reduce emissions of criteria pollutants, additional vehicle use associated 

with the rebound effect and additional electricity generation to meet the demand of plug-in 

electric vehicles will increase emissions of these pollutants. Thus the net effect of stricter fuel 

efficiency and GHG standards on total emissions of each criteria pollutant depends on the 

relative magnitudes of reduced emissions during fuel refining and distribution, increases in 

emissions from additional vehicle use, and changes in electricity generation (increases due to 

EV/PHEVs and decreases due to reduced gasoline production at refineries). Because the 

relationship between the emission rates in each sector (emissions per gallon refined of fuel, 

mile driven, or kwh generated) is different for each criteria pollutant, the net effect of 

increases in fuel efficiency and GHG standards on total emissions of each pollutant differs. 

4.2.9.1 Emissions of criteria air pollutants 

EPA and NHTSA calculated the net changes in domestic emissions of each criteria 

pollutant by summing the increases in its emissions projected to result from increased vehicle 

use, changes in electricity production, and the reductions in emissions anticipated to result 

from lower domestic fuel refining and distribution. As indicated previously, the effect of 

adopting improved fuel efficiency and GHG standards on total emissions of each criteria 

pollutant depends on the relative magnitudes of the resulting reduction in emissions from fuel 

refining and distribution, and the increase in emissions from additional vehicle use. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-40 

4.2.9.2 Vehicles 

For the analysis of criteria emissions in this rule, EPA and NHTSA estimate the 

increases in emissions of each criteria air pollutant from additional vehicle use by multiplying 

the increase in total miles driven by cars and light trucks of each model year and age by their 

estimated emission rates per vehicle-mile of each pollutant. These emission rates differ 

between cars and light trucks, between gasoline and diesel vehicles, and by age. With the 

exception of SO2, the agencies calculated the increase in emissions of these criteria pollutants 

from added car and light truck use by multiplying the estimated increases in vehicle use 

during each year over their expected lifetimes by per-mile emission rates appropriate to each 

vehicle type, fuel used, model year, and age as of that future year. 

As in the MY 2012-2016 rulemaking and the proposal, the relevant emission rates 

were estimated by U.S. EPA using the most recent version of the Motor Vehicle Emission 

Simulator (MOVES2010a).49 The downstream emission rates are unchanged from the 

proposal, and no comments were received on the use of the MOVES model or its 

configuration. The MOVES model assumes that the per-mile rates at which these pollutants 

are emitted are determined by EPA regulations and the effectiveness of after-treatment of 

engine exhaust emissions, and are thus unaffected by changes in car and light truck fuel 

economy. The MOVES modeling conducted for this rule is assuming RFS2 volumes of 

renewable fuel volumes in both the “reference case” and the control case.pp

 The emission 

analysis assumed a 10% ethanol fuel supply.qqAs a consequence, the downstream impacts of 

required increases in fuel economy on emissions of these pollutants from car and light truck 

use are determined entirely by the increases in driving that result from the fuel economy 

rebound effect. 

Emission factors in the MOVES database are expressed in the form of grams per 

vehicle-hour of operation. To convert these emission factors to grams per mile, MOVES was 

run for the year 2050, and was programmed to report aggregate emissions from vehicle start, 

running, brake and tirewear and crankcase exhaust operations. EPA analysts selected the year 

2050 in order to generate emission factors that were representative of lifetime average 

emission rates for vehicles meeting the agency’s Tier 2 emission standard.rr

 Separate 

estimates were developed for each vehicle type and model year, as well as for each state and 

month, in order to reflect the effects of regional and temporal variation in temperature and 

other relevant variables on emissions. 

The MOVES emissions estimates were then summed to the model year level and 

divided by total distance traveled by vehicles of that model year in order to produce per-mile 

 

pp The agencies assume 100 percent E10 in both the reference and control cases, which is a simplifying 

assumption that is appropriate to the level of detail necessary for our rulemaking analyses. 

qq More discussion on fuel supply and this rule is in Preamble Section III.F 

rr Because all light-duty emission rates in MOVES2010a are assumed to be invariant after MY 2010, a calendaryear 2050 run produced a full set of emission rates that reflect anticipated deterioration in the effectiveness of 

vehicles’ emission control systems with increasing age and accumulated mileage for post-MY 2010 vehicles. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-41 

emission factors for each pollutant. The resulting emission rates represent average values 

across the nation, and incorporate typical variation in temperature and other operating 

conditions affecting emissions over an entire calendar year. These national average rates also 

reflect county-specific differences in fuel composition, as well as in the presence and type of 

vehicle inspection and maintenance programs.ss 

Emission rates for the criteria pollutant SO2 were calculated by using average fuel 

sulfur content estimates supplied by EPA, together with the simplifying assumption that the 

entire sulfur content of fuel is emitted in the form of SO2. These calculations assumed that 

national average gasoline and diesel sulfur levels would remain at current levels, because 

there are no current regulations which will change those levels, and we have no expectation 

that the market will cause such changes on its own.tt Therefore, unlike many other criteria 

pollutants, sulfur dioxide emissions from vehicle use decline in proportion to the decrease in 

fuel consumption. 

4.2.9.3 Fuel Production and Transport 

Emissions of criteria air pollutants also occur during each phase of fuel production and 

distribution, including crude oil extraction and transportation, fuel refining, and fuel storage 

and transportation. The reduction in emissions during each of these phases depends on the 

extent to which fuel savings result in lower imports of refined fuel, or in reduced domestic 

fuel refining. To a lesser extent, they also depend on whether reductions in domestic gasoline 

refining are reflected in reduced imports of crude oil or in reduced domestic extraction of 

petroleum. 

Both EPA’s and NHTSA’s analyses assume that reductions in imports of refined fuel 

would reduce criteria pollutant emissions during fuel storage and distribution only. 

Reductions in domestic fuel refining using imported crude oil as a feedstock are assumed to 

reduce emissions during fuel refining, storage, and distribution, because each of these 

activities would be reduced. Finally, reduced domestic fuel refining using domesticallyproduced crude oil is assumed to reduce emissions during all phases of fuel production and 

distribution.uu

 This includes reducing emissions from electric generating units that power the 

refineries. 

 

ss The national mix of fuel types includes county-level market shares of conventional and reformulated gasoline, 

as well as county-level variation in sulfur content, ethanol fractions, and other fuel properties. 

Inspection/maintenance programs at the county level account for detailed program design elements such as test 

type, inspection frequency, and program coverage by vehicle type and age. 

tt These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, 

which produces emission rates of 0.17 grams of SO2 per gallon of gasoline and 0.10 grams per gallon of diesel. 

uu In effect, this assumes that the distances crude oil travels to U.S. refineries are approximately the same 

regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline 

travels from refineries to retail stations are approximately the same as those from import terminals to gasoline 

stations. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-42 

The agencies estimated the reductions in criteria pollutant emissions from producing 

and distributing fuel that would occur under alternative fuel efficiency and GHG standards 

using emission rates obtained from Argonne National Laboratories’ Greenhouse Gases and 

Regulated Emissions in Transportation (GREET) model.50,vv The GREET model provides 

separate estimates of air pollutant emissions that occur in four phases of fuel production and 

distribution: crude oil extraction, crude oil transportation and storage, fuel refining, and fuel 

distribution and storage.ww EPA modified this version of the GREET model to change certain 

assumptions about emissions during crude petroleum extraction and transportation, as well as 

to update its emission rates to reflect adopted and pending EPA emission standards. 51 EPA 

also incorporated emission factors for the air toxics estimated in this analysis: benzene, 1,3-

butadiene, acetaldehyde, acrolein, and formaldehyde. 52

The agency converted these emission rates from the mass per fuel energy content basis 

on which GREET reports them to mass per gallon of fuel supplied using the estimates of fuel 

energy content reported by GREET. The resulting emission rates were applied to both EPA’s 

and NHTSA’s estimates of fuel consumption under alternative fuel efficiency standards to 

develop estimates of total emissions of each criteria pollutant during fuel production and 

distribution. The assumptions about the effects of changes in fuel consumption on domestic 

and imported sources of fuel supply discussed above were then employed to calculate the 

effects of reductions in fuel use from alternative fuel efficiency and GHG standards on 

changes in domestic emissions of each criteria pollutant throughout the fuel supply and 

distribution process. Additional discussion of the emission factors related to fuel production 

and transport is provided in EPA’s RIA. 

Electricity Generation 

For the NPRM, EPA and NHTSA utilized emission factors from EPA’s Integrated 

Planning Model (IPM) to assess the increased electricity used for EVs and PHEVs. As 

discussed in our respective RIAs, EPA and NHTSA have independently developed updated 

emission factors for use in estimating these emissions. Comments on estimation of these 

emissions are also discussed in sections III and IV of the preamble, and in the agencies’ RIAs. 

4.2.9.4 Estimated values of reducing PM-related emissions in the model year analysis 

The agencies’ analysis of PM2.5-related benefits over the lifetime of specific model 

years uses a “benefit-per-ton” method to estimate selected PM2.5-related health benefits. 

 

vv GREET has been updated since the last major update of the EPA impact spreadsheet, most recently with 

GREET 1 2012, released on June 28, 2012. Due to the lead time required for modeling, and the resultant timing 

constraints, these updates have not been incorporated in this analysis. The agencies will monitor relevant 

developments for future rulemakings. 

ww Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of 

volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to 

estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of 

gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted 

to mass per gallon of gasoline using the average energy content of gasoline. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-43 

These PM2.5-related benefit-per-ton estimates provide the total monetized human health 

benefits (the sum of premature mortality and premature morbidity) of reducing one ton of 

directly emitted PM2.5, or one ton of a pollutant that contributes to secondarily-formed PM2.5

(such as NOx and SOx) from a specified source. Ideally, the human health benefits would be 

estimated based on changes in ambient PM2.5 concentrations and population exposure, as 

determined by complete air quality and exposure modeling. However, conducting such 

detailed modeling for the model year analysis was not possible within the timeframe for the 

final rule. Note that EPA and NHTSA conducted full-scale photochemical air quality 

modeling for the calendar year analysis in 2030. Please refer to Chapter 6.2 of the RIA for a 

description of EPA’s air quality modeling results and to Chapter 6.3 for a description of the 

quantified and monetized PM- and ozone-related health impacts of the FRM. 

Due to analytical limitations, the estimated benefit-per-ton values do not include 

comparable benefits related to reductions in other ambient concentrations of criteria pollutants 

(such as ozone, NO2 or SO2) or toxic air pollutants, nor do they monetize all of the potential 

health and welfare effects associated with PM2.5 or the other criteria pollutants. As a result, 

monetizing PM-related health impacts alone underestimates the benefits associated with 

reductions of the suite of non-GHG pollutants that would be reduced by the final standards. 

The dollar-per-ton estimates used to monetize reductions in emissions that contribute 

to ambient concentrations of PM2.5 are provided in Table 4-11. 

Table 4-11 PM2.5-related Benefits-per-ton Values (2010$)a 

Year All Sourcesd Upstream (Non-EGU) 

Sourcesd

Mobile Sources

SO2 NOX Direct PM2.5 NOX Direct PM2.5

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Using a 3 

Percent Discount Ratec

2015 $30,000 $4,900 $230,000 $5,100 $280,000

2020 $33,000 $5,400 $250,000 $5,600 $310,000

2030 $38,000 $6,400 $290,000 $6,700 $370,000

2040 $45,000 $7,600 $340,000 $8,000 $440,000

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated 

Using a 7 Percent Discount Ratec

2015 $27,000 $4,500 $210,000 $4,600 $250,000

2020 $30,000 $4,900 $230,000 $5,100 $280,000

2030 $35,000 $5,800 $270,000 $6,100 $330,000

2040 $41,000 $6,900 $310,000 $7,300 $400,000

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 3 

Percent Discount Ratec

2015 $73,000 $12,000 $560,000 $12,000 $680,000

2020 $80,000 $13,000 $620,000 $14,000 $750,000

2030 $94,000 $16,000 $720,000 $16,000 $900,000

2040 $110,000 $19,000 $840,000 $20,000 $1,100,000

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 7 

Percent Discount Ratec

2015 $66,000 $11,000 $510,000 $11,000 $620,000

2020 $72,000 $12,000 $560,000 $12,000 $680,000

2030 $84,000 $14,000 $650,000 $15,000 $810,000

2040 $99,000 $17,000 $760,000 $18,000 $960,000
Economic and Other Assumptions Used in the Agencies' Analysis 

4-44 

a Total dollar-per-ton estimates include monetized PM2.5-related premature mortality and morbidity endpoints. 

Range of estimates are a function of the estimate of PM2.5-related premature mortality derived from either the 

ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006). 

b

 Dollar-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA extrapolated 

exponentially based on the growth between 2020 and 2030. 

c

 The dollar-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the 

valuation of premature mortality to account for a twenty-year segmented cessation lag. 

d

 Note that the dollar-per-ton value for SO2 is based on the value for Stationary (Non-EGU) sources; no SO2 

value was estimated for mobile sources. 

As Table 4-11 indicates, EPA projects that the per-ton values for reducing emissions 

of criteria pollutants from both vehicle use and stationary sources such as fuel refineries and 

storage facilities will increase over time.xx

 These projected increases reflect rising income 

levels, which are assumed to increase affected individuals’ willingness to pay for reduced 

exposure to health threats from air pollution. They also reflect future population growth and 

increased life expectancy, which expands the size of the population exposed to air pollution in 

both urban and rural areas, especially in older age groups with the highest mortality risk.53,yy 

For certain PM2.5-related pollutants (such as direct PM2.5 and NOx), EPA estimates 

different per-ton values for reducing mobile source emissions than for reductions in 

emissions of the same pollutant from stationary sources such as fuel refineries and storage 

facilities. These reflect differences in the typical geographic distributions of emissions of 

each pollutant by different sources, their contributions to ambient levels of PM2.5, and 

resulting changes in population exposure. EPA and NHTSA apply these separate values to its 

estimates of changes in emissions from vehicle use and from fuel production and distribution 

to determine the net change in total economic damages from emissions of those pollutants. 

The benefit per-ton technique has been used in previous analyses, including the 2012-

2016 Light-Duty Greenhouse Gas Rule,54 the Ozone National Ambient Air Quality Standards 

(NAAQS) RIA,55 the Portland Cement National Emissions Standards for Hazardous Air 

Pollutants (NESHAP) RIA,56 and the final NO2 NAAQS.57 Table 4-12 shows the quantified 

and monetized PM2.5-related co-benefits that are captured in these benefit-per-ton estimates, 

and also lists other effects that remain un-quantified and are thus excluded from the estimates. 

Table 4-12 Human Health and Welfare Effects of PM2.5 

Pollutant / 

Effect

Quantified and Monetized 

in Primary Estimates

Un-quantified Effects 

Changes in:

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute

Hospital admissions: respiratory and 

Subchronic bronchitis cases

Low birth weight

Pulmonary function

 

xx As we discuss in the emissions chapter of EPA’s RIA (Chapter 4), the rule would yield emission reductions 

from upstream refining and fuel distribution due to decreased petroleum consumption. 

yy For more information about EPA’s population projections, please refer to the following: 

http://www.epa.gov/air/benmap/models/BenMAPManualAppendicesAugust2010.pdf (See Appendix K) 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-45 

Pollutant / 

Effect

Quantified and Monetized 

in Primary Estimates

Un-quantified Effects 

Changes in:

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial 

infarction)

Lower and upper respiratory illness

Minor restricted-activity days

Work loss days

Asthma exacerbations (asthmatic 

population)

Infant mortality

Chronic respiratory diseases other than 

chronic bronchitis

Non-asthma respiratory emergency room 

visits

Visibility

Household soiling

Consistent with the NO2 NAAQS,zz the benefits estimates utilize concentrationresponse functions as reported in the epidemiology literature. Readers interested in reviewing 

the complete methodology for creating the benefit-per-ton estimates used in this analysis can 

consult the Technical Support Document (TSD)58 accompanying the final ozone NAAQS 

RIA. Readers can also refer to Fann et al. (2009)59 for a detailed description of the benefitper-ton methodology.aaa

As described above, national per-ton estimates were developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., NO2 emitted from 

mobile sources; direct PM emitted from stationary sources). Our estimate of total PM2.5 

benefits is therefore based on the total direct PM2.5 and PM2.5–related precursor emissions 

(NOx, SOx, and VOCs) controlled from each source and multiplied by the respective per-ton 

values of reducing emissions from that source. 

Reductions in PM-related mortality provide the majority of the monetized value in 

each benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients 

that underlie the benefits-per-ton estimates are drawn from epidemiology studies that examine 

two large population cohorts: the American Cancer Society cohort (Pope et al., 2002)60 and 

the Harvard Six Cities cohort (Laden et al., 2006).61 The concentration-response (C-R) 

function developed from the extended analysis of American Cancer Society (ACS) cohort, as 

 

zz Although we summarize the main issues in this chapter, we encourage interested readers to see benefits 

chapter of the NO2 NAAQS for a more detailed description of recent changes to the PM benefits presentation 

and preference for the no-threshold model. 

aaa The values included in this report are different from those presented in the article cited above. Benefits 

methods change to reflect new information and evaluation of the science. Since publication of the June 2009 

article, EPA has made two significant changes to its benefits methods: (1) We no longer assume that a threshold 

exists in PM-related models of health impacts, which is consistent with the findings reported in published 

research; and (2) We have revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an 

estimate of $5.5 million (year 2000$) used in the June 2009 report. Please refer to the following website for 

updates to the dollar-per-ton estimates: http://www.epa.gov/air/benmap/bpt.html 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-46 

reported in Pope et al. (2002), has previously been used by EPA to generate its primary 

benefits estimate. The extended analysis of the Harvard Six Cities cohort, as reported by 

Laden et al (2006), was published after the completion of the Staff Paper for the 2006 PM2.5 

NAAQS and has been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 cobenefits estimates in analyses completed since the PM2.5 NAAQS. 

These studies provide logical choices for co-equal anchor points when presenting PMrelated benefits because, while both studies are well designed and peer-reviewed, there are 

strengths and weaknesses inherent in each. Although EPA’s primary method of 

characterizing PM-related premature mortality is to use both studies to generate a co-equal 

range of benefits estimates, EPA has chosen to present only the benefit-per-ton value derived 

from the ACS study in its summary tables of total Model Year costs and benefits (See 

Preamble Section III.H.10 and RIA Chapter 7). This decision was made to provide the reader 

with summary tables that are easier to understand and interpret and does not convey any 

preference for one study over the other. We note that this is also the more conservative of the 

two estimates - PM-related benefits would be approximately 245 percent (or nearly two-anda-half times) larger had we used the per-ton benefit values based on the Harvard Six Cities 

study instead. 

As is the nature of benefits analyses, assumptions and methods evolve over time to 

reflect the most current interpretation of the scientific and economic literature. For a period 

of time (2004-2008), EPA’s Office of Air and Radiation (OAR) valued mortality risk 

reductions using a value of statistical life (VSL) estimate derived from a limited analysis of 

some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 

million (2000$) consistent with two meta-analyses of the wage-risk literature. 

The $1 million value represented the lower end of the interquartile range from the 

Mrozek and Taylor (2002)62 meta-analysis of 33 studies. The $10 million value represented 

the upper end of the interquartile range from the Viscusi and Aldy (2003)63 meta-analysis of 

43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean 

VSL of $5.4 million estimated in the Kochi et al. (2006)64 meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rulemakings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

Until updated guidance is available, EPA determined that a single, peer-reviewed 

estimate applied consistently best reflects the Science Advisory Board Environmental 

Economics Advisory Committee (SAB-EEAC) advice it has received. Therefore, EPA has 

decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2000)65 while they continue efforts to update their 

guidance on this issue.bbb This approach calculates a mean value across VSL estimates 

 

bbb In the update of the Economic Guidelines (U.S. EPA, 2011), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-47 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton 

estimates used in this analysis are based on this revised VSL.ccc

The benefit-per-ton estimates are subject to a number of assumptions and 

uncertainties. 

They do not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an 

overestimate or underestimate of the actual benefits of controlling fine particulates in 

specific locations. Please refer to Chapter 6 of EPA’s RIA for the description of the 

agency’s quantification and monetization of PM- and ozone-related health impacts for 

the final standards. 

This analysis assumes that all fine particles, regardless of their chemical composition, 

are equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from stationary sources 

may differ significantly from direct PM2.5 released from engines and other industrial 

sources. At the present time, however, no clear scientific grounds exist for supporting 

differential effects estimates by particle type. 

This analysis assumes that the health impact function for fine particles is linear within 

the range of ambient concentrations under consideration. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied initial concentrations 

of PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard, down to the lowest modeled concentrations. 

There are several health benefits categories that EPA and NHTSA were unable to 

quantify due to limitations associated with using benefits-per-ton estimates, several of 

which could be substantial. Because NOX and VOC emissions are also precursors to 

ozone, changes in NOX and VOC would also impact ozone formation and the health 

effects associated with ozone exposure. Benefits-per-ton estimates for ozone do not 

exist due to issues associated with the complexity of the atmospheric air chemistry and 

nonlinearities associated with ozone formation. The PM-related benefits-per-ton 

estimates also do not include any human welfare or ecological benefits. Please refer to 

Chapter 6.3 of EPA’s RIA for a description of the unquantified co-pollutant benefits 

associated with this rulemaking. 

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as the localized impacts 

associated with the rulemaking may vary significantly. Additionally, the atmospheric 

 

near future. The update of the Economic Guidelines is available on the Internet at 

http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf. 

ccc This value differs from the Department of Transportation’s most recent estimate of the value of preventing 

transportation-related fatalities, which is $6.1 million when expressed in today’s (2011) dollars. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-48 

chemistry related to ambient concentrations of PM2.5, ozone and air toxics is very complex. 

Full-scale photochemical modeling is therefore necessary to provide the needed spatial and 

temporal detail to more completely and accurately estimate the changes in ambient levels of 

these pollutants and their associated health and welfare impacts. For this final rule, EPA and 

NHTSA conducted national-scale air quality modeling analyses for 2030 to analyze the 

impacts of the standards on PM2.5, ozone, and selected air toxics. 

4.2.10 Reductions in emissions of greenhouse gases 

Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the 

process of producing and distributing transportation fuels, as well as from fuel combustion 

itself. By increasing fuel efficiency and thus reducing the volume of fuel consumed by 

passenger cars and light trucks, the standards will reduce GHG emissions generated by fuel 

use, as well as throughout the fuel supply cycle. Lowering these emissions is likely to slow 

the projected pace and reduce the ultimate extent of future changes in the global climate, thus 

reducing future economic damages that changes in the global climate are otherwise expected 

to cause. Further, by reducing the probability that climate changes with potentially 

catastrophic economic or environmental impacts will occur, lowering GHG emissions may 

also result in economic benefits that exceed the resulting reduction in the expected future 

economic costs caused by gradual changes in the earth’s climatic systems. Quantifying and 

monetizing benefits from reducing GHG emissions is thus an important step in estimating the 

total economic benefits likely to result from establishing improved fuel efficiency and GHG 

standards. 

4.2.10.1 Estimating reductions in GHG emissions 

NHTSA estimates emissions of carbon dioxide (CO2) from passenger car and light 

truck use by multiplying the number of gallons of each type of fuel (gasoline and diesel) they 

are projected to consume with each alternative CAFE standard in effect by the quantity or 

mass of CO2 emissions released per gallon of fuel consumed. EPA directly calculates CO2

emissions from the projected CO2 emissions of each vehicle under the CO2 standards. This 

calculation assumes that the entire carbon content of each fuel is ultimately converted to CO2

emissions during the combustion process. The weighted average CO2 content of certification 

gasoline is estimated to be 8,887 grams per gallon, while that of diesel fuel is estimated to be 

approximately 10,200 grams per gallon. For details, please see EPA’s and NHTSA’s 

respective RIAs. These same methods were used in the proposal and no comments were 

received. 

Although carbon dioxide emissions account for nearly 95 percent of total GHG 

emissions that result from fuel combustion during vehicle use, emissions of other GHGs are 

potentially significant as well because of their higher “potency” as GHGs than that of CO2

itself. EPA and NHTSA estimated the increases in emissions of methane (CH4) and nitrous 

oxide (N2O) from additional vehicle use by multiplying the increase in total miles driven by 

cars and light trucks of each model year and age by emission rates per vehicle-mile for these 

GHGs. These emission rates, which differ between cars and light trucks as well as between 

gasoline and diesel vehicles, were estimated by EPA using MOVES 2010a. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-49 

The MOVES model assumes that the per-mile rates at which cars and light trucks emit 

these non-CO2 GHGs are determined by the efficiency of fuel combustion during engine 

operation and chemical reactions that occur during catalytic after-treatment of engine exhaust, 

and are thus independent of vehicles’ fuel consumption rates. Thus MOVES emission factors 

for these GHGs, which are expressed per mile of vehicle travel, are assumed to be unaffected 

by changes in fuel economy. 

Much like criteria pollutants, emissions of GHGs occur during each phase of fuel 

production and distribution, including crude oil extraction and transportation, fuel refining, 

and fuel storage and transportation. Emissions of GHGs also occur in generating electricity, 

which the agencies’ analysis anticipates will account for an increased but likely still small 

share of energy consumption in the model years subject to the standards. The agencies’ 

analyses assume that reductions in fuel consumption would reduce global GHG emissions 

during all four phases of fuel production and distribution.ddd

 Unlike criteria pollutants, the 

agencies report both domestic and international reductions in GHG emissions. EPA derived 

GHG emission rates corresponding to producing and distributing fuel from Argonne National 

Laboratories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) 

model.66eee

For the NPRM, EPA and NHTSA utilized emission factors from EPA’s Integrated 

Planning Model (IPM) to assess the increased electricity used for EVs and PHEVs. As 

discussed in our respective RIAs, EPA and NHTSA have independently developed updated 

emission factors for use in estimating these emissions. Comments on estimation of these 

emissions are also discussed in sections III and IV of the preamble, and in the agencies’ RIAs. 

Increases in emissions of non-CO2 GHGs are converted to equivalent increases in CO2

emissions using estimates of the Global Warming Potential (GWP) of methane (CH4), nitrous 

oxide (N2O), and hydrofluorocarbons (HFC-134a).fff These GWPs are one way of accounting 

 

ddd The four stages are crude oil extraction, crude oil transportation and storage, fuel refining, and fuel 

distribution and storage 

eee This version of the model was modified, and is discussed in section 4.2.9.1

fff As in the MY 2012-2016 LD rules and in the MY 2014-2018 MD and HD rules, the global warming potentials 

(GWP) used in this rulemaking are consistent with the 100-year time frame values in the 2007 Intergovernmental 

Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At this time, the 100-year GWP values 

from the 1995 IPCC Second Assessment Report (SAR) are used in the official U.S. GHG inventory submission 

to the United Nations Framework Convention on Climate Change (UNFCCC) per the reporting requirements 

under that international convention. The UNFCCC recently agreed on revisions to the national GHG inventory 

reporting requirements, and will begin using the 100-year GWP values from AR4 for inventory submissions in 

the future (United Nations Framework Convention on Climate Change, “Decisions adopted by the Conference of 

the Parties: 15/CP.17 ‘Revision of the UNFCCC reporting guidelines on annual inventories for Parties included 

in Annex I to the Convention’,” FCCC/CP/2011/9/Add.2, Durban, South Africa, December 2011). According to 

the AR4, N2O has a 100-year GWP of 298, CH4 has a 100-year GWP of 25, and HFC-134a has a 100-year GWP 

of 1430. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-50 

for the higher radiative forcing capacity and differing lifetimes of methane and nitrous oxide 

when they are released into the earth’s atmosphere, measured relative to that of CO2 itself. 

Because these gases differ in atmospheric lifetimes, their relative damages are not constant 

over time. Impacts other than temperature change also vary across gases in ways that are not 

captured by GWP. For instance, CO2 emissions, unlike methane and other greenhouse gases, 

contribute to ocean acidification. Methane contributes to health and ecosystem effects arising 

from increases in tropospheric ozone, while damages from methane emissions are not offset 

by the positive effect of CO2 fertilization. Noting these caveats, the CO2 equivalents of 

increases in emissions of these gases are then added to the increases in emissions of CO2 itself 

to summarize the effect of the total increase in CO2-equivalent GHG emissions from vehicle 

use. 

4.2.10.2 Economic benefits from reducing GHG emissions 

NHTSA and EPA have taken the economic benefits of reducing CO2 emissions (or 

avoiding damages from increased emissions) into account in developing the final GHG and 

CAFE standards and in assessing the economic benefits of the final standards. Specifically, 

NHTSA and EPA have assigned dollar values to reductions in carbon dioxide (CO2) 

emissions using estimates of the global “social cost of carbon” (SCC). The SCC is an 

estimate of the monetized damages associated with an incremental increase in carbon 

emissions in a given year. It is intended to include (but is not limited to) changes in net 

agricultural productivity, human health, property damages from increased flood risk, and the 

value of ecosystem services due to climate change. The SCC is expressed in constant dollars 

per additional metric ton of CO2 emissions occurring during a specific year, and is higher for 

more distant future years because the damages caused by an additional ton of emissions 

increase with larger concentrations of CO2 in the earth’s atmosphere. 

The estimates used in this analysis were developed through an interagency process 

that included EPA, DOT/NHTSA, and other executive branch entities, and concluded in 

February 2010. The interagency group focused on global SCC values because emissions of 

CO2 involve a global externality: greenhouse gases contribute to damages around the world 

wherever they are emitted. Consequently, to address the global nature of the climate change 

problem, the SCC must incorporate the full (global) damages caused by GHG emissions. 

Furthermore, climate change occurs over very long time horizons and represents a problem 

that the United States cannot solve independently. The February 2010 SCC Technical 

Support Document (SCC TSD) provides a complete discussion of the SCC estimates and the 

methods used to develop them.67

 

We first used these SCC estimates in the benefits analysis for the final joint EPA/DOT 

Rulemaking to establish 2012-2016 Light-Duty Vehicle Greenhouse Gas Emission Standards 

and Corporate Average Fuel Economy Standards; see the rule’s preamble for discussion about 

application of the SCC (75 FR 25324; May 7, 2010). We have continued to use these 

 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-51 

estimates in other rulemaking analyses, including the Greenhouse Gas Emission Standards 

and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles (76 FR 

57106; 9/15/11). Finally, see also preamble Section III.H.6, Section IV.C.3.l, EPA RIA 

Chapter 7.1, and NHTSA RIA VIII.C for discussion about the application of SCC estimates to 

this final rule. 

The SCC estimates corresponding to assumed values of the discount rate are shown 

below in Table 4-13. 

Table 4-13 Social Cost of of CO2, 2017 – 2050a

 (in 2010 dollars) 

Year

Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3%

95th percentile

2017 $6 $26 $41 $79

2020 $7 $27 $43 $84

2025 $9 $31 $48 $94

2030 $10 $34 $52 $104

2035 $12 $37 $56 $114

2040 $13 $41 $61 $124

2045 $15 $44 $64 $133

2050 $16 $47 $68 $142

a

 The SCC values apply to emissions occurring during each year 

shown (in 2010 dollars), and represent the present value of future damages as 

of the year shown. 

As Table 4-13 shows, the SCC estimates selected by the interagency group for use in 

regulatory analyses range from roughly $6 to about $79 (in 2010 dollars) for emissions 

occurring in the year 2017. The first three estimates are based on the average SCC estimated 

using different models and reflect discount rates of 5, 3, and 2.5 percent, respectively. The 

fourth value is included to represent the possibility of higher-than-expected impacts from 

accumulation of GHGs in the earth’s atmosphere, and the consequently larger economic 

damages. For this purpose, the interagency group elected to use the SCC value for the 95th

percentile at a 3 percent discount rate. 

The value that the interagency group centered its attention on is the average SCC 

estimate at a 3 percent discount rate, or about $26 per metric ton in 2017. To capture the 

uncertainties involved in regulatory impact analysis, however, the group emphasized the 

importance of considering the full range of estimated SCC values. As the table shows, the 

SCC estimates rise over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change; for example, the average SCC at 3 percent increases from about $26

per ton of CO2 in 2017 to approximately $34 per ton of CO2 by 2030. 

Reductions in CO2 emissions that are projected to result from lower fuel consumption, 

refining, and distribution during each future year are multiplied by the appropriate SCC 

estimates for that year, to determine the range of total economic benefits from reduced 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-52 

emissions during that year. For internal consistency, these annual benefits are discounted 

back to net present value terms using a discount rate that is consistent with that used to 

develop each SCC estimate. 

This final rulemaking also responds to comments regarding the valuation of non-CO2

GHGs and analyzes changes in non-CO2 GHGs. The 2010 interagency group, however, did 

not directly estimate the social cost of non-CO2 GHGs. One way to approximate the value of 

marginal non-CO2 GHG emission reductions in the absence of direct model estimates is to 

convert the reductions to CO2-equivalents which may then be valued using the SCC. 

Conversion to CO2-e is typically done using the global warming potential (GWP) for the nonCO2 gas.ggg

 We refer to this as the “GWP approach.” 

One potential problem with using temporally aggregated statistics such as GWP is that 

the additional radiative forcing from the GHG perturbation is not constant over time and any 

differences in temporal dynamics between gases will be lost. This is a potentially 

confounding issue given that the social cost of GHGs is based on a discounted stream of 

damages that are non-linear in temperature. For example, methane has an expected adjusted 

atmospheric lifetime of about 12 years and associated GWP of 25 (IPCC Fourth Assessment 

Report (AR4) 100-year GWP estimate). Gases with a relatively shorter lifetime, such as 

methane, have impacts that occur primarily in the near term and thus are not discounted as 

heavily as those caused by longer-lived gases such as CO2, while the GWP treats additional 

forcing the same independent of when it occurs in time. Furthermore, the baseline 

temperature change is lower in the near term and therefore the additional warming from 

relatively short lived gases will have a lower marginal impact relative to longer lived gases 

that have an impact further out in the future when baseline warming is higher. In addition, 

impacts other than temperature change also vary across gases in ways that are not captured by 

GWP. For instance, CO2 emissions, unlike methane will result in CO2 passive fertilization to 

plants. 

In short, the GWP-weighted approach will produce social cost estimates that are less 

accurate than the directly modeled estimates. A limited number of studies in the published 

literature explore these differences. A recent working paper (Marten and Newbold, 2011), 

found that the GWP-weighted benefit estimates for CH4 and N2O are likely to be lower than 

those that would be derived using a directly modeled social cost of the non-CO2 GHGs for a 

variety of reasons.68 hhh The GWP reflects only the integrated radiative forcing of a gas over 

100 years. In contrast, the directly modeled social cost differs from the GWP because the 

differences in timing of the warming between gases are explicitly modeled, the non-linear 

 

ggg The GWP is an aggregate measure that approximates the additional energy trapped in the atmosphere over a 

given timeframe from a perturbation of a non-CO2 gas relative to CO2. 

hhh As discussed in Marten and Newbold, the discount rate influences the relative social cost of a gas, i.e., the 

ratio of the social cost of the gas and the social cost of CO2. Methane is a short-lived gas and therefore at higher 

discount rates, the relative social cost is higher than at low discount rates. Depending on the discount rate, the 

relative social cost of methane ranged from 22 to 41 in 2015, compared to 25 for the AR4 GWP. The relative 

social cost of N2O was calculated to be at least 372, much higher than the AR4 value of 298. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-53 

effects of temperature change on economic damages are included, and rather than treating all 

impacts over a hundred years equally, the modeled social cost applies a discount rate but 

calculates impacts through the year 2300. 

The agencies recognize the importance of considering the economic impacts from 

changes to non-CO2 GHGs under this final rule. Therefore, in the absence of direct model 

estimates from the interagency analysis, EPA and NHTSA have used the GWP approach to 

estimate the dollar value of this rule’s non-CO2 GHG benefits in a sensitivity analysis; these 

estimates are presented for illustrative purposes and therefore not included in the total benefits 

estimate for the rulemaking. NHTSA and EPA converted CH4 and N2O emissions to CO2

equivalents using the GWP of each gas, then multiplied these CO2-equivalent emission by the 

interagency social cost of CO2 estimates. EPA also converted HFC-134a emissions to CO2

equivalents and applied the social cost of carbon.iii Please see NHTSA’s preamble IV.G.4 and 

RIA Chapter X and EPA’s preamble Section III.H.6 and RIA Chapter 7 for more details about 

the agencies’ respective sensitivity analyses and results. 

4.2.11 Benefits due to reduced refueling time 

Direct estimates of the value of extended vehicle range are not available in the 

literature, so the agencies instead calculate the reduction in the required annual number of 

refueling cycles due to improved fuel economy, and assess the economic value of the 

resulting benefits. Chief among these benefits is the time that owners save by spending less 

time both in search of fueling stations and in the act of pumping and paying for fuel. 

NHTSA conducted an analysis to estimate the benefits associated with reduced 

refueling, which both agencies are using in their respective analyses of overall programmatic 

costs and benefits, but which the agencies are applying slightly differently. See chapter VIII 

of NHTSA’s RIA and chapter 7 of EPA’s RIA for more details. 

 

iii As in the MY 2012-2016 LD rules and MY 2014-2018 MD and HD rules, the global warming potentials 

(GWP) used in this rulemaking are consistent with the 100-year time frame values in the 2007 Intergovernmental 

Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At this time, the 100-year GWP values 

from the 1995 IPCC Second Assessment Report (SAR) are used in the official U.S. GHG inventory submission 

to the United Nations Framework Convention on Climate Change (UNFCCC) per the reporting requirements 

under that international convention. The UNFCCC recently agreed on revisions to the national GHG inventory 

reporting requirements, and will begin using the 100-year GWP values from AR4 for inventory submissions in 

the future (United Nations Framework Convention on Climate Change, “Decisions adopted by the Conference of 

the Parties: 15/CP.17 ‘Revision of the UNFCCC reporting guidelines on annual inventories for Parties included 

in Annex I to the Convention’,” FCCC/CP/2011/9/Add.2, Durban, South Africa, December 2011). According to 

the AR4, N2O has a 100-year GWP of 298, CH4 has a 100-year GWP of 25, and HFC-134a has a 100-year GWP 

of 1430. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-54 

4.2.12 Discounting future benefits and costs 

Discounting future fuel savings and other benefits is intended to account for the 

reduction in their value to society when they are deferred until some future date, rather than 

received immediately. The discount rate expresses the percent decline in the value of these 

benefits – as viewed from the current perspective – for each year they are deferred into the 

future. In evaluating the benefits from alternative increases in fuel economy and GHG 

standards for MY 2017-2025 passenger cars and light trucks, EPA and NHTSA consider 

discount rates of both 3 and 7 percent per year. 

Three percent may be the appropriate rate for discounting future benefits from 

increased fuel economy and GHG standards because most or all of vehicle manufacturers’ 

costs for complying with improved fuel economy and GHG standards are likely to be 

reflected in higher sales prices for their new vehicle models. By increasing sales prices for 

new cars and light trucks, GHG and CAFE regulations will thus primarily affect vehicle 

purchases and other private consumption decisions. 

Both economic theory and OMB guidance on discounting indicate that the future 

benefits and costs of regulations that mainly affect private consumption should be discounted 

at the consumption rate of time preference.69 OMB guidance indicates that savers appear to 

discount future consumption at an average real (that is, adjusted to remove the effect of 

inflation) rate of about 3 percent when they face little risk about its likely level, which makes 

it a reasonable estimate of the consumption rate of time preference.70 Because there is some 

uncertainty about the extent to which vehicle manufacturers will be able to recover their costs 

for complying with improved fuel economy and GHG standards by increasing vehicle sales 

prices, however, the use of a higher percent discount rate may also be appropriate. OMB 

guidance indicates that the real economy-wide opportunity cost of capital is the appropriate 

discount rate to apply to future benefits and costs when the primary effect of a regulation is 

“...to displace or alter the use of capital in the private sector,” and estimates that this rate 

currently averages about 7 percent.71

 Thus the agencies have employed both 3 and 7 percent 

rates to discount projected future benefits and costs resulting from improved fuel economy 

and GHG standards for MY 2017-2025 passenger cars and light trucks. 

One important exception to these values are the rates used to discount benefits from 

reducing CO2 emissions from the years in which reduced emissions occur, which span the 

lifetimes of model year 2017-2025 cars and light trucks, to their present values. In order to 

ensure consistency in the derivation and use of the SCC estimates of the unit values of 

reducing CO2 emissions, the total benefits from reducing those emissions during each future 

year are discounted using the same rates that were used to derive the alternative values of 

reducing each ton of CO2 emissions (2.5, 3.0, and 5.0 percent). 

4.2.13 Additional Costs of Vehicle Ownership 

Sales Taxes:

Consumers may consider the sales taxes they have to pay at the time of purchasing the 

vehicle. As these costs are transfer payments, they are not included in the societal cost of the 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-55 

program, but they are included as one of the increased costs to the consumer for these 

standards, when we calculate costs that the consumer pays out for vehicle ownership. The 

agencies took the most recent auto sales taxes by statejjj and weighted them by population by 

state to determine a national weighted-average sales tax of 5.46 percent. The agencies sought 

to weight sales taxes by new vehicle sales by state; however, such data were unavailable. It is 

recognized that for this purpose, new vehicle sales by state is a superior weighting mechanism 

to Census population; in effort to approximate new vehicle sales by state, a study of the 

change in new vehicle registrations (using R.L. Polk data) by state across recent years was 

conducted, resulting in a corresponding set of weights. Use of the weights derived from the 

study of vehicle registration data resulted in a national weighted-average sales tax rate almost 

identical to that resulting from the use of Census population estimates as weights, just slightly 

above 5.5 percent. The agencies opted to utilize Census population rather than the 

registration-based proxy of new vehicle sales as the basis for computing this weighted 

average, as the end results were negligibly different and the analytical approach involving 

new vehicle registrations had not been as thoroughly reviewed. 

Financing Costs:

The agencies considered that 70 percent of new vehicle purchasers take out loans to 

finance their purchases.kkk As these costs are transfer payments, they are not included in the 

societal cost of the program, but they are included as one of the increased costs to the 

consumer for these standards. Using proprietary forecasts available from Global Insight, 

estimates of 48-monthlll bank and auto finance company loan rates for years 2017 through 

2025 were developed, which – when deflated by Global Insight’s corresponding forecasts of 

the CPI – range from 3.73% to 5.38%, averaging 5.16 percent over the nine years.mmm In the 

construction of this estimate, it was assumed that there will be an equal distribution of bank 

and auto finance company loans – an assumption necessitated by the lack of data on the 

distribution of the volume of loans between the differing types of creditors. The agencies 

opted to adjust future loan rates using the CPI rather than the GDP deflator, as this analysis is 

 

jjj See http://www.factorywarrantylist.com/car-tax-by-state.html (last accessed April 5, 2012). Note that county, 

city, and other municipality-specific taxes were excluded from the weighted averages, as the variation in locality 

taxes within states, lack of accessible documentation of locality rates, and lack of availability of weights to apply 

to locality taxes complicate the ability to reliably analyze the subject at this level of detail. Localities with 

relatively high automobile sales taxes may have relatively fewer auto dealerships, as consumers would endeavor 

to purchase vehicles in areas with lower locality taxes, therefore reducing the impact of the exclusion of 

municipality-specific taxes from this analysis. 

kkk Bird, Colin. “Should I Pay Cash, Lease or Finance My New Car?”, 

http://www.cars.com/go/advice/Story.jsp?section=fin&story=should-i-pay-cash&subject=loan-quickstart&referer=advice&aff=sacbee , July 12, 2011, citing CNW Marketing Research. Accessed 9/27/11. 

lll No projections were available for rates of loan terms of 60 months. The agencies compared the historical 

difference of 48-month and 60-month loan rates and determined the 48-month rate to be a suitable proxy for the 

60-month rate. 

mmm Global Insight data are available on a fee basis at http://www.ihs.com/products/global-insight/countryanalysis/us-economic-forecasts.aspx. Analysis of future auto loan rates is based on Global Insight data available 

as of March, 2012. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-56 

intended to facilitate further analysis from the perspective of the consumer, for which the CPI 

is the preferred deflation factor. 

Insurance Costs:

The agencies considered the rule’s impact to consumers’ auto insurance expenses over 

vehicle lifetimes. More expensive vehicles will require more expensive collision and 

comprehensive (e.g., theft) car insurance. The scope of this analysis is to estimate the 

increased cost to the consumer for these standards, not the increase in societal costs due to 

collision and property damage. The increase in insurance costs was estimated from the 

average value of collision plus comprehensive insurance as a proportion of average new 

vehicle price. Collision plus comprehensive insurance represent the portion of insurance costs 

that depend on vehicle value. A recent study by Quality Planningnnn provides the average 

value of collision plus comprehensive insurance for new vehicles, in 2010$, is $521 ($396 of 

which is collision and $125 of which is comprehensive). The average consumer expenditure 

for a new passenger car in 2011, according to the Bureau of Economic Analysis was $24,572 

and the average price of a new light truck was $31,721 in $2010.

ooo Using sales volumes 

from the Bureau, we determined an average passenger car and an average light truck price 

was $27,953 in $2010 dollars.ppp

Dividing the cost to insure a new vehicle by the average price of a new vehicle gives 

the proportion of comprehensive plus collision insurance as 1.86% of the price of a vehicle. 

As vehicles’ values decline with vehicle age, comprehensive and collision insurance 

premiums likewise decline. Data on the change in insurance premiums as a function of 

vehicle age are scarce; however, the agencies utilized data from the aforementioned Quality 

Planning study that cite the cost to insure the average vehicle on the road today (average age 

10.8 years) to enable a linear interpolation of the change in insurance premiums during the 

first 11 years of a typical vehicle’s life.qqq

 To illustrate, as a percentage of the base vehicle 

price of $27,953, the cost of collision and comprehensive insurance in each of the first five 

years of a vehicle’s life is 1.86%, 1.82%, 1.75%, 1.64%, and 1.50%, respectively, or 8.57% in 

aggregate. The agencies additionally utilized data from the same Quality Planning study that 

cite average insurance costs for vehicles greater than 10 years of age (for which the agencies 

estimated age to be 18, as this is the age at which half of vehicles in service at age 10 remain 

in service) to extrapolate insurance costs to age 18. Discounting is applied to future insurance 

 

nnn “During Recession, American Drivers Assumed More Risk to Reduce Auto Insurance Costs,” Quality 

Planning, March 2011. See https://www.qualityplanning.com/media/4312/110329%20tough%20times_f2.pdf 

(last accessed April 4, 2012). 

ooo U.S. Department of Commerce, Bureau of Economic Analysis, Table 7.2.5S. Auto and Truck Unit Sales, 

Production, Inventories, Expenditures, and Price, Available at 

http://www.bea.gov/national/nipaweb/nipa_underlying/Table7.2.5s (last accessed May 4, 2012) 

ppp http://www.bls.gov/cpi/cpid11av.pdf, Table 1A. Consumer Price Index for All Urban Consumers (CPI-U): 

U.S. city average, by expenditure category and commodity and service group, for new vehicles. 

qqqInsurance data did not differentiate between passenger cars and light trucks. Therefore, a 30-year lifetime was 

assumed in this analysis. Due to several factors, among them discounting, decreased vehicle value with age, and 

limited vehicle survival in later years of vehicles’ lifetimes, this assumption is of minimal impact on the results. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-57 

payments in the model’s calculations, and all calculations are adjusted by projected vehicle 

survival rates. 

The agencies considered whether to estimate incremental comprehensive and collision 

insurance premiums only to year 18. As vehicles age, it becomes increasingly impractical to 

purchase these forms of insurance, and the Quality Planning study indicates that many owners 

drop these forms of insurance much earlier – in some cases upon repayment of the initial auto 

loan. The agencies nevertheless use the 30-year lifetime of the vehicle because we use 

survival-weighted values, which take into account the probability that some vehicles are no 

longer incurring costs because they no longer exist. This approach may tend to overstate 

insurance costs, because many owners are not paying insurance premiums even on vehicles 

that continue to exist. . Therefore, the insurance premiums were age-adjusted to year 30 

using the assumption that by end-of-life, no vehicle would remain on comprehensive or 

collision insurance. This approach provides the agencies with our estimates of the impact of 

insurance costs on vehicle owners based on the expected increase in MSRP resulting from the 

rule. 

As discussed earlier, the scope of this analysis is to estimate the increased cost to the 

consumer for these standards, not the increase in societal costs or benefits. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-58 

References:

1

EPA, Final Rulemaking. Motor Vehicle Emissions Federal Test Procedure Revisions 

10/1996. 61 FR at 54852 

2

EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Calculation of Fuel 

Economy Estimates; Final Rule, 40 CFR Parts 86 and 600, 71 Fed. Reg, 77872 (Dec. 27, 

2006). Available at http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/Day27/a9749.pdf. 

3

EPA, Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle 

Revisions to Improve Calculation of Fuel Economy Estimates, Office of Transportation and 

Air Quality EPA420-R-06-017 December 2006, Chapter II, 

http://www.epa.gov/fueleconomy/420r06017.pdf. 

4

See 71 FR at 77887, and U.S. Environmental Protection Agency, Final Technical Support 

Document, Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of 

Fuel Economy Estimates, EPA420-R-06-017, December 2006 for general background on the 

analysis. See also EPA’s Response to Comments (EPA-420-R-11-005) to the 2011 labeling 

rule, page 189. 

136 EPA and NHTSA, § 600.210-12 Calculation of fuel economy and CO2 emission values 

for labeling. Revisions and Additions to Motor Vehicle Fuel Economy Label. Federal 

Register / Vol. 76, No. 129 / Wednesday,76 Fed. Reg. 39478 (July 6, 2011). § 600.115-11 . 

6

EPA. E15 Waiver Decision, EPA420-F-10-054. October 2010. 

7

EPA, Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle 

Revisions to Improve Calculation of Fuel Economy Estimates, at 70. Office of Transportation 

and Air Quality EPA420-R-06-017 December 2006, Chapter II, 

http://www.epa.gov/fueleconomy/420r06017.pdf. 

8

75 FRM at 25372 

9 OMB Circular A-4, September 17, 2003. 

http://www.whitehouse.gov/omb/assets/omb/circulars/a004/a-4.pdf 

10 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and 

Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at

http://www-nrd.nhtsa.dot.gov/pdf/nrd-30/NCSA/Rpts/2006/809952.pdf (last accessed Sept. 

9, 2011). 

11 FHWA, Highway Statistics, Summary to 1995, Table vm201at 

http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw , and annual editions 1996-2005, 

Table VM-1 at http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm (last accessed Feb. 15, 

2010). 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-59 

12 Energy Information Adminstration, AEO 2011 Final Relase, Light Duty Vehicle Miles 

Traveled by Technology Type. 

http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2011&subject=0-

AEO2011&table=51-AEO2011®ion=0-0&cases=ref2011-d020911a

13 Sorrell, S. and J. Dimitropoulos, 2007. “UKERC Review of Evidence for the Rebound 

Effect, Technical Report 2: Econometric Studies”, UKERC/WP/TPA/2007/010, UK Energy 

Research Centre, London, October and Greening, L.A., D.L. Greene and C. Difiglio, 2000. 

“Energy Efficiency and Consumption – The Rebound Effect – A Survey”, Energy Policy, vol. 

28, pp. 389-401. (Docket EPA-HQ-OAR-2010-0799) 

14 Pickrell, D. and P. Schimek, 1999. “Growth in Motor Vehicle Ownership and Use: 

Evidence from the Nationwide Personal Transportation Survey,” Journal of Transportation 

and Statistics, vol. 2, no. 1, pp. 1-17. (Document ID EPA-HQ-OAR-2010-0799-0027)

15 Puller, Steven and Lorna Greening. 1999. “Household Adjustment to Gasoline Price 

Change: An Analysis Using Nine Years of U.S. Survey Data," Energy Economics 21(1):37-

52. (Docket EPA-HQ-OAR-2010-0799) 

16 Small, K. and K. Van Dender, 2007a. “Fuel Efficiency and Motor Vehicle Travel: The 

Declining Rebound Effect.” The Energy Journal, vol. 28, no. 1, pp. 25-51. (Docket EPA-HQOAR-2010-0799) 

17 Small, K. and K. Van Dender, 2007b. "Long Run Trends in Transport Demand, Fuel Price 

Elasticities and Implications of the Oil Outlook for Transport Policy," OECD/ITF Joint 

Transport Research Centre Discussion Papers 2007/16, OECD, International Transport 

Forum. (Docket EPA-HQ-OAR-2010-0799) 

18 Report by Kenneth A. Small of University of California at Irvine to EPA, “The Rebound 

Effect from Fuel Efficiency Standards: Measurement and Projection to 2030,”, June 12, 2009. 

(See docket: EPA-HQ-OAR-2010-0799) 

19 Hymel, Kent M., Kenneth A. Small, and Kurt Van Dender, “Induced demand and rebound 

effects in road transport,” Transportation Research Part B: Methodological, Volume 44, Issue 

10, December 2010, Pages 1220-1241, ISSN 0191-2615, DOI: 10.1016/j.trb.2010.02.007. 

(Docket EPA-HQ-OAR-2010-0799) 

20Greene, David, 2012. “Rebound 2007: Analysis of U.S. light-duty vehicle travel statistics,” 

Energy Policy, vol. 41, pp. 14-28. (Docket EPA-HQ-OAR-2010-0799) 

21 Gillingham, Kenneth. “The Consumer Response to Gasoline Price Changes: Empirical 

Evidence and Policy Implications.” Ph.D. diss., Stanford University, 2011. (Document ID 

EPA-HQ-OAR-2010-0799-0760). The California Air Resources Board submitted this work 

for independent academic review in September 2011 and the three reviewers found the results 

and conclusions to be valid and reasonable. These reviews are available in Docket EPA-HQOAR-2010-0799 under the title, California Air Resources Board Independent Academic 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-60 

Review of Gillingham, Kenneth. “The Consumer Response to Gasoline Price Changes: 

Empirical Evidence and Policy Implications.” Ph.D. diss., Stanford University, 2011. 

22 Dargay, J.M.,Gately,D.,1997. “The demand for transportation fuels: imperfect pricereversibility?,” Transportation Research PartB 31(1). (Docket EPA-HQ-OAR-2010-0799) 

23 Sentenac-Chemin, E.,2012. “Is the price effect on fuel consumption symmetric? Some 

evidence from an empirical study,” Energy Policy, vol. 41, pp. 59-65. (Docket EPA-HQOAR-2010-0799) 

24 Dermot Gately, 1993. "The Imperfect Price-Reversibility of World Oil Demand," The 

Energy Journal, International Association for Energy Economics, vol. 14(4), pp. 163-182. 

(Docket EPA-HQ-OAR-2010-0799) 

25 Bento, Antonio M., Lawrence H. Goulder, Mark R. Jacobsen, and Roger H. von Haefen, 

2009, “Distributional and Efficiency Impacts of Increased US Gasoline Taxes,” American 

Economic Review, v. 99, pp. 1–37 and Su, Qing, 2012, “A Quantile Regresssion Analysis of 

the Rebound Effect: Evidence form the 2009 National Household Transportation Survey in 

the United States,” Energy Policy, v. 45, pp. 368-377. (Docket EPA-HQ-OAR-2010-0799) 

26 FHWA. 1997 Federal Highway Cost Allocation Study; 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed Sept. 9, 2011). 

27

 Federal Highway Administration, 1997 Federal Highway Cost Allocation Study, 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V-22, V-23, and V-24 (last 

accessed Sept. 9, 2011). 

28 http://www.eia.gov/totalenergy/data/monthly/pdf/sec1_13.pdf 

29 http://www.eia.gov/totalenergy/data/monthly/pdf/sec1_13.pdf 

30 http://www.eia.gov/totalenergy/data/monthly/pdf/sec3_3.pdf 

31 http://www.eia.gov/totalenergy/data/monthly/pdf/sec3_3.pdf 

32 U.S. Department of Energy, Annual Energy Review, 2008, Report No. DOE/EIA0384/2008 Tables 5.1 and 5.1c, June 26, 2009

33 http://www.census.gov/foreign-trade/statistics/historical/gands.txt. 

34 Leiby, Paul N. "Estimating the Energy Security Benefits of Reduced U.S. Oil Imports," 

Oak Ridge National Laboratory, ORNL/TM-2007/028, Final Report, 2008. 

35 ORNL-TM-2007-028_Oil_Import_Premium_revisited_2008Mar14 Rev8.pdf, "Estimating 

the U.S. Oil Security Premium for the Proposed 2017-2025 Light -Duty Vehicle GHG/Fuel 

Economy Rule", Paul N. Leiby, Oak Ridge National Laboratory. March 2008.

36 Ibid. References are in the back of the document. 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-61 

37 Estimating the U.S. Oil Security Premium for the Proposed 2017-2025 Light -Duty Vehicle 

GHG/Fuel Economy Rule", Paul N. Leiby, Oak Ridge National Laboratory (ORNL), July 15 

2012

38 Based on data from the CIA, combining various recent years, 

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2176rank.html. 

39 IEA 2011 “IEA Response System for Oil Supply Emergencies”. 

40 Transcript of Philadelphia Public Hearing, p. 175 (January 19, 2012). 

41 Center for Naval Analyses, “Ensuring America’s Freedom of Movement: A National 

Security Imperative to Reduce U.S. Oil Dependence” October 2011. 

42 U.S. Department of Defense. 2010. Quadrennial Defense Review Report. Secretary of 

Defense: Washington, D.C. 128 pages. 

43 The Department of the Navy’s Energy Goals 

(http://www.navy.mil/features/Navy_EnergySecurity.pdf) (Last accessed May 31, 2011). 

44 U.S. Department of Defense, Speech: Remarks at the White House Energy Security 

Summit. Tuesday, April 26, 2011. 

(http://www.defense.gov/speeches/speech.aspx?speechid=1556) (Last accessed May 31, 

2011). 

45 Leiby, Paul. “Military Costs of Energy Security”. 2012. 

46 Delucchi, Mark A. and James J. Murphy. “US military expenditures to protect the use of 

Persian Gulf oil for motor vehicles.” Energy Policy 36, no. 6 (June 2008): 2253-2264. 

doi:10.1016/j.enpol.2008.03.006. 

http://linkinghub.elsevier.com/retrieve/pii/S0301421508001262. 

47 Crane, Keith, et al. “Does Imported Oil Threaten U.S. National Security?” RAND, 2009. 

48 Stern, RJ. “United States cost of military force projection in the Persian Gulf, 1976–2007”, 

Energy Policy, 2010. 

49 EPA MOVES model ver 2010a (http://www.epa.gov/otaq/models/moves/index.htm). 

50 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from 

Transportation (GREET) Model, Version 1.8, June 2007, available at http://greet.es.anl.gov/ 

(last accessed Sept. 9, 2011). 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-62 

51 Calculation of Upstream Emissions for the Greenhouse Gas (GHG) Vehicle Rule, 

Memorandum from Craig A. Harvey to Docket EPA-HQ-OAR-2009-0472, September 14, 

2009 

52 Calculation of Upstream Emissions for the Greenhouse Gas (GHG) Vehicle Rule, 

Memorandum from Craig A. Harvey to Docket EPA-HQ-OAR-2009-0472, September 14, 

2009 

53 The issue is discussed in more detail in the PM NAAQS RIA from 2006. See U.S. 

Environmental Protection Agency. 2006. Final Regulatory Impact Analysis (RIA) for the 

Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office 

of Air and Radiation. October 2006. Available at http://www.epa.gov/ttn/ecas/ria.html. 

54 U.S. Environmental Protection Agency (U.S. EPA), 2010. Regulatory Impact Analysis, 

Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and 

Corporate Average Fuel Economy Standards. Office of Transportation and Air Quality. 

April. Available at http://www.epa.gov/otaq/climate/regulations/420r10009.pdf. EPA-420-R10-009 

55 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 

2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of 

Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. EPA-HQ-OAR-2009-

0472-0238 

56 U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: 

National Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. Augues. Available on the Internet at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf >. EPA-HQ-OAR2009-0472-0241 

57 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory 

Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. April. Available on the Internet at 

http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 

15, 2010. EPA-HQ-OAR-2009-0472-0237 

58 U.S. Environmental Protection Agency (U.S. EPA). 2008. Technical Support Document: 

Calculating Benefit Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2007-0225-

0284. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. 

Available on the Internet at <http://www.regulations.gov>. EPA-HQ-OAR-2009-0472-0228 

59 Fann, N. et al. (2009). The influence of location, source, and emission type in estimates of 

the human health benefits of reducing a ton of air pollution. Air Qual Atmos Health. 

Published online: 09 June, 2009. EPA-HQ-OAR-2009-0472-0229 

60 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 
Economic and Other Assumptions Used in the Agencies' Analysis 

4-63 

Air Pollution.” Journal of the American Medical Association 287:1132-1141. EPA-HQOAR-2009-0472-0263 

61 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine 

Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care 

Medicine 173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. Washington, DC: The National Academies Press. EPA-HQ-OAR-2009-0472-

1661 

62 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A MetaAnalysis.” Journal of Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-

0472-1677 

63 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of 

Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. EPAHQ-OAR-2009-0472-0245 

64 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis. Environmental 

and Resource Economics. 34: 385-406. EPA-HQ-OAR-2009-0472-0235 

65 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. National Center for Environmental Economics, 

Office of Policy Economics and Innovation. Washington, DC. September. Available on the 

Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. EPAHQ-OAR-2009-0472-0226 

66 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from 

Transportation (GREET) Model, Version 1.8, June 2007, available at

http://www.transportation.anl.gov/software/GREET/index.html (last accessed April 20, 

2008). 

67 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost 

of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency 

Working Group on Social Cost of Carbon, with participation by Council of Economic 

Advisers, Council on Environmental Quality, Department of Agriculture, Department of 

Commerce, Department of Energy, Department of Transportation, Environmental Protection 

Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of 

Treasury (February 2010). Also available at http://epa.gov/otaq/climate/regulations.htm 

68 Marten, A. and S. Newbold. 2011. “Estimating the Social Cost of Non-CO2 GHG 

Emissions: Methane and Nitrous Oxide.” NCEE Working Paper Series #11-01. 

http://yosemite.epa.gov/ee/epa/eed.nsf/WPNumber/2011-01?opendocument. Accessed May 

24, 2012. 

69 Id.
Economic and Other Assumptions Used in the Agencies' Analysis 

4-64 

70 Office of Management and Budget, Circular A-4, “Regulatory Analysis,” September 17, 

2003, 33. Available at http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed 

July 24, 2009). 

71 Id.
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-1 

Chapter 5: Air Conditioning, Off-Cycle Credits, and Other 

Flexibilities 

5.1 Air conditioning technologies and credits 

5.1.1 Overview 

Air conditioning (A/C) is virtually standard equipment in new cars and trucks, as over 

95% of the new cars and light trucks in the United States are equipped with mobile air 

conditioning (or MAC) systems. Given the large penetration of A/C in today’s light duty 

vehicle fleet, its impact on the amount of energy consumed is significant. In the MYs 2012-

2016 Light-Duty Greenhouse Gas Rule, EPA structured the rule to allow vehicle 

manufacturers’ to generate credits for improved air conditioner systems in complying with the 

CO2 fleetwide average standards and accounted for these AC improvements in determining 

the stringency of the GHG standards. EPA will continue with and expand upon these 

provisions, and manufacturers can generate credits for improved performance of both direct 

(A/C leakage) and indirect (tailpipe emissions attributable to A/C use) A/C emissions. In 

addition, EPA is acting pursuant to its authority under EPCA to allow manufacturers to 

generate fuel consumption improvement values for purposes of CAFE compliance based on 

the use of A/C efficiency-improving technologies. In the 2012-2016 rule, EPA and NHTSA 

did not allow manufacturers to include reductions in fuel consumption resulting from A/C 

efficiency improvements) in the CAFE calculations. As was the case in the MYs 2012-2016

rule, manufacturers do not to count reductions in A/C leakage toward their CAFE calculations 

since these improvements do not affect fuel economy. In the sections below, the agencies will 

first describe the structure of the EPA A/C program, followed by a description of the A/C 

program under CAFE. 

Through model years 2012-2016, EPA expects that all manufacturers will generate 

A/C credits offered (for reduced leakage and improved efficiency) to help come into 

compliance with the standards. EPA estimated that there would be significant penetration of 

A/C technologies to gain credits, and this was reflected in the stringency of the standards.a

Consistent with the 2008-based fleet definitions, the base level of A/C technologies in 2008 

forms the A/C “baseline”, and the A/C technologies projected to penetrate to the fleet in 2016 

is referred to as the A/C “reference”. For this MYs 2017-2025 rule, EPA will maintain the 

credit program the amount of credit being determined in relation to the MY 2008 baseline . 

The credits should continue to the present rule since without them, a manufacturer utilizing 

credits in MY 2016 could suddenly find in MY 2017 that the stringency of the standards are 

artificially increased due to discontinued A/C credits.b In this chapter, A/C credits are 

 

a

 NHTSA will also be referencing these efficiency improving A/C technologies in its rule, referring to them as 

“fuel consumption improvements.” 

b

 Put another way, the MY 2016 GHG standards would remain even if there were no new MY 2017-2025 

standards and A/C credits would also continue. Thus, if the AC credits were removed or significantly changed 

from these (perpetuated) post-2016 standards, the stringency of those standards would effectively be increased. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-2 

calculated from the 2008 baseline vehicle fleet (though there are some changes to the credit 

program), while costs are calculated from the 2008 model year based reference fleet. Any 

additional A/C credits projected for MYs 2017-2025 are reflected in the stringency of the 

standards as described in Section III.C.1 of the preamble. 

A/C is different from the other technologies described in Chapter 3 of the joint TSD in 

several ways. First, most of the technologies described earlier directly affect the efficiency of 

the engine, transmission, and vehicle systems. As such, these systems are almost always 

active while the vehicle is moving down the road or while being tested on a dynamometer for 

the fuel economy and emissions test drive cycles. A/C, on the other hand, is a parasitic load 

on the engine that only burdens the engine when the vehicle occupants demand it. Since it is 

not tested as a part of the fuel economy and GHG emissions standards compliance test drive 

cycles (the A/C system is off while the vehicle is operated on the two test cycles -- the FTP 

and HFET -- used for compliance purposes) it is referred to as an “off-cycle” effect. There 

are many other off-cycle loads that can be switched on by the occupant that affect the engine; 

these include lights, wipers, stereo systems, electrical defroster/defogger, heated seats, power 

windows, etc. However, these electrical loads individually amount to a very small effect on 

the engine (although together they can be significant). The A/C system (by itself) adds a 

significant load on the engine (especially on sunny, hot, and/or humid days), resulting in 

increased fuel consumption, or “indirect” CO2 emissions. 

There are two mechanisms by which A/C systems contribute to the emissions of 

greenhouse gases. The first is through direct leakage of the refrigerant into the air. The 

hydrofluorocarbon (HFC) refrigerant compound currently used in all recent model year 

vehicles is R-134a (also known as 1,1,1,2-Tetrafluoroethane, or HFC-134a). Based on the 

higher global warming potential of HFCs, a small leakage of the refrigerant has a greater 

global warming impact than a similar amount of emissions of some other mobile source 

GHGs. R-134a has a global warming potential (GWP) of 1,430. This means that 1 gram of 

R-134a has the equivalent global warming potential of 1,430 grams of CO2 (which has a 

GWP of 1). In order for the A/C system to take advantage of the refrigerant’s 

thermodynamic properties and to exchange heat properly, the system must be kept at high 

pressures even when not in operation. Typical static pressures can range from 50-80 psi 

depending on the temperature, and during operation, these pressures can get to several 

hundred psi. At these pressures leakage can occur through a variety of mechanisms. The 

refrigerant can leak slowly through seals, gaskets, and even small failures in the containment 

of the refrigerant. Through normal use, the rate of leakage may also increase due to wear on 

the system components. Leakage may also increase more quickly through rapid component 

deterioration such as during vehicle accidents, maintenance or end-of-life vehicle scrappage 

(especially when refrigerant capture and recycling programs are less efficient). Small 

amounts of leakage can also occur continuously even in extremely “leak-tight” systems by 

permeating through hose membranes and seals. This last mechanism is not dissimilar to fuel 

permeation through porous fuel lines (and seals). Manufacturers may be able to reduce these 

leakage emissions through the implementation of technologies/designs such as leak-tight, 

non-porous, durable components. The global warming impact of leakage emissions also can 

be addressed by using alternative refrigerants, such as HFO-1234yf, R-744 (CO2), HFC-152a 

(R-152a), or other refrigerants under development with lower global warming potentials than 

R -134a. Refrigerant emissions can also occur during maintenance and at the end of the 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-3 

vehicle’s life (as well as emissions during the initial charging of the system with refrigerant), 

and these emissions are already addressed by the CAA Title VI stratospheric ozone program, 

as described below.c 

The second mechanism by which vehicle A/C systems contribute to GHG emissions is 

through the consumption of additional fuel required to provide power to the A/C system and 

from carrying around the weight of the A/C system hardware year-round. These indirect 

emissions result from the additional fuel which is required to provide power to the A/C 

system (and the additional fuel is converted into CO2 by the engine during combustion). 

These increased emissions due to A/C operation can be reduced by increasing the overall 

efficiency of the vehicle’s A/C system, as described below. The final rules do not provide 

credits for the weight of the A/C system, since the incremental increase in CO2 emissions and 

fuel consumption due to carrying the A/C system is directly measured during the normal (2-

cycle) federal test procedure, and is thus already accounted for in the CO2 tailpipe standard. 

EPA’s analysis from the MYs 2012-2016 rule indicates that A/C-related indirect 

emissions represent about 3.9% of the total greenhouse gas emissions from cars and light 

trucks. In this document, EPA will separate the discussion of these two categories of A/Crelated emissions because of the fundamental differences in the emission mechanisms and the 

methods of emission control. Refrigerant leakage control is akin in many respects to past 

EPA fuel evaporation control programs (in that containment of a fluid is the key feature), 

while efficiency improvements are more similar to the vehicle-based control of CO2 using the 

technologies described in chapter 3 of the joint TSD in that emission reductions would be 

achieved through specific hardware and controls. Finally, the accounting for credits for 

control of direct and indirect A/C improvement credits is independent, which allows for a 

separate discussion of these two categories. 

5.1.2 Air Conditioner Leakage 

5.1.2.1 Impacts of Refrigerant Leakage on Greenhouse Gas Emissions 

There have been several studies in the literature which have attempted to quantify the 

emissions (and impact) of air conditioner HFC emissions from light duty vehicles. In this 

section, several of these studies are discussed. These inventories and impacts form the basis 

for the air conditioner environmental credits, and in this final rule, we are using the same 

emissions inventory and analysis method for refrigerant leakage as we did in the 2012-2016 

rule as described in section 5.1.2.3.3. 

 

c

 Even if A/C systems utilize a “low-GWP” refrigerant, such as HFO-1234yf (GWP = 4), emissions are still a 

concern. First, as refrigerant leaks from the system, once the refrigerant level drops to 40 to 50 percent of its 

normal capacity, the operating efficiency of the system will degrade, resulting in an increase in fuel consumption 

due to A/C use, and an increase in indirect emissions. Second, if systems do leak refrigerant at an excessive rate, 

there is a higher probability that someone will unlawfully recharge the system with a cheaper, and higher-GWP 

refrigerant, resulting in increased direct emissions. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-4 

Based on measurements from 300 European vehicles (collected in 2002 and 2003), 

Schwarz and Harnisch estimate that the average HFC direct leakage rate from modern A/C 

systems was 53 g/yr.1 This corresponds to a leakage rate of 6.9% per year. This was 

estimated by extracting the refrigerant from recruited vehicles and comparing the amount 

extracted to the amount originally filled (as per the vehicle specifications). The fleet and size 

of vehicles differs from Europe and the United States, therefore it is conceivable that vehicles 

in the United States could have a different leakage rate. The authors measured the average 

charge of refrigerant at initial fill to be about 747 grams (it is somewhat higher in the U.S. at 

770g), and that the smaller cars (684 gram charge) emitted less than the higher charge 

vehicles (883 gram charge). Moreover, due to the climate differences, the A/C usage patterns 

also vary between the two continents, which may influence leakage rates. 

Vincent et al., from the California Air Resources Board estimated the in-use 

refrigerant leakage rate to be 80 g/yr.2 This value is based on the consumption of refrigerant 

in commercial fleets, and surveys of vehicle owners and technicians. The study assumed an 

average A/C charge size of 950 grams and a recharge rate of 1 in 16 years (lifetime). The 

recharges occurred when the system was 52% empty and the fraction recovered at end-of-life 

was 8.5%. 

5.1.2.1.1 Emission Inventory 

The EPA publishes an inventory of greenhouse gases and sinks on an annual basis. 

The refrigerant emissions numbers that are used in the present analysis are from the Vintaging 

model, which is used to generate the emissions included in this EPA inventory source. The 

HFC refrigerant emissions from light duty vehicle A/C systems was estimated to be 61.8 Tg 

CO2 equivalent in 2005 by the Vintaging model.3,d In 2005, refrigerant leakage accounted for 

about 5.1% of total greenhouse gas emissions from light duty sources. From a vehicle 

standpoint, the Vintaging model assumes that 42% of the refrigerant emissions are due to 

direct leakage (or “regular” emissions), 49% for service and maintenance (or “irregular” 

emissions), and 9% occurs at disposal or end-of-life as shown in the following table. These 

are based on assumptions of the average amount of chemical leaked by a vehicle every year, 

how much is lost during service of a vehicle (from professional service center and do-ityourself practices), and the amount lost at disposal. These numbers vary somewhat over time 

based on the characteristics (e.g. average charge size and leakage rate) of each “vintage” of 

A/C system, assumptions of how new A/C systems enter the market, and the number of 

vehicles disposed of in any given year. 

Table 5-1 Light Duty Vehicle HFC-134a Emissions in 2005 from Vintaging Model - HFC Emissions 

Multiplied by 1430 GWP to Convert to CO2 Equivalent 

Emission Process HFC emissions (metric 

tons)

Fraction of total

 

d

 EPA reported the MVAC emissions at 56.6 Tg CO2 EQ, using a GWP of 1300. This number has been adjusted 

using a GWP of 1430. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-5 

Leakage 18,151 0.42

Maintenance/servicing 21,176 0.49

Disposal/end-of-life 3,890 0.09

Total 43,217 1.0

5.1.2.2 Alternative Refrigerants 

Leakage emissions can also be reduced with the use of refrigerants other than R-134a, 

which has a global warming potential (GWP) of 1430. To address future GHG regulations in 

the Europe Union and the State of California, air conditioning systems which use alternative 

refrigerants are under development, and have been demonstrated in prototypes by vehicle 

manufacturers and A/C component suppliers. The European Union has enacted regulations 

which require the use of refrigerants with a GWP less than 150. Phase-in of these EU 

regulations began with new vehicle platforms in MY 2011, and will be completely phased-in 

for all vehicles by MY 2017. Some of the alternative refrigerants under development by 

manufacturers and A/C component suppliers include HFO-1234yf, CO2, HFC-152a, and lowGWP blends of existing refrigerants. The air conditioning component and refrigerant 

manufacturers, as well as automotive manufacturers, are actively studying the performance, 

efficiency, safety, and cost of these alternative refrigerants. 

HFO-1234yf, with a GWP of 4, is a leading candidate as an alternative to R-134a 

refrigerant. For example, General Motors has selected HFO1234yf for use in certain model 

year 2013 vehicles.4

 While the performance and efficiency of A/C systems using HFO1234yf can be equivalent to those using HFC-134a, the higher cost of implementing this 

refrigerant – estimated at $67 (2010$, direct manufacturing cost) per vehicle in model year 

2016 (see section 5.1.4) – is causing the industry to consider other solutions which are lowercost. 

A so-called “natural refrigerant” under consideration is CO2, which has a GWP of 1. 

While this refrigerant is environmentally neutral from a GWP perspective (i.e. relative to a 

CO2 baseline), and is currently used in some commercial refrigeration units, its use in 

automotive applications is challenging due to the higher operating pressure of CO2 systems, 

where the peak pressure can be as high as 2000 PSI, compared to the peak pressure in HFC134a systems of around 450 PSI. Several European auto manufacturers have successfully 

developed CO2 A/C systems, but none have been produced for use in new vehicles at this 

time. An A/C system which uses CO2 is estimated to cost from about $140 to $210 more than 

an equivalent HFC-134a system; however, the cost of the refrigerant itself is expected to be 

considerably less than HFO-1234yf.5

HFC-152a (1,1-difluoroethane) is a flammable refrigerant with a GWP of 120 and an 

ASHRAE flammability designation of Class 2. Given the flammability of this refrigerant, we 

expect that manufacturers would either need to design their A/C systems with a secondary 

loop or with directed relief valves to mitigate safety concerns within the cabin area, and to 

comply with the use conditions at 40 CFR Part 82 Subpart G Appendix B. With a secondary 

loop design, the evaporator is not located inside the passenger cabin area, but inside a chiller 

in an underhood location, where a secondary fluid (such as an ethylene glycol-water mixture) 

is circulated to transfer heat from the cabin to the chiller. This approach requires additional 

system components (chiller, pump, reservoir, and plumbing for secondary loop), which adds 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-6 

an estimated 12 lbs. of mass to the vehicle.6 Secondary loop systems have added value in that 

they have the ability to store cooling within the loop, which in turn allows for “free” cooling 

to occur during deceleration events, and then delivered to the cabin during engine idle off 

conditions (for example). With the directed relief valve design, the refrigerant within the A/C 

system is vented and ducted to the atmosphere by opening high and low-side relief valves 

when a leak is detected.7 The advantage of the directed relief valve approach (relative to a 

secondary loop) is that fewer components are needed, potentially minimizing the mass and 

cost of the system. 

Other alternative refrigerants which may be used in the future may include low-GWP 

blends. Recent studies have shown that the low-GWP refrigerant blends AC5 and AC6 from 

the chemical manufacturer Mexichem, have performance and efficiency characteristics which 

are similar to HFC-134a under high-load (maximum cooling) conditions, and slightly reduced 

performance and efficiency under low-load conditions. These mildly-flammable (similar to 

HFO-1234yf) refrigerant blends, being comprised of several different refrigerant components, 

have zeotropic properties. This means that the fraction of each component in the gas and 

liquid phases is not constant, and varies with temperature and pressure within the system.8

 

Zeotropic behavior may result in mal-distribution of the refrigerant within the evaporator and 

condenser, which negatively affects system efficiency, especially at low loads.9

 However, it 

is believed that optimization of evaporator and condenser design can improve the low-load 

efficiency. These blends may be similar enough in performance and in their physical 

characteristics to HFC-134a and HFO-1234yf that they may be used in current production 

A/C systems designs with relatively minor modifications 

We expect that stakeholders in the automotive A/C industry will continue to study and 

develop low-GWP refrigerant solutions in order to minimize the direct and indirect impact of 

A/C-related emissions. With the statutory requirements for low-GWP refrigerants in the 

European Union, which began in model year 2011 for new vehicles designs, we expect that 

one or more of these low-GWP solutions will be available for at least 20% of the U.S. vehicle 

fleet by model year 2017, and that an additional 20% of the fleet can adopt the alternative 

refrigerant in each subsequent model year. EPA expects that manufacturers would be 

changing over to alternative refrigerants at the time of complete vehicle redesign, which 

occurs about every 5 years, though in confidential meetings, some manufacturers/suppliers 

have informed EPA that it may be possible to modify the hardware for some alternative 

refrigerant systems between redesign periods. 

5.1.2.3 A/C Leakage Credit 

The level to which each technology can reduce leakage can be estimated using the 

February 2012 version of SAE Surface Vehicle Standard J2727 – HFC-134a Mobile Air 

Conditioning System Refrigerant Emission Chart. While this standard was developed for 

leakage of HFC-134a refrigerant, it is also applicable to the alternative refrigerant HFO1234yf, and may be applicable to other low-GWP refrigerants as well. To convert J2727 

chart emission (leak) rates from HFC-134a to HFO-1234yf leakage rates, the result is 

multiplied by 1.03. This conversion factor for HFO-1234yf is derived by multiplying the ratio 

of the molecular weights of the two refrigerants (114 kg/kmol for HFO-1234yf and 102 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-7 

kg/kmol for HFC-134a) by the inverse ratio of the dynamic viscosities of the two refrigerants 

(11.1 x 10-6 Pa·s for HFC-134a and 12.0 x 10-6 Pa·s for HFO-1234yf). 

The J2727 standard was developed by SAE and the cooperative industry and 

government IMAC (Improved Mobile Air Conditioning) program using industry experience,

laboratory testing of components and systems, and field data to establish a method for 

calculating leakage. With refrigerant leakage rates as low as 10 g/yr, it would be exceedingly 

difficult to measure such low levels in a test chamber (or shed). Since the J2727 method has 

been correlated to “mini-shed”, or SAE J2763, results (where A/C components are tested for 

leakage in a small chamber, simulating real-world driving cycles), the EPA considers this 

method to be an appropriate surrogate for vehicle testing of leakage.10 It is also referenced by 

the California Air Resources Board in their Environmental Performance Label regulation and 

the State of Minnesota in their GHG reporting regulation.11,12

 

5.1.2.3.1 Why Is EPA Continuing to Rely on a Design-Based Approach to Quantify 

Leakage? 

EPA is not reopening, reconsidering, or otherwise reevaluating its approach to 

quantifying A/C leakage in the MYs 2012-2016 final rule. However, as in the MYs 2012-

2016 rule, EPA will continue to use a design-based method for quantifying refrigerant leakage 

from A/C systems. In the time since the MYs 2012-2016 rule was finalized, the Agency was 

not informed of any new approaches or methods for measuring actual refrigerant leak rates. 

While EPA generally prefers performance testing for emissions, a feasible method for 

measuring refrigerant emissions accurately from a vehicle is not available, and we are 

finalizing for MYs 2017-2025 a continuation of the SAE J2727-based approach adopted in the 

MYs 2012-2016 rule. EPA believes that the SAE J2727 method, as discussed below, is an 

appropriate method for quantifying the expected yearly refrigerant leakage rate from A/C 

systems. 

5.1.2.3.2 How Will Leakage Credits Be Calculated? 

For model years 2017 through 2025, the A/C credit available to manufacturers will be 

calculated based on how much a particular vehicle’s annual leakage value is reduced 

compared to an average MY 2008 vehicle with baseline levels of A/C leakage technology, 

and will be calculated using a method drawn directly from the updated SAE J2727 approach 

(for details on these updates, see 5.1.2.3.2.2). By scoring the minimum leakage rate possible 

on the J2727 components enumerated in the rule (expressed as a measure of annual leakage), 

a manufacturer can generate the maximum A/C credit (on a gram per mile basis). To avoid 

backsliding on leakage rates when using low-GWP refrigerants, where manufacturers could 

choose less costly sealing technologies and/or materials, EPA is finalizing the proposed 

disincentive credit for “high leak” on alternative refrigerant systems. The maximum value for 

this high leak disincentive credit (or HiLeakDisincentive) is 1.8 g/mi for cars and 2.1 g/mi for 

trucks, with lower amounts possible for leakage rates between the minimum leakage score 

(MinScore) and the average impact (AvgImpact). The terms used for calculating the A/C 

Leakage Credit as well as the HiLeakDisincentive are discussed later in this section. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities

5-8 

The A/C credit available to manufacturers will be calculated based on the reduction to 

a vehicle’s yearly leakage rate, using the following equation for HFC-134a refrigerant: 

Equation 5-1 Credit Equation for HFC-134a Refrigerant

A/C Leakage Credit = (MaxCredit) * [ 1 - (§86.166-12 Score / AvgImpacte ) * 

(GWPRefrigerant / 1430)] 

and the following equation for low-GWP, alternative refrigerants:

Equation 5-2 Credit Equation for Alternative Refrigerants

A/C Leakage Credit = (MaxCredit) * [ 1 - (§86.166-12 Score / AvgImpacte ) * 

(GWPRefrigerant / 1430)] - HiLeakDisincentive

where the HiLeakDisincentive is determined in accordance with one of the following three 

conditions, depending on the refrigerant capacity (RefrigCapacity), or charge level, of the A/C 

system: 

For A/C systems with a refrigerant capacity <= 733g:

HiLeakDis = 0.0, if Score ≤ 11.0 g/yr

HiLeakDis = 1.8 ∗ �Score − 11

3.3 � , if 11.0 < Score ≤ 14.3, 

HiLeakDis = 1.8, if Score > 14.3

For A/C systems with a refrigerant capacity > 733g:

HiLeakDis = 0.0, if Score ≤ RefrigCapacity ∗ 0.015

HiLeakDis = 1.8 ∗ (Score − (RefrigCapacity ∗ 0.015)/3.3),if RefrigCapacity ∗ 0.015

< Score ≤ RefrigCapacity ∗ 0.015 + 3.3

HiLeakDis = 1.8, if Score > RefrigCapacity ∗ 0.015 + 3.3

There are four significant terms to the credit equation. Each is briefly summarized below, and 

is then explained more thoroughly in the following sections. Please note that the values of 

 

e Section 86.166-12 sets out the individual component leakage values based on the SAE value.
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-9 

many of these terms change depending on whether HFC-134a or an alternative refrigerant is 

used. The values are shown in Table 5-2, and are documented in the following sections. 

“MaxCredit” is a term for the maximum amount of credit entered into the equation 

before constraints are applied to terms. The maximum credits that could be generated 

by a manufacturer is limited by the choice of refrigerant and by assumptions regarding 

maximum achievable leakage reductions. Some of these values may have changed 

since the 2012-2016 rule. 

“Score” is the leakage score of the A/C system as measured according to the §86.166-

12 calculation in units of g/yr, where the minimum score which is deemed feasible is 

fixed. 

“AvgImpact” is a term which represents the annual average impact of A/C leakage. 

“MinScore” is the lowest leak score that EPA projects is possible, when starting from 

a baseline, or AvgImpact, system. The MinScore represents a 50% reduction in 

leakage from the baseline levels based on the feasibility analysis detailed below. 

“GWPRefrigerant” is the global warming potential for direct radiative forcing of the 

refrigerant as defined by EPA (or IPCC). 

“HiLeakDisincentive” is a term for the disincentive credit deducted for low-GWP 

alternative refrigerant systems which have a leakage rate greater than the minimum 

leakage score of 11.0 g/year for cars and trucks. The maximum Disincentive is 1.8 

g/mile for cars and 2.1 g/mile for trucks. The 11.0 g/year threshold for generating a 

HiLeakDisincentive is based on the analysis we used for the MY 2014-2018 GHG 

Emissions Standards for Heavy-Duty Engines and Vehicles, where a maximum 

refrigerant leak rate standard of 11.0 g/year was set for vehicles with a refrigerant 

capacity of 733 g or lower, and 1.5 percent of the refrigerant capacity (in grams) for 

systems with a refrigerant capacity greater than 733 g. . 

Table 5-2 Components of the A/C Credit Calculation 

HFC-134a

Lowest-GWP 

Refrigerant 

(GWP=1)

Cars Trucks Cars Trucks

MaxCredit equation input (grams/mile CO2 EQ) 12.6 15.6 13.8 17.2

A/C credit maximum (grams/mile CO2 EQ)a

6.3 7.8 13.8 17.2

§86.166-12 MinScore (grams HFC/year) 8.3 10.4 8.3 10.4

Avg Impact (grams HFC/year) 16.6 20.7 16.6 20.7

 

a With electric compressor, value increases to 9.5 and 11.7 for cars and trucks, respectively. 

5.1.2.3.2.1 Max Credit Term 

In order to determine the maximum possible credit on a gram per mile basis, it was 

necessary to determine the projected real world HFC emissions per mile. This calculation is 

done exactly the same as it was done for the MYs 2012-2016 final rule. Because HFC is a 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-10 

leakage type of emission, it is largely disconnected from vehicle miles traveled (VMT).f 

Consequently, EPA calculated the total HFC inventory (in 2016), and then calculated the 

VMT for that year separately. The quotient of these two terms is the HFC contribution per 

mile. 

Consistent with the methodology presented in the MYs 2012-2016 rule, the HFC 

emission inventories were estimated from a number of existing data sources. The per-vehicle 

per-year HFC emission of the current vehicle fleet was determined using averaged 2005 and 

2006 registration data from the Transportation Energy Databook (TEDB) and 2005 and 2006 

mobile HFC leakage estimates from the EPA Emissions and Sinks report described above.3

,13

 

The per-vehicle per-year emission rates were then adjusted to account for the new definitions 

of car and truck classes by increasing the car contribution proportionally by the percentage of 

former trucks that are reclassified as cars.g

 This inventory calculation assumes that the 

leakage rates and charge sizes of future fleets (absent any standards) are equivalent to the fleet 

present in the 2005/2006 reference years. Preliminary EPA analysis indicates that this may 

increasingly overstate the future HFC inventory, as charge sizes are decreasing, though more 

is discussed on this topic below. 

The per-vehicle per-year average emission rate was then scaled by the projected 

vehicle fleet in each future year (using the fleet predicted in the emissions analysis) to 

estimate the HFC emission inventory if no further controls were enacted on the fleet. After 

dividing the 2016 inventory by total predicted VMT in 2016, an average per mile HFC 

emission rate (“base rate”) was obtained. 

The base rate is an average in-use number, which includes both old vehicles with 

significant leakage, as well as newer vehicles with very little leakage. The new vehicle 

leakage rate is discussed in section 5.1.2.4, while deterioration is discussed in section 5.1.2.6.

Max Credit with Conventional Refrigerant (HFC-134a)

Two adjustments were made to the base rate in order to calculate the Maximum HFC 

credit with conventional refrigerant. First, EPA has determined that 50% leakage 

prevention is the maximum potentially feasible prevention rate in the timeframe of this 

rule (see section 5.1.2.4). Some leaks will occur and are expected, regardless of 

prevention efforts. The accuracy of the J2727 approach (as expressed in §86.112), as 

a design based test, decreases as the amount of expected leakage diminishes. 50% of 

the base rate is therefore set as the maximum potential leakage credit for 

improvements to HFC leakage using conventional refrigerant. 

 

f

 In short, leakage emissions occur even while the car is parked, so the connection to a gram/mile credit is not 

straightforward. However, HFC emissions must be converted to a gram/mile basis in order to create a relevant 

credit. 

g

 Many of these “older” references still use the old definition of car and truck. The new definitions do not apply 

until model year 2011. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-11 

Second, EPA expects that improvements to conventional refrigerant systems will 

affect both leakage and service emissions, but will not affect end of life emissions. 

EPA expects that reductions in the leakage rate from A/C systems will result in fewer 

visits for maintenance and recharges. This will have the side benefit of reducing the 

emissions leftover from can heels (leftover in the recharge cans) and the other releases 

that occur during maintenance. However, as disposal/end of life emissions will be 

unaffected by the leakage improvements (and also are subject to control under the 

rules implementing Title VI of the CAA), the base rate was decreased by a further 9% 

(Table 5-1). 

Max Credit with Alternative Refrigerant 

Emission reductions greater than 50% are possible with alternative refrigerants. As an 

example, if a refrigerant with a GWP of 0 were used, it would be possible to eliminate 

all refrigerant GHG emissions. In addition, for alternative refrigerants, the EPA 

believes that vehicles with reduced GWP refrigerants should get credit for end of life 

emission reductions. Thus, the maximum credit with alternative refrigerant is about 

9% higher than twice the maximum leakage reduction. 

As discussed above, EPA recognizes that substituting a refrigerant with a significantly 

lower GWP will be a very effective way to reduce the impact of all forms of refrigerant 

emissions, including maintenance, accidents, and vehicle scrappage. 

The A/C Leakage Credits that will be available will be a function of the GWP of the 

alternative refrigerant as well as of leakage, with the largest credits being available for 

refrigerants with GWPs at or approaching a value of 1, while also maintaining a low leakage 

rate. For a hypothetical alternative refrigerant with a GWP of 1 (e.g., CO2 as a refrigerant), 

effectively eliminating leakage as a GHG concern, our credit calculation method could result 

in maximum credits equal to total average emissions, or credits of 13.8 and 17.2 g/mi CO2eq 

for cars and trucks, respectively, as incorporated into the A/C Leakage Credit formula above 

as the "MaxCredit" term. 

As we did for the MYs 2012-2017 rule, EPA made a final adjustment to each credit to 

account for the difference between real-world HFC emissions and test-cycle CO2 emissions. 

It has been shown that the tests currently used for CAFE certification represents an 

approximately 20% gap from real world fuel consumption and the resulting CO2 emissions.14 

Because the credits from direct A/C improvements are taken from a real world source, and are 

being traded for an increase in fuel consumption due to increased CO2 emissions, the credit 

was multiplied by 0.8 to maintain environmental neutrality (Table 5-3). 

Table 5-3 HFC Credit Calculation for Cars and Trucks Based on a GWP of 1430 

HFC 

Inventory 

(MMT 

CO2 EQ)

VMT 

(Billions 

of Miles)

Total HFC 

EmissionsPer 

Mile 

(CO2 EQ 

Gram/mile)

HFC 

Leakage and 

Service 

EmissionsPer 

Mile 

(CO2 EQ 

Maximum 

Credit w/ 

alternative 

refrigerant 

(Adjusted 

for OnMaximum 

Credit w/o 

alternative 

refrigerant 

(50% of

Adjusted 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-12 

Gram/mile) road gap & 

including 

end of life)

HFC & 

excluding 

end of life)

Car 27.4 1,580 17.2 15.5 13.8 6.3

Truck 30.4 1,392 21.5 19.6 17.2 7.8

Total 57.8 2,972 18.6 16.9 14.9 6.8

5.1.2.3.2.2 Section 86.166-12, implementing the J2727 Score Term 

The J2727 score is the SAE J2727 yearly leakage estimate of the A/C system as 

calculated according to the J2727 procedure. In the time since the MYs 2012-2016 LightDuty GHG Rule, there have been several refinements to the J2727 procedure which EPA has 

incorporated into the EPA regulations. First, a provision was made for system joints where 

100 percent of the joints are leak test with helium and a mass-spectrometer leak detector. If 

the joints pass this leak test, they can be considered to have a leakage factor equivalent to that 

of a seal washer, which is next to the lowest factor possible for system joints. Second, a 

requirement was added to use SAE J2064 hose permeation test results in place of the discrete 

values for various hose material and construction types that were provided in previous 

versions of the J2727 test method. By using the test chamber results for refrigerant 

permeation through hoses, a more representative leakage estimate for the overall system is 

achieved. The minimum J2727 score for cars and trucks is a fixed value, and the section 

below describes the derivation of the minimum leakage scores that can be achieved using the 

J2727 procedure. 

To calculate a J2727 score and credit for the alternative refrigerant HFO-1234yf, all 

values relevant to the credit calculations, as well as the J2727 score, shall be adjusted to 

account for the higher molecular weight of this refrigerant. In contrast to the studies 

discussed in section 5.1.2.6 which examines the HFC emission rate of the in-use fleet (which 

includes vehicles at all stages of life), the SAE J2727 estimates leakage from new vehicles. In 

the development of J2727, two relevant studies were assessed to quantify new vehicle 

emission rates. In the first study, measurements from relatively new (properly functioning 

and manufactured) Japanese-market vehicles were collected. This study was based on 78 inuse vehicles (56 single evap, 22 dual evap) from 7 Japanese auto makers driven in Tokyo and 

Nagoya from April, 2004 to December, 2005. The study also measured a higher emissions 

level of 16 g/yr for 26 vehicles in a hotter climate (Okinawa). This study indicated the 

leakage rate to be close to 8.6 g/yr for single evaporator systems and 13.3 g/yr for dual 

evaporator systems.15

 A weighted (test) average gives 9.9 g/yr. In the second study, 

emissions were measured on European-market vehicles up to seven years age driven from 

November, 2002 to January, 2003.16 The European vehicle emission rates were slightly 

higher than the Japanese fleet, but overall, they were consistent. The average emission rate 

from this analysis is 17.0 g/yr with a standard deviation of 4.4 g/yr. European vehicles, 

because they have smaller charge sizes, likely understate the leakage rate relative to the 

United States. To these emission rates, the J2727 authors added a factor to account for 

occasional defective parts and/or improper assembly and to calibrate the result of the SAE 

J2727 calculation with the leakage measured in the vehicle and component leakage studies. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-13 

We adjust this rate up slightly by a factor proportional to the average European 

refrigerant charge to the average United States charge (i.e. 770/747 from the Vintaging model 

and Schwarz studies respectively). The newer vehicle emission rate is thus 18 g/yr for the 

average newer vehicle emissions (average for car and truck). 

To derive the minimum score, the 18 gram per year rate was used as a ratio to convert 

the gram per mile emission impact into a new vehicle gram per year for the test. The car or 

truck direct a/c emission factor (gram per mile) was divided by the average emission factor 

(gram per mile) and then multiplied by the new vehicle average leakage rate (gram per year) 

Equation 5-3 – J2727 Minimum Score 

J2727 Minimum Score = Car or truck average pre control emissions (gram per 

mile)/ Fleet average pre-control emissions (grams per mile) x New vehicle annual 

leakage rate (grams per year) x Minimum Fraction

By applying this equation, the minimum J2727 score is fixed at 8.3 g/yr for cars and 

10.4 g/yr for trucks. This corresponds to a total fleet average of 18 grams per year, with a 

maximum reduction fraction of 50%. 

The GWP Refrigerant term in Equation 1 allows for the accounting of refrigerants 

with lower GWP (so that this term can be as low as zero in the equation), which is why the 

same minimum score is kept regardless of refrigerant used. It is technically feasible for the 

J2727 Minimum score to be less than the values presented in the table. But this will usually 

require the use of an electric compressor (see below for technology description). 

5.1.2.3.2.3 AvgImpact Term 

AvgImpact is the average annual impact of A/C leakage, which is 16.6 and 20.7 g/yr 

for cars and trucks respectively. This was derived using Equation 2, but by setting the 

minimum fraction to one. 

5.1.2.3.2.4 GWPRefrigerant Term 

This term is relates to the global warming potential (GWP) of the refrigerant as 

documented by EPA. A full discussion of GWP and its derivation is too lengthy for this 

space, but can be found in many EPA documents.4c This term is used to correct for 

refrigerants with global warming potentials that differ from HFC-134a. 

5.1.2.3.2.5 HiLeakDisincentive 

As proposed, EPA is adding (compared to the MYs 2012-2016 rule’s formula) a 

disincentive to the leakage credit formula for systems which use a low GWP refrigerant, but 

“backslide” on low leakage levels. As stated above, low leakage levels provide an 

environmental benefit by maintaining the charge of the system. This has two advantageous 

effects. First, it preserves the efficiency of the system. Reduced refrigerant charge levels can 

reduce overall efficiency, especially if the compressor starts “short-cycling”. Also, since 

lubrication is combined with most of the current and likely future refrigerants, the shortage of 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-14 

lubrication can wear out the compressor and cause it to seize and malfunction. CARB testing 

has shown that preserving the refrigerant charge level in a conventional A/C system results in 

improved system efficiency.17 Second, by reducing the leak rate of the low GWP system, the 

probability that the new system will run out of charge will be minimized. When a system 

runs out of charge, vehicle owners can either drive without A/C, or have a professional 

recharge the system, or recharge the system themselves. The latter are called “do-it-yourselfers” (DIYers). It is possible that DIYers (and some repair shops) may refill a low-GWP 

system (e.g., HFO-1234yf) with a high-GWP refrigerant (e.g., HFC-134a), in order to save on 

costs.h Due to the demand from the legacy fleet, refill containers of HFC-134a would be 

available to the market for many years to come (so it would be available to DIYers). Since 

the thermodynamic properties of HFC-134a and HFO-1234yf are similar, HFO-1234yf 

systems may function with HFC-134a, although with some reduced effectiveness, and in 

some systems may lead to long term damage.i Unfortunately, the extent to which this will 

occur is difficult to predict. EPA regulations prohibit topping-off a system with a refrigerant 

other than the original (for which the system was designed). EPA will use this disincentive 

credit to maintain low refrigerant leakage emission levels and to reduce the potential for 

leakage of high GWP refrigerants from systems that have been improperly recharged. Thus, 

EPA believes that there are real, but unquantifiable, benefits for a leakage disincentive credit, 

and we are finalizing a (Max)HiLeakDisincentive of 1.8 g/mi for cars and 2.1 g/mi for trucks. 

The EPA believes that these numbers strike a balance in that it is a large enough incentive to 

maintain low leakage levels, but it is not too large as to diminish incentive to switch to an 

alternative refrigerant. 

The leakage rate at which a disincentive, or negative credit, would be generated 

(MinScore) was increased from the levels proposed in the NPRM. Most commenters 

requested that the EPA remove this negative credit for the final rule. Other commenters 

believed that the 8.3 g/yr and 10.4 g/yr thresholds for cars and trucks that we proposed were 

overly stringent, and that a more realistic value should be specified, based what the current 

fleet of vehicles is achieving. In response to these comments, we examined the approach we 

used in the MYs 2014-2018 rule for GHG Emissions Standards for Heavy-Duty Engines and 

Vehicles, where a maximum refrigerant leak rate standard of 11.0 g/year was set for vehicles 

with a refrigerant capacity of 733 g or less, and 1.5 percent of the system refrigerant capacity 

for vehicles with a refrigerant capacity greater than 733 g. We believe that the approach used 

in the heavy-duty GHG rule is appropriate for setting the threshold for which a 

HiLeakDisincentive (a “negative” credit) is generated in the MYs 2017-2025 light-duty GHG 

rule, as the air conditioning systems in both categories are similar, in terms of the components 

and materials used, as well as general system design and layout. Furthermore, analysis of the 

2012 model year J2727-based leakage rate data in the State of Minnesota reporting database 

affirms that this approach will require leakage reductions in a large portion of the vehicle 

 

h

 Refilling/ topping-off systems designed for use for one refrigerant (e.g., HFO-1234yf) with another refrigerant 

(e.g., HFC-134a) is a violation of CAA Section 612 regulations. See 40 CFR part 82, subpart G, appendix D, 

section 3. 

i

 Based on discussions with vehicle manufacturers. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-15 

fleet, while allowing early adopters of low-leakage technologies to avoid the disincentive. In 

addition, this scaled approach, where larger A/C systems with higher refrigerant capacities 

can have a higher leakage rate before triggering the HiLeakDisincentive, provides 

manufacturers with the flexibility they need to install appropriately-sized A/C systems in 

larger vehicles, while using common low-leak technologies and components across their 

vehicle models. If a single threshold were applied to cars and trucks, extraordinary leakage 

mitigation measures would likely be necessary on larger systems in order to avoid the 

disincentive. Figure 5-1 illustrates how this approach to the HiLeakDisincentive fits within 

the 2012 model leakage scores for light-duty vehicles. 

Figure 5-1 2012 State of Minnesota Leakage Reporting Data (All Vehicles) with HiLeakDis Thresholds

5.1.2.3.3 Why are the leakage credits different from the 2010 Technical Assessment 

Report? 

The 2010 Technical Assessment report employed a different methodology for 

calculating the HFC credit, which resulted in significantly fewer credits available for A/C 

leakage compared to the MYs 2012-2016 final rule (approximately 40% less). The TAR 

analysis decreased the average charge size and leakage assumed in its analysis of future model 

years as compared to the MYs 2012-2016 final rule. In the present rule, we maintain the MYs 

2012-2016 credit value. EPA chose this approach for both technical and policy reasons. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 J2727 Score (g/yr)

Refrigerant Charge Size (g)

2012 MN Data

2017-2025 

HiLeakDisincentiv

e Min. Threshold

2017-2025 

HiLeakDisincentiv

e Max. Threshold
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-16 

 Like any inventory, the refrigerant inventory produced by the Vintaging model has 

uncertainties associated with it. This is especially true given that we do not know how many 

“high emitters” exist in the U.S. fleet. A high emitter is a vehicle that rapidly leaks HFC, but is 

also continually recharged. A typical light duty vehicle may require recharge approximately 

every seven years (see section 5.1.2.6). However, the owner of a high emitter may continually 

charge their systems each summer, thereby increasing the overall average emissions of the fleet. 

In the 2009-2010 study of the leakage rates from 70 in-use heavy duty vehicles, the California Air 

Resources Board found a relatively high prevalence of high emitting vehicles. Of the 70, 5 had 

leakage rates that were greater than one-half a charge per year, while seven additional vehicles 

had annualized leakage rates greater than one-quarter charge per year.j

 These values could 

potentially be used to recalculate the HFC inventories from the TAR and recalculate the leakage 

credit. 

EPA considered the lower inventory discussed in the TAR as well as the CARB study 

when determining the leakage credit for this rule. While there is ultimately a mass balance 

between HFC produced and HFC leaked, this balance is not closed on an annual basis, and is 

difficult to directly verify. Given the counterbalancing factors, EPA made the policy decision to 

maintain continuity with the MYs 2012-2016 FRM analysis, and will incorporate this level of the 

credit in the standard setting process. A reduction in A/C credits (in 2017 compared to 2016 for 

example) would artificially increase the stringency of the standard for those manufacturers who 

generated leakage (and alternative refrigerant) credits in 2016 as a means of compliance. With 

little lead time, these manufacturers would need to add other technologies to their fleet in order to 

close the gap their compliance target created by a reduction in the maximum potential A/C credit. 

Alternately, the stringency of the 2017 standards would have to be relaxed, and in some cases may 

even be less stringent than 2016 standards if this adjustment is made. 

ICCT expressed the concern that maintaining continuity with the earlier rule would 

encourage manufacturers to seek to generate leakage credits more aggressively than they 

otherwise would. As we stated above, we acknowledge that some manufacturers might choose a 

slightly different technology approach to compliance if fewer A/C leakage credits were available. 

However, we believe that the disruption to the transition from the MYs 2012-2016 rule to the 

MYs 2017-2025 rule that would result is not acceptable. Given the need for stability for the 

standards (and stringencies), EPA is “freezing” the credit assessment based on what we presented 

in the MYs 2012-2016 rule, and also presented again above. 

5.1.2.4 Technologies That Reduce Refrigerant Leakage and their Effectiveness 

In this section, the analysis used in the MYs 2012-2016 rule is again applied to the 

baseline technology levels and the effectiveness for leakage-reducing technologies. For the 

MYs 2012-2016 rule, EPA conducted an analysis to determine the historic leakage emission 

rate for motor vehicle A/C systems, and it was estimated in section 5.1.2.3.2.2 that the A/C 

systems in new vehicles would leak refrigerant at an average rate of 18 g/yr – a value which 

EPA believes represented the types of A/C components and technologies in use prior to MY 

 

j

 While the Vintaging model assumes an average annualized leakage rate of 18% + 43% at end of life, it assumes 

that the MAC unit only lasts 12 years. Actual MACs, particularly those that are recharged, may last longer. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-17 

2007. EPA believes, through utilization of the leakage-reducing technologies described 

below, that it will be possible for manufacturers to reduce refrigerant leakage 50%, relative to 

the 18 g/yr baseline level.18 EPA also believes that all of these leakage-reducing technologies 

are currently available, and that manufacturers will use these technologies to generate credits 

under provisions of the 2012-2016 rule, as well as under the provisions of this rule. 

In describing the technologies below, only the relative effectiveness figures are 

presented, as the individual piece costs are not known. The EPA only has costs of complete 

systems based on the literature, and the individual technologies are described below. 

5.1.2.4.1 Baseline Technologies 

The baseline technologies assumed for A/C systems which have an average annual 

leak rate of 18 g/yr are common to many mass-produced vehicles in the United States. In 

these mass-produced vehicles, the need to maintain A/C system integrity (and the need to 

avoid the customer inconvenience of having their A/C system serviced due to loss of 

refrigerant) is often balanced against the cost of the individual A/C components. For 

manufacturers seeking improved system reliability, components and technologies which 

reduce leakage (and possibly increased cost) are selected, whereas other manufacturers may 

choose to emphasize lower system cost over reliability, and choose components or 

technologies prone to increased leakage. In EPA’s baseline scenario, the following 

assumptions were made concerning the definition of a baseline A/C system: 

all flexible hose material is rubber, without leakage-reducing barriers or veneers, of 

approximately 650 mm in length for both the high and low pressure lines 

all system fittings and connections are sealed with a single o-rings 

the compressor shaft seal is a single-lip design 

one access port each on the high and low pressure lines 

two of the following components: pressure switch, pressure relief valves, or pressure 

transducer 

one thermostatic expansion valve (TXV) 

The design assumptions of EPA baseline scenario are also similar to the sample 

worksheet included in SAE’s surface vehicle standard J2727 – (R) HFC-134a Mobile Air 

Conditioning System Refrigerant Emission Chart.10 In the J2727 emission chart, it is the 

baseline technologies which are assigned the highest leakage rates, and the inclusion of 

improved components and technologies in an A/C system will reduce this annual leakage rate, 

as a function of their effectiveness relative to the baseline. EPA considers these ‘baseline’ 

technologies to be representative of recent model year vehicles, which, on average, can 

experience a refrigerant loss of 18 g/yr. However, depending on the design of a particular 

vehicle’s A/C system (e.g. materials, length of flexible hoses, number of fittings and adaptor 

plates, etc.), it is possible to achieve a leakage score much higher (i.e. worse) than 18 g/yr. 

According to manufacturer data submitted to the State of Minnesota, 19% of 2009 model year 

vehicles have a J2727 refrigerant score greater than 18 g/yr, with the highest-scoring vehicle 

reporting a leakage rate of 30.1 g/yr.19

 For the 2010 model year, the average J2727 leakage 

score reporting database was 14.0 g/yr for cars, and 14.8 g/yr for trucks, but this is simply the 

average result of all vehicles in the database, and does not reflect sales weighting of the 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-18 

leakage scores nor does it eliminate identical models (vehicles with different brands or 

nameplates, but identical with respect to the A/C system design and components) when 

calculating the average score. 

Here again, the 18g/yr baseline is maintained at the MYs 2012-2016 rule levels for 

both technical and policy reasons. As mentioned earlier, there is great uncertainty in the 

leakage emissions from vehicles. The J2727 scoring system, which is calibrated to in-use 

emissions from properly functioning vehicles, does not include high emitters. EPA considers 

J2727 to be a surrogate for in-use emissions, and not necessarily an accurate representation of 

real-world emissions. Thus, to maintain continuity with MY 2016 standards (and credits), 

EPA is “freezing” the baseline assumption of leakage rate from the fleet. 

5.1.2.4.2 Flexible Hoses 

The flexible hoses on an automotive A/C system are needed to isolate the system from 

engine vibration and to allow for the engine to roll within its mounts as the vehicle accelerates 

and decelerates. Since the compressor is typically mounted to the engine, the lines going toand-from the compressor (i.e. the suction and pressure lines) must be flexible, or unwanted 

vibration would be transferred to the body of the vehicle (or other components), and excessive 

strain on the lines would result. It has been industry practice for many years to manufacture 

these hoses from rubber, which is relatively inexpensive and durable. However, rubber hoses 

are not impermeable, and refrigerant gases will eventually migrate into the atmosphere. To 

reduce permeation, two alternative hose material can be specified. The first material, is 

known as a standard ‘veneer’ (or ‘barrier’) hose, where a polyamide (polymer) layer - which 

has lower permeability than rubber - is encased by a rubber hose. The barrier hose is similar 

to a veneer hose, except that an additional layer of rubber is added inside the polyamide layer, 

creating three-layer hose (rubber-polyamide-rubber). The second material is known as ‘ultralow permeation’, and can be used in a veneer or barrier hose design. This ultra-low 

permeation hose is the most effective at reducing permeation, followed by the standard veneer 

or barrier hose. Permeation is most prevalent during high pressure conditions, thus it is even 

more important that these low permeable hoses are employed on the high pressure side, more 

so than on the low pressure side. 

According to J2727, standard barrier veneer hoses have 25% the permeation rate of 

rubber hose, and ultra low permeable barrier veneer hoses have 10% the permeation rate (as 

compared to a standard baseline rubber hose of the same length and diameter). However, in 

the February 2012 version of J2727, manufacturers are required to use actual SAE J2064 hose 

permeation data, instead of the discrete values provided for various hose material and 

construction types, as was specified in previous versions of the J2727 method. 

5.1.2.4.3 System Fittings and Connections 

Within an automotive A/C system and the various components it contains (e.g. 

expansion valves, hoses, rigid lines, compressors, accumulators, heat exchangers, etc.), it is 

necessary that there be an interface, or connection, between these components. These 

interfaces may exist for design, manufacturing, assembly, or serviceability reasons, but all 

A/C systems have them to some degree, and each interface is a potential path for refrigerant 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-19 

leakage to the atmosphere. In SAE J2727 emission chart, these interfaces are described as 

fittings and connections, and each type of fitting or connection type is assigned an emission 

value based on its leakage potential; with a single O-ring (the baseline technology) having the 

highest leak potential; and a metal gasket having the lowest. In between these two extremes, 

a variety of sealing technologies, such as multiple o-rings, seal washers, and seal washers with 

o-rings, are available to manufacturers for the purpose of reducing leakage. It is expected that 

manufacturers will choose from among these sealing technology options to create an A/C 

system which offers the best cost-vs-leakage rate trade-off for their products. 

The relative effectiveness of the fitting and connector technology is presented in Table 

5-4. For example, the relative leakage factor of 125 for the baseline single O-ring is 125 

times more “leaky” than the best technology - the metal gasket. 

Table 5-4 Effectiveness of Fitting and Connector Technology 

Fitting or Connector Relative 

Leakage

Single O-ring 125

Single Captured O-ring 75

Multiple O-ring 50

Seal Washer 10

Seal Washer with O-ring 5

Metal Gasket 1

100% Helium Leak Test 10

5.1.2.4.4 Compressor Shaft Seal 

A major source of refrigerant leakage in automotive A/C systems is the compressor 

shaft seal. This seal is needed to prevent pressurized refrigerant gasses from escaping the 

compressor housing. As the load on the A/C system increases, the pressure and the leakage 

past the seal also increase. In addition, with a belt-driven A/C compressor, a side load is 

placed on the compressor shaft by the belt, which can cause the shaft to deflect slightly. The 

compressor shaft seal must have adequate flexibility to compensate for this deflection, or 

movement, of the compressor shaft to ensure that the high-pressure refrigerant does not leak 

past the seal lip and into the atmosphere. When a compressor is static (not running), not only 

are the system pressures lower, the only side load on the compressor shaft is that from tension 

on the belt, and leakage past the compressor shaft is at a minimum. However, when the 

compressor is running, the system pressure is higher and the side load on the compressor shaft 

is higher (i.e. the side load is proportional to the power required to turn the compressor shaft) 

- both of which can increase refrigerant leakage past the compressor shaft seal. It is estimated 

that the rate of refrigerant leakage when a compressor is running can be 20 times that of a 

static condition.20 Due to the higher leakage rate under running conditions, SAE J2727 

assigns a higher level of impact to the compressor shaft seal. In the example shown in the 

August 2008 version of the J2727 document, the compressor is responsible for 58% of the 

system refrigerant leakage, and of that 58%, over half of that leakage is due to the shaft seal 

alone (the remainder comes from compressor housing and adaptor plate seals). To address 

refrigerant leakage past the compressor shaft, manufacturers can use multiple-lip seals in 

place of the single-lip seals. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-20 

5.1.2.5 Technical Feasibility of Leakage-Reducing Technologies 

EPA believes that the leakage-reducing technologies discussed in the previous 

sections are available to manufacturers today, are relatively low in cost, and that their 

feasibility and effectiveness have been demonstrated by the SAE IMAC teams. EPA also 

believes – as has been demonstrated in the J2727 calculations submitted by manufacturers to 

the State of Minnesota – that reductions in leakage from 18 g/yr to 9 g/yr are possible (e.g. the 

2009 Saturn Vue has a reported leakage score of 8.5 g/yr). In addition to generating credit for 

reduced refrigerant leakage, we expect many manufacturers to choose to introduce alternative 

refrigerant systems, such as HFO-1234yf, as discussed in Section III.C.1 of the preamble to 

this rule. 

5.1.2.6 Deterioration in Leakage Controls 

In the MYs 2012-2016 rule as well as in the proposal for this rule, EPA presented a 

“model” of the deterioration of leakage systems. This analysis would have been necessary if 

EPA wanted to quantify the maintenance benefits of leakage control. EPA received no 

comments on this model for the proposal. For this final rule, EPA is not claiming 

maintenance cost savings due to refrigerant leakage regulations. This is due to uncertainty as 

to how these low-leak technologies will actually perform and deteriorate in use. While we 

expect that low-leak systems will have a lower probability of a requiring a recharge 

maintenance event, or at least a longer period before such an event is required, it is difficult to 

quantify such factors, and such quantification is necessary for their inclusion in a cost-benefit 

analysis. Moreover, EPA is estimating that the predominant technology in the MYs 2017-

2025 timeframe will be alternative refrigerantsk, thus minimizing the need for this 

deterioration model. 

Despite the fact that we are not using the deterioration model (as proposed), EPA 

believes that it is important to address the issue as it relates to the hi-leak disincentive. Since 

the deterioration model was presented, EPA has reconsidered some of the assumptions that 

went into the model. Given that the deterioration mechanisms are not fully understood and 

quantified, it is difficult to project the precise rate at which leak-reducing A/C technologies 

will deteriorate compared to conventional technologies. But we do know that even if a 

similar rate of deterioration is assumed, the A/C system with a lower initial leakage rate will 

have a lower frequency of required recharge events over its lifetime, as it will take the lowleak vehicle longer to reach a level of 50 percent charge remaining in the system. In addition, 

we believe that many of the leak-reducing technologies, such as use of seal washers in place 

of O-rings, and 100 percent leak testing of assembled components with helium, are inherently 

beneficial to system durability. In the case of seal washers, a rigid, metal connection is 

created at system joints instead of a less-rigid o-ring seal, which can be susceptible to damage 

upon installation. In the case of helium leak testing, any defective joints, connections, or 

components are detected prior to installation in the vehicle, which reduces the probability of 

 

k

 Though we are encouraging manufacturers to keep the leakage scores low with the hi leak disincentive. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-21 

vehicles with higher-than-expected leakage leaving the assembly plant. EPA believes that 

establishing an incentive to achieve low-leak systems through a HiLeakDisincentive (see 

5.1.2.3.2.5) will result in lower deterioration rates and extend the interval for which a system 

recharge is required. For these reasons, EPA believes that the deterioration model presented 

in the proposal was potentially overly “conservative”. EPA will continue to monitor data in 

the future on the issue of leakage deterioration and the effects of the hi-leak disincentive. 

5.1.2.7 Other Benefits of Improving A/C Leakage Performance 

The EPA is assuming that a reduction in leakage emissions from new vehicles will 

also improve the leakage over the lifetime of the vehicle. There is ample evidence to show 

that A/C systems that leak more also have other problems that occur (especially with the 

compressor) due to the lack of oil circulating in the system. Thus, it is expected that an A/C 

system which utilizes leak-reducing components and technologies should, on average, last 

longer than one which does not. 

A European study conducted in 2001 (by Schwarz) found that the condenser is the 

component most likely to fail and result in a total leak.21

 The study also found that 

compressor component was most likely the culprit when other malfunctions were present 

(other than total loss). A more recent (and larger) study found that condensers required 

replacement at half the rate of a compressor (10% vs 19% of the entire part replacement rate), 

and that evaporators and accumulators failed more often.16 The same study also found that 

many of the repairs occurred when the vehicles were aged 5-10 years. Both these studies 

indicate that the condenser and compressor are among the major causes of failure in an A/C 

system. Leakage reductions in the system are expected to greatly reduce the incidence of 

compressor repair, since one of the main root causes of compressor failure is a shortage of 

lubricating oil, which originates from a shortage of refrigerant flowing through the system 

(and it is a refrigerant-oil mixture which carries lubricating oil to the compressor).22

Monitoring of refrigerant volume throughout the life of the A/C system may provide 

an opportunity to circumvent some previously described failures specifically related to 

refrigerant loss. Similar to approaches used today by the engine on-board diagnostic systems 

(OBD) to monitor engine emissions, a monitoring system that informed the vehicle operator 

of a low refrigerant level could potentially result in significant reductions in A/C refrigerant 

emissions due to component failure(s) by creating an opportunity for early repair actions. 

While most A/C systems contain sensors capable of detecting the low refrigerant pressures 

which result from significant refrigerant loss, these systems are generally not designed to 

inform the vehicle operator of the refrigerant loss, and that further operation of the unrepaired 

system can result in additional component damage (e.g. compressor failure). Electronic 

monitoring of the refrigerant may be achieved by using a combination of existing A/C system 

sensors and new software designed to detect refrigerant loss before it progresses to a level 

where component failure is likely to occur. 

EPA requested comment in the 2012-2016 NPRM on allowing additional leakage 

credits for systems that monitor the leak levels, especially where manufacturers are willing to 

warrant such systems. Presently, the EPA is not aware if any such technology exists to 

accurately monitor refrigerant levels, as the technical challenges are high. As a result, there 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-22 

were no manufacturers who expressed interest in this credit, and the EPA did not finalize such 

credits in the 2012-2016 program. EPA again sought comment during the MY 2017-2025 

proposal on allowing these credits again, in the hopes of encouraging innovative technologies 

to monitor leakage levels. ICCT supported EPA request for monitoring technology in their 

comments, however, no manufacturer (or supplier) provided comment on this issue and EPA 

remains unaware of any such technology in existence (much less in implementation). 

5.1.3 CO2 Emissions and Fuel Consumption due to Air Conditioners 

As stated above, for model years 2012 to 2016, EPA provided credits for the use of 

A/C technologies that improve efficiency and achieve reductions in indirect CO2 emissions 

related to A/C use. These credits were not previously applicable to the CAFE program fuel 

economy calculations. For this rule, the agencies are finalizing provisions that the A/C 

indirect credits are applicable to both the greenhouse gas and fuel economy calculations. 

5.1.3.1 Impact of Air Conditioning Use on Fuel Consumption and CO2 Emissions 

Three studies have been performed in recent years which estimate the impact of A/C 

use on the fuel consumption of motor vehicles in the United States. In the first study, the 

National Renewable Energy Laboratory (NREL) and the Office of Atmospheric Programs 

(OAP) within EPA have performed a series of A/C related fuel use studies.23,24

 The energy 

needed to operate the A/C compressor under a range of load and ambient conditions was 

based on testing performed by Delphi, an A/C system supplier. They used a vehicle 

simulation model, ADVISOR, to convert these loads to fuel use over the EPA’s FTP test 

cycle. They developed a personal “thermal comfort”-based model to predict the percentage of 

drivers which will turn on their A/C systems under various ambient conditions. Overall, 

NREL estimated A/C use to represent 5.5% of car and light truck fuel consumption in the 

U.S. 

In the second study, the California Air Resources Board (CARB) estimated the impact 

of A/C use on fuel consumption as part of their GHG emission rulemaking.25 The primary 

technical analysis utilized by ARB is summarized in a report published by NESCCAF for 

CARB. The bulk of the technical work was performed by two contractors: AVL Powertrain 

Engineering and Meszler Engineering Services. This work is founded on that performed by 

NREL-OAP. Meszler used the same Delphi testing to estimate the load of the A/C 

compressor at typical ambient conditions. The impact of this load on onroad fuel 

consumption was estimated using a vehicle simulation model developed by AVL - the 

CRUISE model - which is more sophisticated than ADVISOR. These estimates were made 

for both the EPA FTP and HFET test cycles. (This is the combination of test cycle results 

used to determine compliance with NHTSA’s current CAFE standards.) NREL’s thermal 

comfort model was used to predict A/C system use in various states and seasons. 

The NESCCAF results were taken from Table 3-1 of their report and are summarized 

in Table 5-5.

26

Table 5-5 CO2 Emissions Over 55/45 FTP/HFET Tests and From A/C Use (g/mi) Based on the NESCCAF 

study 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-23 

Small Car Large Car Minivan Small Truck Large Truck

55/45 FTP/HFET 278 329 376 426 493

Indirect A/C 

Fuel Use (g/mi CO2) 16.8 19.1 23.5 23.5 23.5

Total 294.8 348.1 399.5 449.5 516.5

Indirect A/C 

Fuel Use 5.7% 5.5% 5.9% 5.2% 4.6%

NESCCAF estimated that nationwide, the average impact of A/C use on vehicle fuel 

consumption ranged from 4.6% for a large truck or SUV, to 5.9% for a minivan. The impact 

on vehicle CO2 and fuel consumption resulting from A/C use was determined using a 

55%/45% weighting of CO2 emissions from EPA FTP and HFET tests (hereafter referred to 

simply as FTP/HFET). Simulation modeling to assess A/C related fuel consumption and CO2

emissions was first conducted without the load from the A/C system followed by modeling 

which included the load from the A/C system. For the purposes of this analysis of A/C system 

fuel use, the percentage of CO2 emissions and fuel use are equivalent, since the type of fuel 

being used is always gasoline.l

In the MYs 2012-2016 rule, there was a third analysis presented along with a thorough 

comparison of these studies. While not repeated here, it was estimated that the impact of A/C 

on onroad fuel consumption was 3.9% based on a combination of the results from these 

studies. This resulted in an average impact of 14.3g/mi (independent of car or truck type) and 

hence a maximum of 5.7 g/mi credit, identical for car and truck (based on a 40% 

improvement feasibility). For this rule, EPA has conducted a new analysis, which supports 

the results achieved in the MYs 2012-2016 final rule, though there is now a distinction made 

between cars and trucks as it relates to A/C efficiency impacts (and credits). 

5.1.3.2 Updated Analysis of Efficiency Impacts 

As just mentioned, in the Light-Duty GHG final rule for model years 2012 through 

2016, EPA estimated that the average CO2 emission increase due to A/C use would be 14.3 

g/mi taking into account both manual and automatic climate control systems with market 

penetrations of 62% and 38%, respectively. For this study of the A/C compressor load impact 

on vehicle fuel economy, EPA relied on comparisons of measured fuel economy over two 

warmed up bags (or phases) of the FTP test (without A/C operating) and the SC03 test (A/C 

emissions test). EPA had based its estimates on testing of over 600 production vehicles. 

These test results were combined with the Phoenix study, where the A/C compressor on-time 

was estimated to be 23.9% for manual climate control systems and 35% for automatic climate 

control systems. For more technical details, one can refer to the Regulatory Impact Analysis 

for the model year 2012 to 2016 final rule. 

 

l

 Because NESCCAF estimated A/C fuel use nationwide, while ARB focused on that in California, the 

NESCCAF and EPA methodologies and results are compared below. 
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5-24 

For the proposed and final rule, EPA developed a more robust and systematic method 

of estimating vehicle CO2 emissions related to A/C usage. This method is based on a 

sophisticated, newly-developed EPA vehicle simulation tool. The next few paragraphs 

provide an overview of the vehicle simulation tool and describe how this approach improves 

on the earlier analysis. More detailed descriptions about the vehicle simulation tool and its 

use for the A/C indirect impact analysis are in Chapter 2 of the EPA Regulatory Impact 

Analysis. 

Over the past year, EPA has developed full vehicle simulation capabilities in order to 

support regulations and vehicle compliance by quantifying the effectiveness of different 

technologies with scientific rigor over a wide range of engine and vehicle operating 

conditions. This in-house simulation tool has been developed for modeling a wide variety of 

light, medium, and heavy duty vehicle applications over various driving cycles. In order to 

ensure transparency of the models and free public access, EPA has developed the tool in 

MATLAB/Simulink environment with the completely open source code. To support these 

simulation capabilities in part, EPA is upgrading its testing infrastructure (such as engine test 

cells, vehicle dynamometers, Portable Emissions Measurement Systems, and a battery 

laboratory) at the National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan. 

This testing infrastructure provides necessary data to calibrate and validate vehicle 

simulations, such as engine fuel maps, engine torque maps, vehicle aerodynamic parameters, 

battery, electrical component parameters, etc. 

EPA’s first application of the vehicle simulation tool was for purposes of heavy-duty 

vehicle compliance and certification. For the model years 2014 to 2018 final rule for medium 

and heavy duty trucks, EPA created the “Greenhouse gas Emissions Model” (GEM), which is 

used both to assess Class 2b-8 vocational vehicle and Class 7/8 combination tractor GHG 

emissions and fuel efficiency and to demonstrate compliance with the vocational vehicle and 

combination tractor standards. See 40 CFR sections 1037.520 and 1037.810. This GEM 

documentation is also currently in publication.27

For light-duty vehicles, EPA has developed a conventional (non-hybrid) vehicle 

simulation tool and used it to estimate indirect A/C CO2 emissions. These estimates are used, 

in turn, to quantify the maximum amount of indirect A/C credit (i.e. the maximum credit 

potential). As mentioned previously, the tool is based on MATLAB/Simulink and is a 

forward-looking full vehicle model that uses the same physical principles as other 

commercially available vehicle simulation tools (e.g. Autonomie, AVL-CRUISE, GT-Drive, 

etc.) to derive the governing equations. These governing equations describe steady-state and

transient behaviors of each electrical, engine, transmission, driveline, and vehicle systems, 

and they are integrated together to provide overall system behavior during transient conditions 

as well as steady-state operations. Chapter 2 of EPA’s Regulatory Impact Analysis provides 

more details on this light-duty vehicle simulation tool used for estimating indirect A/C impact 

on fuel consumption. 

In the light-duty vehicle simulation tool, there are four key system elements that 

describe the overall vehicle dynamics behavior and the corresponding fuel efficiency: 

electrical, engine, transmission, and vehicle. The electrical system model consists of parasitic 

electrical load and A/C blower fan, both of which were assumed to be constant. The engine 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-25 

system model is comprised of engine torque and fueling maps. For estimating indirect A/C 

impact on fuel consumption increase, two engine maps were used: baseline and EGR boost 

engines. These engine maps were obtained by reverse-engineering the vehicle simulation 

results provided by Ricardo Inc. For the transmission system, a Dual-Clutch Transmission 

(DCT) model was created and used along with the gear ratios and shifting schedules used for 

the earlier Ricardo simulation work. For the vehicle system, four vehicles were modeled: 

small, mid, large size passenger vehicles, and a light-duty pick-up truck. The transient 

behavior and thermodynamic properties of the A/C system was not explicitly simulated, in 

favor of a simpler approach of capturing the compressor load based on national average 

ambient conditions. We believe this simplification is justified since the goal is to capture the 

behavior on the average of a fleet of vehicles (not the behavior of an individual make or 

model). 

In order to properly represent average load values to the engine caused by various A/C 

compressors and vehicle types, EPA has adopted power consumption curves of A/C systems, 

published by an A/C equipment supplier, Delphi.28,29

 Also, in an effort to characterize an 

average A/C compressor load in the presence of widely varying environmental conditions in 

the United States, EPA has adopted data from the National Renewable Energy Laboratory 

(NREL) to estimate environmental conditions associated with typical vehicle A/C 

usage.30,31,32

 Based on the NREL data, EPA selected an A/C power consumption curve as a 

function of engine speed that was acquired by Delphi at 27 C and 60% relative humidity as a 

representative average condition. This power consumption curve data was taken from a fixed 

displacement compressor with a displacement volume of 210 cc. Thus, the curve includes the 

effect of compressor cycling as well as non-summer defrost/defog usage. In order to associate 

each vehicle type with the appropriate A/C compressor displacement, EPA scaled the curve 

based on the displacement volume ratio. For determining indirect A/C impact on fuel 

consumption increase, EPA estimated A/C compressor sizes of 120 cc, 140 cc, 160 cc, and 

190 cc for small, medium, large passenger vehicles, and light-duty pick-up truck, respectively. 

By applying the displacement volume ratios to the 210 cc power consumption curve, EPA 

created A/C load curves for four vehicle types, as shown in Figure 5-2. 

Figure 5-2 Representative A/C Compressor Load Curves 
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5-26 

With these A/C compressor load curves, EPA ran full vehicle simulations based on the 

following matrix. In this matrix, the baseline engine represents a typical Spark-Ignition (SI), 

Port-Fuel Injection (PFI), Naturally-Aspirated (NA) engine equipped with a Variable Value 

Actuation (VVA) technology. In this technology, the valve timing (both intake and exhaust) 

is continuously varied over a wide range of engine operating conditions in order to result in 

optimal engine breathing efficiency. On the other hand, the EGR boost engine uses 

turbocharging and cooled EGR to increase engine’s Brake Mean Effective Pressure (BMEP) 

level while managing combustion and exhaust temperatures. This engine usually has a peak 

BMEP of 25 to 30 bar, which supports significant downsizing (e.g. about 50%) compared to 

the baseline engines. Table 5-6 provides simulation results over SC03 driving cycle with an 

EGR boost engine for various vehicle classes. 

Small, medium, large cars, and pick-up truck 

FTP, Highway, and SC03 cycles 

Baseline and EGR boost engines 

A/C off and A/C on 

Table 5-6 Vehicle Simulation Results on CO2 Emissions over SC03 Cycle with EGR Boost Engine 

SC03 Cycle Small Car Medium Car Large Car Truck

CO2 with A/C off [g/mi] 196.4 235.7 293.7 472.4

CO2 Increase with A/C on [g/mi] 11.7 12.0 13.8 17.2

Total CO2 with A/C [g/mi] 208.1 247.7 307.5 489.6

Indirect A/C Fuel Use [%] 5.6 4.8 4.5 3.5

EPA ran the SC03 cycle simulations instead of FTP/Highway combined cycle 

simulations so that the simulation results would represent the actual A/C cycle test. EPA also 

assumed the EGR boost engine during vehicle simulations because EGR boost engine better 

represents engine technology more likely to be implemented in model years 2017 to 2025 and 

because the A/C impact on CO2 increase in the EGR boost engine is similar to that in the 

baseline engine as shown in Table 5-6 and Table 5-7. Details of this analysis which showed 

impact of A/C usage on fuel consumption is independent of engine technology are provided in 

the next section. Moreover, EPA assumed 38% of a market penetration for automatic climate 

control systems as well as 23.9% and 35.0% of A/C on-time for manual and automatic climate 

control systems, respectively. These are the same assumptions made in the MYs 2012-2016 

rule.

33 In order to come up with overall impact of A/C usage on CO2 emissions for passenger 

cars, the simulation results for cars shown in Table 5-6 were sales-weighted for each model 

year from 2017 to 2025. For the end result, the impact of A/C usage was estimated at 11.9 

CO2 g/mile for cars and 17.2 CO2 g/mile for trucks. This corresponds to an impact of 

approximately 14.0 CO2 g/mile for the (2012) fleet, which is comparable to the MYs 2012-

2016 final rule result, but still lower than the two studies by NREL and NESCCAF cited 

above. 
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5.1.3.2.1 Effect of Engine Technology on Fuel Consumption by A/C System 

In order to continue to maintain the credit levels from the MYs2012-2016 rule, EPA 

had to first demonstrate that the fuel economy and CO2 emissions due to A/C was relatively 

insensitive to the engine technologies that may be expected to be prevalent in the future. If 

for example, more efficient engines are able to run the A/C system more efficiently such that 

the incremental increase in emissions due to A/C decreased compared to the base engines, 

then credits for the same A/C technologies must decrease over time as engines become more 

efficient. This would correspond to a decrease in credits proportional (or multiplicative) to 

the increase in efficiency of the engine. Conversely, if the incremental increase in emissions 

due to A/C remained relatively constant, then the credits available for A/C efficiency should 

also remain stable. This would correspond to the credits (A/C impact) being additive to the 

base emissions rate, thus being independent of engine efficiency). The EPA based the 

hypothesis on the latter assumption. 

In order to prove out this hypothesis, EPA carried out vehicle simulations for several 

cases, including two engine technologies: baseline and EGR boost engines (a surrogate for a 

future advanced efficient engine). Table 5-7 shows the vehicle simulation results of CO2

emissions over the SC03 driving cycle when baseline engines are used, as opposed to the 

advanced EGR boost engines. By comparing the values of CO2 increase with A/C on in Table 

5-6 and Table 5-7, it is evident that the impact of A/C usage on fuel consumption is not very 

dependent on the engine technologies. In fact, the difference in the CO2 increase with A/C on 

(2nd row in table) between the emissions from the baseline and EGR boost engines is less than 

10% for all vehicle classes. 

Table 5-7 Vehicle Simulation Results on CO2 Emissions over SC03 Cycle with Baseline Engine 

SC03 Cycle Small Car Medium Car Large Car Truck

CO2 with A/C off [g/mi] 259.3 348.0 425.4 628.1

CO2 Increase with A/C on [g/mi] 11.3 11.1 12.5 16.2

Total CO2 with A/C [g/mi] 270.6 359.1 437.9 644.3

Indirect A/C Fuel Use [%] 4.2 3.1 2.9 2.5

Figure 5-3 depicts zoomed-in BSFC maps for baseline and EGR boost engines. The 

circles on these maps represent average operating conditions of the engines over the FTP 

(city) drive cycle. The blue circle represents a simulated average operating condition without 

A/C while the red circle represents an average operating condition with A/C. As can be seen 

in the figure, the engines operate at higher load levels when the A/C is on. In this figure, the x 

and y axes present engine speed in RPM and torque in Nm, respectively. 
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5-28 

(a) Baseline Engine (b) EGR Boost Engine

Figure 5-3 Average Engine Operating Conditions with A/C Off and A/C On over Fueling Maps for 

Baseline and EGR Boost Engines 

For the baseline engine case, the engine efficiency improves significantly (375 g/kW-h 

to almost 330 g/kW-h) as it moves along the BSFC surface, whereas the improvement is 

much less for the EGR boost engine as it moves from approximately 250 g/kW-h to 240 

g/kW-h. However, the large improvement in AC efficiency for the baseline engine is offset 

by the fact that the engine itself is less efficient than the EGR boost engine. Conversely, the 

small AC efficiency improvement for the EGR boost engine is compensated by the fact that 

the engine is much more efficient than the baseline engine. As a result, the CO2 increase seen 

by both engines due to A/C usage becomes similar between the two different technologies. 

This result allows us to approximate the A/C impact on vehicle fuel consumption as an 

additive effect rather than as a multiplicative effect since it is independent of engine 

technologies. For the same reason, it also means that A/C credits for a given technology can 

remain constant over time, which will greatly simplify the progression of future credits.m

5.1.3.3 Technologies That Improve Efficiency of Air Conditioning and Their 

Effectiveness 

Most of the excess load on the engine comes from the compressor, which pumps the 

refrigerant around the system loop. Significant additional load on the engine may also come 

from electrical or hydraulic fan units used for heat exchange across the condenser and 

 

m

 It also means that the last row in the above two tables are a bit misleading as A/C impact should not be 

quantified as a fraction of the total emissions, but rather an additive increment. The numbers are left onto the 

tables for comparison purposes to studies in the literature that use this convention. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-29 

radiator. The controls that EPA and NHTSA believe manufacturers would use to generate 

credits for improved A/C efficiency and to improve fuel efficiency in the CAFE program 

through the use of an adjustment in calculated fuel economy would focus primarily, but not 

exclusively, on the compressor, electric motor controls, and system controls which reduce 

load on the A/C system (e.g. reduced ‘reheat’ of the cooled air and increased of use 

recirculated cabin air). EPA and NHTSA are finalizing a program that will result in improved 

efficiency of the A/C system (without sacrificing passenger comfort) while improving the fuel 

efficiency of, and reducing the CO2 emissions from, the vehicle. 

The cooperative IMAC program described above has demonstrated that average A/C 

efficiency can be improved by 36.4% (compared to an average MY 2008 baseline A/C 

system), when utilizing “best-of-best” technologies.34

 EPA and NHTSA consider a baseline 

A/C system to contain the following components and technologies; internally-controlled fixed 

displacement compressor (in which the compressor clutch is controlled based on ‘internal’ 

system parameters, such as head pressure, suction pressure, and/or evaporator outlet 

temperature); blower and fan motor controls which create waste heat (energy) when running 

at lower speeds; thermostatic expansion valves; standard efficiency evaporators and 

condensers; and systems which circulate compressor oil throughout the A/C system. These 

baseline systems are also extraordinarily wasteful in their energy consumption because they 

add heat to the cooled air out of the evaporator in order to control the temperature inside the 

passenger compartment. Moreover, many systems default to a fresh air setting, which brings 

hot outside air into the cabin, rather than recirculating the already-cooled air within the cabin. 

The IMAC program indicates that improvements can be accomplished by a number of 

methods related only to the A/C system components and their controls including: improved 

component efficiency, improved refrigerant cycle controls, and reduced reheat of the cooled 

air. The program EPA and NHTSA are finalizing will encourage the reduction of A/C CO2

emissions from cars and trucks by up to 42% from current baseline levels through a CO2 

credit and fuel economy improvement system. EPA and NHTSA believe that the component 

efficiency improvements demonstrated in the IMAC program, combined with improvements 

in the control of the supporting mechanical and electrical devices (i.e. engine speeds and 

electrical heat exchanger fans), can go beyond the IMAC levels and achieve a total efficiency 

improvement of 42% through incremental improvements beyond that shown in the study due 

to the long lead time before MY 2017. The following sections describe the technologies the 

agencies believe manufacturers can use to attain these efficiency improvements. 

Based on the new vehicle simulation research conducted by the EPA described above, 

the EPA believes that the impact of A/C on average CO2 emissions amounts to 11.9 CO2

g/mile for cars and 17.2 CO2 g/mile for trucks (0.001339/0.001935 gallons of gasoline per 

mile car/truck improvement) and that these results are relatively insensitive to the engine and 

transmission efficiency improvements expected to be seen during the rule timeframe. A 42% 

improvement on this emissions rate leads to the maximum credit opportunity of 5.0 g CO2/mi 

for cars and 7.2 g CO2/mi for trucks (-0.000563 / -0.000810 gallons per mile car/truck 

improvement). This compares to the 5.7 g/mi (identical for cars and trucks) finalized in the 

2012-2016 final rule. When cars and trucks are combined, the new final rule maximum 

credits are consistent (on a fleet level) with those finalized in the previous rule, though for 

cars the credits are now somewhat reduced and for trucks increased. The agencies believe 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-30 

that the modification of these credits for this rule is justified given the simulation work 

conducted, which shows that A/C emissions tends to be larger for the larger vehicles (and 

trucks tend to be larger than passenger cars). 

The following sections discuss each of the A/C efficiency-improving technologies that 

EPA recognizes in the efficiency credit menu. We estimated the effectiveness of each of 

these technologies for the MYs 2012-2017 rule based on a variety of sources, including 

testing under the IMAC program and internal EPA testing. We did not receive comments 

challenging these estimates and continue to base the efficiency credits (and CAFE 

improvement values) on these estimates. 

5.1.3.3.1 Reduced Reheat Using a Externally-Controlled, Variable-Displacement 

Compressor 

The term ‘external control’ of a variable-displacement compressor is defined as a 

mechanism or control strategy where the displacement of the compressor adjusted 

electronically, based on the temperature setpoint and/or cooling demand of the A/C system 

control settings inside the passenger compartment. External controls differ from ‘internal 

controls’ that internal controls adjust the displacement of the compressor based on conditions 

within the A/C system, such has head pressure, suction pressure, or evaporator outlet 

temperature. By controlling the displacement of the compressor by external means, the 

compressor load can be matched to the cooling demand of the cabin. With internal controls, 

the amount of cooling delivered by the system may be greater than desired, at which point the 

cooled cabin air is then ‘reheated’ to achieve the desired cabin comfort. It is this reheating of 

the air which results reduces the efficiency of the A/C system – compressor power is 

consumed to cool air to a temperature less than what is desired. 

Reducing reheat through external control of the compressor is a very effective strategy 

for improving A/C system efficiency. The SAE IMAC team determined that an annual 

efficiency improvement of 24.1% was possible using this technology alone.34 The agencies 

estimate that additional improvements to this technology are possible (e.g. the increased use 

of recirculated cabin air), and that when A/C control systems and components are fully 

developed, calibrated, and optimized to particular vehicle’s cooling needs, an efficiency 

improvement of 42% can be achieved, compared to the baseline system. 

5.1.3.3.2 Reduced Reheat Using a Externally-Controlled, Fixed-Displacement or 

Pneumatic Variable-Displacement Compressor 

When using a fixed-displacement or pneumatic variable-displacement compressor 

(which controls the stroke, or displacement, of the compressor based on system suction 

pressure), reduced reheat can be realized by disengaging the compressor clutch momentarily 

to achieve the desired evaporator air temperature. This disengaging, or cycling, of the 

compressor clutch must be externally-controlled in a manner similar to that described in 

2.3.2.1. The agencies believe that a reduced reheat strategy for fixed-displacement and 

pneumatic variable-displacement compressors can result in an efficiency improvement of 

20%. This lower efficiency improvement estimate (compared to an externally-controlled 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-31 

variable displacement compressor) is due to the thermal and kinetic energy losses resulting 

from cycling a compressor clutch off-and-on repeatedly. 

5.1.3.3.3 Defaulting to Recirculated Cabin Air 

In ambient conditions where air temperature outside the vehicle is much higher than 

the air inside the passenger compartment, most A/C systems draw air from outside the vehicle 

and cool it to the desired comfort level inside the vehicle. This approach wastes energy 

because the system is continuously cooling the hotter outside air instead of having the A/C 

system draw its supply air from the cooler air inside the vehicle (also known as recirculated 

air, or ‘recirc’). By only cooling this inside air (i.e. air that has been previously cooled by the 

A/C system), less energy is required, and A/C Idle Tests conducted by EPA indicate that an 

efficiency improvement of 35-to-40% improvement is possible under idle conditions. 

Ongoing testing on the new AC17 test, described below, may in the future shed light on the 

overall effectiveness of this technology during other driving conditions. 

A mechanically-controlled door on the A/C system’s air intake typically controls 

whether outside air, inside air, or a mixture of both, is drawn into the system. Since the 

typical ‘default’ position of this air intake door is outside air (except in cases where maximum 

cooling capacity is required, in which case, many systems automatically switch this door to 

the recirculated air position), EPA and NHTSA are specifying that, as cabin comfort and defogging conditions allow, an efficiency credit be granted if a manufacturer defaults to 

recirculated air whenever the outside ambient temperature is greater than 75°F. To maintain 

the desired quality inside the cabin (in terms of freshness and humidity), EPA believes some 

manufacturers will control the air supply in a ‘closed-loop’ manner, equipping their A/C 

systems with humidity sensors or fog sensors (which detect condensation on the inside glass), 

allowing them to adjust the blend of fresh-to-recirculated air and optimize the controls for 

maximum efficiency. Vehicles with closed-loop control of the air supply (i.e. sensor 

feedback is used to control the interior air quality) will qualify for a 1.7 g/mi CO2 credit and a 

0.000124 gal/mi fuel consumption improvement. Vehicles with open-loop control (where 

sensor feedback is not used to control interior air quality) will qualify for a 1.1 g/mi CO2

credit and a 0.000124 gal/mi fuel consumption improvement. We believe that the closed-loop 

control system will be inherently more efficient than the open-loop control system because the 

former can maximize the amount to recirculation to achieve a desired air quality and interior 

humidity level, whereas the latter will use a fixed ‘default’ amount of recirculated air which 

provides the desired air quality under worst case conditions (e.g. maximum number of 

passengers in the vehicle). 

Electric drive vehicles such as HEVs, PHEVs and EVs may require some fraction of 

the A/C cooling capacity to control the battery temperature under hot conditions. PHEVs are 

most likely to require A/C cooling because their batteries have higher current requirements for 

all-electric driving than HEVs, and much less battery mass and energy storage than pure EVs. 

Some electrified vehicles today, such as the Nissan Leaf, cool their batteries with outside air, 

while others, such as the Toyota Prius and Ford Fusion Hybrid, use cooled cabin air, and the 

Chevrolet Volt is an example of a vehicle which uses a refrigerant loop to cool the battery 

coolant and thus to cool the battery. With the increased penetration of these electrified 

vehicles, it is possible that there will be some loss of efficiency of the A/C system (especially 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-32 

as it relates to cabin air recirculation). Vehicles which use cabin air to cool the battery must 

discharge this heated air outside the vehicle, rather than recirculating it through the climate 

control system. Currently, EPA does not account for this A/C efficiency loss in the credit 

menu. EPA and NHTSA requested comments on the technical merits or applicability of 

accounting for this loss of efficiency within the crediting and fuel economy improvement 

scheme, however there were no comments submitted. The agencies are therefore not making 

any adjustment in the credit menu for electric drive vehicles. As these types of vehicles 

become more common, the agencies will continue to study the effectiveness of air 

conditioning technologies. 

5.1.3.3.4 Improved Blower and Fan Motor Controls 

In controlling the speed of the direct current (DC) electric motors in an air 

conditioning system, manufacturers often utilize resistive elements to reduce the voltage 

supplied to the motor, which in turn reduces its speed. In reducing the voltage however, these 

resistive elements produce heat, which is typically dissipated into the air ducts of the A/C 

system. Not only does this waste heat consume electrical energy, it contributes to the heat 

load on the A/C system. One method for controlling DC voltage is to use a pulsewidth 

modulated (PWM) controller on the motor. A PWM controller can reduce the amount of 

energy wasted, and based on Delphi estimates of power consumption for these devices, EPA 

and NHTSA believe that when more efficient speed controls are applied to either the blower 

or fan motors, an overall improvement in A/C system efficiency of 15% is possible.35 

5.1.3.3.5 Internal Heat Exchanger 

An internal heat exchanger (IHX), which is alternatively described as a suction line heat 

exchanger, transfers heat from the high pressure liquid entering the evaporator to the gas 

exiting the evaporator, which reduces compressor power consumption and improves the 

efficiency of the A/C system. In the MYs 2012-2016 rule, we considered that IHX 

technology would be required with the changeover to an alternative refrigerant such as HFO1234yf, as the different expansion characteristics of that refrigerant (compared to R-134a) 

would necessitate an IHX. The agencies believe that a 20% improvement in efficiency 

relative to the baseline configuration can be realized if the system includes an IHX, and a 1.1 

g/mi credit and a 0.000124 gal/mi fuel consumption improvement for an IHX. 

5.1.3.3.6 Improved-Efficiency Evaporators and Condensers 

The evaporators and condensers in an A/C system are designed to transfer heat to and 

from the refrigerant – the evaporator absorbs heat from the cabin air and transfers it to the 

refrigerant, and the condenser transfer heat from the refrigerant to the outside ambient air. 

The efficiency, or effectiveness, of this heat transfer process directly effects the efficiency of 

the overall system, as more work, or energy, is required if the process is inefficient. A 

method for measuring the heat transfer effectiveness of these components is to determine the 

Coefficient of Performance (COP) for the system using the industry-consensus method 

described in the SAE surface vehicle standard J2765 – Procedure for Measuring System COP 

of a Mobile Air Conditioning System on a Test Bench.36 The bench test based engineering 

analysis that a manufacturer will submit at time of certification. We will consider the baseline 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

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component to be the version which a manufacturer most recently had in production on the 

same vehicle or a vehicle in a similar EPA vehicle classification. The design characteristics 

of the baseline component (e.g. tube configuration/thickness/spacing and fin density) are to be 

documented in an engineering analysis and compared to the improved components, along 

with data demonstrating the COP improvement. This same engineering analysis can be 

applied to evaporators and condensers on other vehicles and models (even if the overall size 

of the heat exchanger is different), as long as the design characteristics of the baseline and 

improved components are the same. If these components can demonstrate a 10% 

improvement in COP versus the baseline components, EPA and NHTSA estimate that a 20% 

improvement in overall system efficiency is possible. 

5.1.3.3.7 Oil Separator 

The oil present in a typical A/C system circulates throughout the system for the 

purpose of lubricating the compressor. Because this oil is in contact with inner surfaces of 

evaporator and condenser, and a coating of oil reduces the heat transfer effectiveness of these 

devices, the overall system efficiency is reduced.37

 It also adds inefficiency to the system to 

be “pushing around and cooling” an extraneous fluid that results in a dilution of the 

thermodynamic properties of the refrigerant. If the oil can be contained only to that part of 

the system where it is needed – the compressor – the heat transfer effectiveness of the 

evaporator and condenser will improve. The overall COP will also improve due to a 

reduction in the flow of dilutent. The SAE IMAC team estimated that overall system COP 

could be improved by 8% if an oil separator was used. EPA and NHTSA believe that if oil is 

prevented from prevented from circulating throughout the A/C system, an overall system 

efficiency improvement of 10% can be realized. Whether the oil separator is a standalone 

component or is integral to the compressor design, manufacturers can submit an engineering 

analysis to demonstrate the effectiveness of the oil separation technology. 

5.1.3.4 Technical Feasibility of A/C Efficiency-Improving Technologies 

EPA and NHTSA believe that the efficiency-improving technologies discussed in the 

previous sections are available to manufacturers today, are relatively low in cost, and their 

feasibility and effectiveness has been demonstrated by the SAE IMAC teams and various 

industry sources. The agencies also believe that when these individual components and 

technologies are fully designed, developed, and integrated into A/C system designs, 

manufacturers will be able to achieve the estimated reductions in CO2 emissions and generate 

appropriate A/C Efficiency Credits, which are discussed in the following section. 

5.1.3.5 A/C Efficiency Test Procedures 

As proposed, the agencies have two test procedures to determine eligibility for A/C 

efficiency credits. The two test procedures are the Idle and the AC17 test procedures. The 

test procedures play different roles depending on the model year for which the test is 

conducted. For model years 2014 to 2016, there are three options for qualifying for A/C 

efficiency credits: 1) running the A/C Idle Test, as described in the MYs 2012-2016 final rule, 

and demonstrating compliance with the CO2 threshold requirements, 2) running the A/C Idle 

and demonstrating compliance with engine displacement adjusted CO2 threshold 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

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requirements, and 3) running a newly-developed AC17 test and reporting the test results. For 

model years 2017-2019, the AC17 test will be the exclusive means manufacturers will have to 

demonstrate eligibility for A/C efficiency credits, again by reporting the test results. By 

reporting test results, manufacturers gain access to the credits on the menu based on the 

design of their AC system. In MYs 2020 and thereafter, however, the AC17 test will be used 

not only to demonstrate eligibility for efficiency credits, but also to partially quantify the 

amount of the credit. AC17 test results (“A” to “B” comparison) equal to or greater than the 

menu value will allow manufacturers to claim the full menu value for the credit. A test result 

less than the menu value will limit the amount of credit to that demonstrated on the AC17 test. 

In addition, for MYs 2017-2021, A/C fuel consumption improvement values will be available 

for CAFE in addition to efficiency credits being available for GHG compliance. These 

adjustments to the utilization and design of the A/C test procedures were largely a result of 

new data collected, as well as the extensive technical comments submitted on the proposal. 

Details of the AC17 test requirements as well as the modified idle test thresholds are 

described in detail in this section. 

In the MYs 2012-2016 final rule, manufacturers were required, starting in MY 2014, 

to demonstrate the efficiency of a vehicle’s A/C system by running an A/C Idle Test as a 

prerequisite to credit eligibility (the amount of credit determined separately by means of the 

credit menu). If a vehicle met the emissions threshold of 14.9 g/min CO2 or lower on this test, 

a manufacturer was eligible to receive full credit for efficiency-improving hardware or 

controls installed on that vehicle. The vehicle would be able to receive A/C credits based on a 

menu of technologies specifying the credit amount associated with each technology. A 

revised version of this technology menu is described below. For vehicles with a result 

between 14.9 g/min and 21.3 g/min, a downward adjustment factor was applied to the eligible 

credit amount, with vehicles testing higher than 21.3 g/min not being eligible to receive 

credits . The details of this idle test can be found in the MYs 2012-2016 final rule. See 75 FR 

at 25426-27. This methodology for accessing the credit menu based on the Idle Test results 

(and threshold requirements) still apply for model years 2014-2016, so this final rule is not 

making any fundamental changes to the previous rule. EPA is, however providing an optional 

new threshold requirement for MYs 2014-2016 reflecting both the proposed rule and the 

comments submitted on the idle test. 

In order to establish the value of this eligibility threshold for the MYs 2012-2016 final 

rule, the EPA conducted an extensive laboratory testing program to measure the amount of 

additional CO2 a vehicle generated on the Idle Test due to A/C use. The results of this test 

program are summarized in Table 5-8, and represent a wide cross-section of vehicle types in 

the U.S. market. The average A/C CO2 result from this group of vehicles is the value against 

which results from vehicle testing will be compared. The EPA conducted laboratory tests on 

over 60 vehicles representing a wide range of vehicle types (e.g. compact cars, midsize cars, 

large cars, sport utility vehicles, small station wagons, and standard pickup trucks). 

Table 5-8 Summary of A/C Idle Test Study Conducted by EPA at the National Vehicle Fuel and Emissions 

Laboratory 

Vehicle Makes Tested 19

Vehicle Models Tested 29
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5-35 

Model Years Represented (number of vehicles in 

each model year)

1999 (2), 2006 (21), 2007 

(39)

EPA Size Classes Represented Minicompact, Compact, 

Midsize, and Large Cars

Sport Utility Vehicles

Small Station Wagons

Standard Pickup Trucks

Total Number of A/C Idle Tests 62

Average A/C CO2 (g/min) 21.3

Standard Deviation of Test Results (+ g/min) 5.8

The majority of vehicles tested were from the 2006 and 2007 model years and their 

A/C systems are representative of the ‘baseline’ technologies, in terms of efficiency (i.e. to 

EPA’s knowledge, these vehicles do not utilize any of the efficiency-improving technologies 

described in the credit menu finalized for the MYs 2012-2016 rule). For the MYs 2012-2016 

rule, EPA attempted to find a correlation between the A/C CO2 results and a vehicle’s interior 

volume, footprint, and engine displacement, but found it to be minimal, as there is significant 

“scatter” in the test results. This scatter is generally not test-to-test variation, but scatter 

amongst the various vehicle models and types. This original analysis covered a wide range of 

vehicle size classes and vehicle types: from compact cars to light-duty trucks, some of which 

did not have readily-available SAE and CAFE interior volume numbers (i.e. the interior 

volume for small station wagons and pickup trucks had to be inferred from other published 

sources). Due to the variability in the data, EPA chose a constant threshold value for the Idle 

Test performance, which provided access to the credit menu. 

Since the previous rule, manufacturers have had the opportunity to run the Idle Test on 

a wide variety of vehicles and have discovered that even though there may be a small 

correlation between engine displacement and the Idle Test result, the trend was important 

enough that small vehicles had higher A/C idle emissions and were more inclined to fail to 

meet the threshold for the Idle Test than were larger vehicles. Specifically, vehicles with 

smaller displacement engines had a higher Idle Test result than those with larger displacement 

engines, even within the same vehicle platform.38 This was causing some small vehicles with 

advanced A/C systems to fail the Idle Test. The load placed on the engine by the A/C system 

did not seem to be consistent, and in certain cases, larger vehicles perform better than smaller 

ones on the A/C idle test. 

When the EPA test data is sorted according to engine displacement, the relationship 

between engine displacement and idle test result are somewhat apparent, though there is 

significant variability as is evident in Figure 5-4. The threshold value from the MYs 2012-

2016 rule is included in the figure for comparison purposes. 
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5-36 

Figure 5-4 Relationship Between EPA A/C Idle Test Results and Engine Displacement. 

One factor which may explain part of this observed phenomenon is that the brakespecific fuel consumption (bsfc) of a smaller displacement engine is generally lower at idle 

than that of a larger displacement engine. At the idle condition, without A/C load applied, a 

smaller engine is generally more efficient (i.e. has a lower bsfc) than a larger engine, in terms 

of how well it converts fuel heat energy into power. When additional load from the A/C 

system is added to the small displacement engine, the bsfc does not improve as dramatically 

as it does on a larger displacement engine, and if both the small- and large-displacement 

engines require a similar amount of engine power to run the A/C system, the larger engine 

will move from a “less-efficient” to “more-efficient” operating condition, whereas the smaller 

engine remains relatively flat, in terms of bsfc. The result is that a larger displacement engine 

uses less fuel to run the A/C system, relative to a smaller displacement engine, because its 

baseline condition (A/C off) is “less-efficient”, and the incremental amount of fuel used is 

lower. The slope of the linear regression line for this data set is -1.58 g/min/L, with a zero 

intercept of 26.9 g/min.n

 

In the MYs2012-2016 rule, the EPA chose a threshold of 30% improvement on the 

Idle Test as the threshold for accessing the credit menu (the justification and feasibility 

 

n

 The R2 for this fit is 0.09 reflecting the scatter and variability of the data. The slope is statistically significant at 

the 2% confidence level (Significance F) indicating that the slope is statistically significant. 

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Test Results_A/C CO2 Rate (g/min)

Engine Displacement (L)

14.9 g/min Idle Test Threshold 

(for full credit value)
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-37 

argument is presented again below). This corresponded to a 6.4 g/min reduction from the 

average Idle Test result (20.5 g/min). Thus the 30% improvement is the average idle test 

result (20.5 g/min) minus 30% (6.4 g/min) which equals 14.9 g/min (in the previous rule). In 

this rule, EPA will maintain the 6.4g/min gap between the average emissions (equation of the 

line) and the threshold. Equation 5-4 results in an idle test threshold which is scaled 

according to engine displacement, in liters. The threshold equation is overlaid on the data in 

Figure 5-5. Using this equation, the idle test threshold for a 1.2L engine for example (to 

receive full credit) would be 18.6 g/min for a 6.0L engine the threshold would be 11.0 g/min. 

Equation 5-4 – A/C Idle Test Threshold 

In the MYs 2017-2025 NPRM, we acknowledged that the idle test may not fully 

capture the effect of each and every technology, but believed that the test did reflect the 

overall efficiency of the vehicle’s A/C system under a commonly encountered operating 

condition. See 76 FR at 74938. For this reason, we continue to allow the use of the original 

Idle Test through model year 2016. In addition, we have now combined the Idle Test with a 

displacement-adjusted “threshold,” which some manufacturers wishing to use the Idle Test 

may choose to apply. Overall, however, we now believe that the newly developed AC17 test 

is a more accurate method for determining A/C fuel use and CO2 emissions, and that the A/C 

Idle Test requirement in both its forms can eventually be phased out (as described below). 

We believe that part of the variation in the relationship between displacement and Idle 

Test result that is evident in the figure above, was due to the type of components a 

manufacturer chose to use in a particular vehicle. Components such as compressors are shared 

across vehicle model types (e.g. a compressor may be ‘over-sized’ for one application, but the 

use of a common part amongst multiple model types results in a cost savings to the 

manufacturer), rather than being designed for one particular cabin size. Some of the variation 

may also be due to the amount of cooling capacity a vehicle has at idle. For instance, if the 

cooling capacity (or cooling performance) of a particular vehicle was less-than-optimal at idle 

(due to factors such as limitations of the compressor design, pulley ratio, or packaging), this 

vehicle could produce below-average A/C CO2 results, because the amount of energy required 

by the compressor would be lower. Yet at higher engine and/or vehicle speeds, this same 

vehicle may have cooling capacity typical of other vehicles. Therefore, a test which is limited 

to one area of A/C operation is limited in its ability to determine overall A/C system 

efficiency. 

Some of this variation between vehicle models may also be due to the efficiency of the 

fan(s) which draw air across the condenser – since an external fan is not placed in front of the 

vehicle during the A/C Idle Test, it is the vehicle’s radiator fan which is responsible for 

rejecting heat from the condenser (and some models may do this more efficiently than others). 

In this case, EPA believes that an SC03-type test – run in a full environmental chamber with a 

“road-speed” fan on the front of the vehicle – would be a better measure of how a vehicle’s 

A/C system performs under transient conditions, and any limitations the system may have at 

idle could be counter-balanced by improved performance and efficiency elsewhere in the 

drive cycle. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-38 

Since the MYs 2012-2016 final rule, EPA has received a number of idle test results 

from several manufacturers. Testing by Ford, General Motors, and Chrysler has shown that 

there are some significant limitations to the idle test procedure. As mentioned above, there 

was significant test-to-test variability noted, and many vehicles – especially those with 

smaller displacement engines – failed to meet the required test threshold (14.9 g/min) to 

qualify for A/C credits – even when such vehicles are equipped with a significant number of 

efficiency-improving technologies listed in the menu. These tested vehicles were from 

upcoming model years and had a variety of air conditioner components and controls strategies 

(from among the technologies described above and in the menu) implemented. The results 

are shown in the Table 5-9 and are printed with permission from the manufacturers. 

Table 5-9 A/C Idle Test Results from Various Manufacturers 

Engine Displacement (liters) A/C Idle Test Result (gCO2/min)

1.4 19.4

2.0 22.4

2.0 20.0

2.4 28.0

2.4 18.3

3.5 12.0

3.6 24.0

3.6 16.0

5.7 26.0

The test-to-test variability observed by the manufacturers was significant, and is likely 

due to high dilution of the exhaust sample (exhaust mass flow is low at idle), which results in 

greater measurement error, as there is less CO2 present for sampling than there would be 

under normal operating conditions. Furthermore, fluctuations in cell ambient conditions (e.g. 

temperature and humidity), or in the way the driver is positioned in the seat, make accurate 

test-to-test comparisons of the results difficult to achieve. In Figure 5-5, these new data 

points from the manufacturers are overlaid onto the idle test data collected in support of the 

MYs 2012-2016 final rule by the EPA. Most of the EPA vehicles tested over the past two 

years did not contain a significant amount of efficient air conditioner components (off of the 

menu list). The manufacturer data is largely consistent with the EPA data. The data support 

the notion that it might be more appropriate to use an increasing function of emissions as a 

function of engine displacement for a threshold, rather than the flat function we finalized in 

the MYs 2012-2016 rule. 

The test cells on which Idle Tests are conducted are typically the same cells which are 

used for FTP testing for criteria pollutants, where the allowable ambient temperature is 68-to86 °F, and there is no humidity specification. Since there are normal, seasonal fluctuations in 

humidity level for this type of test cell, controlling the ambient conditions to those specified 

in the Idle Test procedure is difficult. EPA is modifying the allowable ambient air 

temperature condition from to 75 ± 2 °F on average to 73-to-80°F on average, and the 

ambient humidity within the test cell be modified from 50 ± 5 grains of water per pound of 

dry air to 40-to-60 grains of water per pound of dry air. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-39 

Figure 5-5 EPA A/C Idle Test Results with Results from Various Manufacturers 

Based on manufacturer data, with the revised threshold, it is still possible for a vehicle 

test to have some A/C technologies but still fail to meet the threshold for the credit menu. For 

the 2014 to 2016 model years, where a manufacturer chooses to run the A/C Idle Test, EPA is 

allowing partial credits for vehicles that fail to meet the threshold but that show an 

improvement over the baseline To qualify for the full credit, it will be necessary for each 

vehicle certified to achieve an A/C CO2 result less than or equal to the threshold function. As 

previously described, the threshold function is 30% less than the average value observed in 

the EPA testing. EPA chose the 30% improvement over the “average” value to drive the fleet 

of vehicles toward A/C systems which approach or exceed the efficiency of best-in-class 

vehicles. EPA test results on three vehicle size classes (large car, SUV, and pickup truck) 

indicate that significant reductions in fuel consumption can be achieved by simply switching 

A/C control from outside air (OSA) to recirculated cabin air. As shown in Table 5-11, the 

percentage reduction in the CO2 and fuel consumption due to A/C use was greater than 30% 

in all three cases. 

Table 5-10 Effect of Outside Air and Recirculated Cabin Air on A/C Idle Test Results (EPA Testing) 

Vehicle Type

A/C CO2 Result (g/min) Change in A/C CO2 

w/Recirc (%) w/Outside Air w/Recirc Cabin Air

Large Car 25.9 14.0 -45.9

SUV 17.4 11.4 -34.5

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Test Results_A/C CO2 Rate (g/min)

Engine Displacement (L)

EPA Data OEM Data Linear (New Threshold)

14.9 g/min Idle Test Threshold (for 

full credit value in 2012 rule)

FRM 

Threshold
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-40 

Pickup Truck 14.1 9.0 -36.2

EPA believes the threshold approach will cause manufacturers to tailor the size of A/C 

components and systems to the cooling needs of a particular vehicle model and focus on the 

overall efficiency of their A/C systems. However, as explained above, the agency is 

establishing as proposed an intermediate level of credit for vehicles that do not meet or 

exceed the Idle Test threshold (either the original fixed threshold from the earlier rule or the 

displacement-adjusted threshold finalized in this rule), so as not to set an all or nothing 

threshold to qualify for credits. EPA will allow an intermediate amount of credit as long as 

the Idle Test performance remains better than the best fit regression obtained from EPA 

testing. A multiplier would be applied to the credits (based on the menu) such that if the 

difference between the Idle Test result and the threshold value (hereafter referred to as the 

“gap”) at the vehicle’s engine displacement is greater than 6.4g/min, then the multiplier would 

be 1.0. If the gap is 0.0 g/min or less, then the multiplier would be 0.0, and the multiplier in 

between would follow a linear function as shown in the following figure. Figure 5-6 shows 

the change in credit received (y axis) as the idle test result in g/min (x axis) decreases. In the 

Figure, as the Idle test result (in g/min) decreases, the difference between the Idle test 

measurement and the threshold increases. The EPA is finalizing an option that allows 

manufacturers the option of using these threshold adjustments as early as MY 2014. 

Figure 5-6 EPA A/C Credit Multiplier as a Function of the Difference Between Idle Test Measured and 

Threshold at any given engine displacement 

5.1.3.6 AC17 Test 

EPA continues to recognize the limitations of the Idle Test. The primary disadvantage 

is that the test does not capture the majority of the driving or ambient conditions in the real 

world when the A/C is in operation, and thus may only encourage the technologies that 

improve idle performance under narrow temperature conditions. Another limitation is that the 

narrow range of engine operating conditions present during the Idle Test make it difficult to 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-41 

quantify the incremental improvement of a given technology to generate an actual credit over 

non-idle operation (without a menu). In order to generate a credit value a more complex test 

procedure is required that can do an “A” to “B” comparison, where B is with the technology 

and A is without. There were comments from a number of stakeholders both after 

promulgation of the MY 2012-2016 rule and in response to the proposed 2017-2025 model 

year standards reiterating some of these limitations. In preparation for the 2017-2025 NPRM, 

EPA initiated a study that engaged automotive manufacturers, USCAR, component suppliers, 

SAE, and CARB in developing a new test procedure for determining A/C system efficiency 

and credits. This effort also explored the applicability and appropriateness of a test method or 

procedure which combines the results of test-bench, modeling/simulation, and chassis 

dynamometer testing into a quantitative metric for quantifying A/C system (fuel) efficiency. 

The goal of this exercise was the development of a reliable, accurate, and verifiable 

assessment and testing method while also minimizing a manufacturer’s testing burden. The 

result of this effort is the new AC17 test, which we believe is capable (in part) of detecting the 

effect of more efficient A/C components and controls strategies during a transient drive cycle, 

rather than just idle. EPA believes that this new test procedure more accurately reflects the 

impact that A/C use (and in particular, efficiency-improving components and control 

strategies) has on tailpipe CO2 emissions. EPA proposed use of this test, to be phased in 

starting in MY 2014 as an option, in MYs 2017-2019 as the exclusive means of determining 

eligibility for A/C efficiency credits, and thereafter as both an eligibility test and as a partial 

means of determining credit amount. See 76 FR 74938, 74940. 

The new AC17 test has four elements: a pre-conditioning cycle, a 30-minute solar 

soak period; Bag 1 is an SC03 drive cycle at 77 °F (to capture the “pull-down,” or post-soak, 

interior cool-down portion of A/C operationo); and Bag 2 is a highway fuel economy cycle (to 

capture the “steady-state” portion of A/C operation). The test cycle is first run with the A/C 

on (Bags 1 and 2) and then re-run with the A/C off (Bags 3 and 4). The A/C-related CO2 

emissions are the difference between the A/C on and A/C off test results, with both bag results 

being averaged (i.e. weighted equally), and the difference between A/C on and A/C off 

averages producing the overall test result for the vehicle, which represents the incremental 

CO2 emissions due to operation of the A/C system. The incremental pull-down and steadystate test results will be reported separately, as well as an average of these two results. EPA 

believes that this new test cycle will be able to capture improvements in all areas related to 

efficient operation of a vehicle’s A/C system: solar control; efficiency improving components; 

and efficient control strategies. Below is a depiction of the new test cycle. To assure 

consistent results for the fuel consumption effect of operating the A/C system, the test is 

always run in a warm condition, where an EPA Urban Dynamometer Driving Schedule 

(UDDS) cycle is run at the start of the test sequence, with the A/C off and the solar lamps on. 

Immediately following this precondition phase, the engine is turned off and the vehicle soaks 

for 30 minutes with the solar lamps on. At the conclusion of the solar soak, the “pull-down” 

(rapid cool-down of cabin temperature) phase begins. This phase utilizes the existing SC03 

 

o

 The pull-down period, is the time during which the cabin goes from an elevated temperature state (after having 

soaked in the heat and sun) to the steady state interior temperature conditions requested by the vehicle occupants. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-42 

drive cycle to simulate dynamic, urban driving conditions. Finally, the highway fuel 

economy test cycle, or HFET, is used to simulate a “steady-state” driving condition, while the 

A/C system is maintaining the cabin temperature. Each element of this cycle exercises modes 

of operation seen in everyday use where cabin cooling is needed. By running the vehicle 

through each of these conditions with and without the A/C system operating, we seek to 

understand the effect that soak, pull-down, and steady-state conditions have on the fuel 

consumption for a particular A/C system design or technology. As shown in Figure 5-7, the 

total time required to run this test on a single vehicle is approximately 4 hours (including A/C 

on and A/C off phases). 

Figure 5-7 AC17 Test 

In the NRPM, EPA sought comment on the ambient conditions and system control 

settings proposed for this test. The ambient temperature for the AC17 test is 77+ 2 °F 

average, and 77+ 5 °F instantaneous, with a humidity level in the test cell of 69 + 5 grains of 

water per pound of dry air average, and 69 + 10 grains instantaneous. These ambient 

temperature and humidity conditions for the AC17 test were chosen because we believed that 

they represented a common operating condition for A/C use, and that they would allow the 

effect of many technologies to be demonstrated. The high temperature and humidity 

conditions of the current SC03 test (95 °F and 100 grains of water per pound of dry air), , 

while encountered in certain parts of the United States, does not adequately demonstrate the 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-43 

impact of technologies such as variable displacement compressors, as their efficiency benefit 

is more evident under lower cooling demand conditions. Several commenters noted that that 

the temperature and humidity tolerances of the test cell conditions may result in voided tests, 

due to the difficulty in maintaining these conditions throughout a 4-hour test interval. The 

Alliance asked that humidity requirements be relaxed to avoid voided tests from the proposed 

69 + 5 average and 69 + 10 instantaneous grains of water/pound of dry air. They also asked 

that ambient temperatures be relaxed from the proposed 77 + 3 degrees on average and 77 + 5 

degrees instantaneous for 95% of the time. They stated that SC03 test facilities were not 

designed to operate at these temperature and humidity conditions at the required solar load of 

850 W/m2. Ford supported the Alliance comments on AC17 temperature and humidity 

requirements. However, we believe that widening these tolerances would negatively affect 

the accuracy of the test – producing either too-high or too-low results for A/C-related CO2 

emissions. As such, we are retaining the proposed tolerances for temperature and humidity as 

proposed, but will stipulate that these tolerances apply only during the emissions 

measurement portions of the test, and temperature and humidity deviations during the nonemissions measurement portions of the test (e.g. preconditioning and soak) may exceed these 

tolerances, as long at the duration of the deviation is no more than three minutes. In addition, 

we will allow manufacturers to use a 30-second moving average on the temperature tolerance, 

instead of an instantaneous temperature value. We will continue to investigate over time 

whether temperature and humidity correction factors on the AC17 test results are appropriate, 

and if so, we will consider their inclusion in the future. 

Commenters also noted that an allowable wind speed for the AC17 test cell should be 

specified for soak and idle conditions, as some air movement within the cell may be necessary 

to assure proper control of the air temperature and humidity. The agencies agree with this 

suggestion, and we will allow a nominal wind speed of 4 miles per hour or less during the 

soak and idle portions of the test cycle. The agencies are also clarifying that the solar lamps 

are off during the soak prior to the A/C-off portion of the AC17 test (Phases 1 and 2). 

Concerning the 30-minute soak time, this is a nominal time requirement, and not a precise 

interval, as is the test involves coordination of the operator exiting and entering the vehicle at 

the beginning and end of the soak period, as well as activation of the solar lamps. Also, if the 

windows on the vehicle need to be partially open to allow for instrumentation and wiring to 

be passed through to the interior, a temporary seal (e.g. foam or tape) can be used to close the 

window gap. 

The control settings for the “A/C ON” portions of the test (Phases 3 and 4 in Figure 

5-7) are different for systems with automatic and manual climate controls. Automatic 

systems will be set to a 72 °F target temperature, with blower (or fan) speed and vent location 

controlled by the automatic mode. Manual systems will set the temperature selector to full 

cold, blower speed at its highest setting, and the air supply set to “recirculated air” for the first 

185 seconds of Phase 3. At the first idle of Phase 3 (186 to 204 seconds), the blower speed 

will be set to achieve a nominal 6 volts at the motor, temperature selector will be set to 

provide 55 °F at the center dash outlet, and the air supply set to “outside air”. The 

recommended temperature selector and blower control positions for manual systems will be 

identified by the manufacturer. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-44 

5.1.3.7 Analysis of EPA’s AC17 Testing 

EPA has conducted independent testing on a variety of vehicles and air conditioning 

technologies on the AC17 test cycle. The purpose of this test effort was to gain insight 

regarding the appropriateness of the AC17 test for verifying the reduction in CO2 emissions 

which are expected from A/C technologies on the efficiency credit menu. EPA tested six 

vehicles, including three pairs of carlines with some element of different air conditioner 

systems. The vehicles and A/C technologies evaluated (and yet to be evaluated) in the EPA 

test program are shown in Table 5-11. 

Table 5-11 EPA AC17 Test Vehicles and Technologies 

YEAR MAKE/MODEL SPECIAL HVAC DETAIL/FEATURES (if applicable)

2009/2010 Dodge Journey 2.4L

2011 Dodge Journey 2.4L New condenser design

2011 Chrysler 300C 5.7L

2012 Chrysler 300C 5.7L Default to re-circulated air above 75 °F

2009 Dodge Caliber 2.4L

2012 Dodge Caliber 2.4L Switch from orifice tube to TXV + default to recirculated air above 75 °F

2009 GM Silverado

2010 GM Sierra Reduced reheat

2011 Buick LaCrosse

2012 Buick LaCrosse Reduced reheat

2011 or 

2012 Ford Fiesta Hatchback 6 speed auto

2011 or 

2012 Ford Fiesta Hatchback 6 speed auto with SFE (Super Fuel Economy)

2011 Ford Edge or Ford Explorer 3.5L 

2012 Ford Edge or Ford Explorer 2.0L Eco-Boost

5.1.3.7.1 Overview: 

In order to verify the appropriateness of the AC17 test as a method to estimate the 

relative efficiency of different A/C systems in a particular vehicle model, EPA is currently 

carrying out vehicle testing at the Mercedes-Benz Tech Center located in Ann Arbor, 

Michigan. The AC17 test is run in a SC03-capable test cell with a road-speed fan and solar 

lamps, with ambient conditions of 77oF and 69 grains of water per pound of dry air (about 

50% relative humidity). 

As noted above, each segment (“A/C On” and “A/C Off”) of the AC17 test procedure 

has four phases: a UDDS preconditioning drive cycle; a 30 minute soak period with a 4MPH 

wind speed; a SC03 transient drive cycle; and a HWFET quasi-steady-state drive cycle. The 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-45 

test procedure is run twice: first with the A/C “On” and 850 W/m2solar load during the soak 

period and SC03 and HWFET drive cycles; and second with the A/C “Off” and no solar load. 

The incremental CO2 emissions related to fuel used to operate the A/C system is 

obtained by subtracting the CO2 emissions when the test procedure is run with the A/C “on” 

from the CO2 emissions when the test procedure is run with the A/C “off”. 

EPA ran AC17 tests on a paired set of the same vehicle model of different model years 

where an A/C system redesign has occurred between models. The purpose of the testing is to 

verify the appropriateness of the AC17 test as a method to estimate the relative efficiency of 

different A/C systems. The AC17 test was repeated between three to five times per vehicle to 

validate the test-to-test accuracy and feasibility of the AC17 test procedure. 

Although the AC17 testing program was ongoing at the time of this final rule, the test 

results of the first three sets of vehicles appear to reinforce the value of the AC17 test for the 

purposes of this rule. EPA testing thus far shows low test-to-test variability, we were able to 

quantify a CO2 increment between A/C “on” and A/C “off,” and we were able to establish a 

relative CO2 emissions difference between two A/C systems. 

5.1.3.7.2 Summary of Testing To Date 

Buick LaCrosse: 

The 2011 Buick LaCrosse AC17 test was repeated three times with A/C “off” and 

another three times with A/C “on.” The average A/C “off” CO2 emission result was 248 g/mi, 

and the average A/C “on” CO2 emission result was 267 g/mi, thus resulting in a difference 

(“delta”) of 19g/mi. These results show a very low test-to-test variability. Similarly, the 

average A/C “off” fuel economy was 33.5 mpg, and the average A/C “on” fuel economy was 

31.1 mpg, or about a 7% reduction in average fuel economy). 

EPA also tested a 2012 Buick LaCrosse that has a reduced reheat strategy and an A/C 

economy mode that will turn “on” and “off” the compressor. For the reduced reheat strategy 

A/C system on this vehicle, the average A/C “off” CO2 emission result was 221g/mi, and the 

average A/C “on” CO2 emission result was 255.8 g/mi; thus resulting in a difference (“delta”) 

of 35g/mi. The reduced reheat strategy (which can generate a CO2 credit of up to 1.5 g/mi for 

cars and 2.2 g/mi for trucks) should have resulted in a lower delta on the AC17 test, but in this 

case, the 2012 vehicle has an automatic start-stop feature, which turns off the engine when the 

vehicle is stopped and the A/C is off. This engine-off feature resulted in the 2012 vehicle 

having an A/C off CO2 emissions result that was 27 g/mi lower than the 2011 vehicle’s, which 

resulted in a larger A/C on-to-A/C off delta for the2012 vehicle. This does not necessarily 

indicate that the 2012 vehicle’s A/C system is less efficient than the 2011 system, but 

illustrates that AC17 is valid only when comparing vehicles with similar A/C and powertrain 

systems. When the 2012 vehicle was run with the A/C on and the “ECO” mode enabled 

(ECO mode is activated via a dash button, which when pressed, allows the engine start-stop 

feature to function under certain conditions while the A/C is on), the AC17 result was 241.6 

g/mi (14.2 g/mi lower than the non-ECO-mode result), resulting in a delta of 21 g/mi, which 

is much closer to the 19 g/mi result observed on the 2011 vehicle. In the case of the 2011 to 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-46 

2012 Buick LaCrosse comparison, an engineering analysis would be required to demonstrate 

that additional technology (or technologies) present on the vehicle result in improved 

efficiency of the A/C system. 

GM Silverado: 

The second pair of vehicles was a 2009 GM Silverado and a 2010 GM Sierra. Both 

vehicles have the same platform. The 2009 GM Silverado had a manual A/C system and the 

2010 GM Sierra had an A/C system with and automatic reheat reduction strategy. The 

Silverado had average A/C “off” CO2 emissions of 444 g/mi and fuel economy of 18.9 mpg, 

and average A/C “on” CO2 emissions of 481 g/mi and fuel economy of 17.4 mpg. This 

corresponds to a CO2 emissions delta of 37g/mi. Again, the test-to-test variability was low. 

The 2010 Sierra, had average A/C “off” CO2 emissions of 410 g/mi and fuel economy 

of 20.8 mpg, and average A/C “on” CO2 emissions of 452.2 g/mi and fuel economy of 18.5 

mpg, with low test-to-test variability. The CO2 emissions delta is thus 41 g/mi. 

Here, the AC17 test between the redesigns was closer in value. However, the “more 

efficient” 2010 system delta was still higher than the “less efficient” 2009 system, which on 

the surface, seems counterintuitive. However, the A/C system settings on the AC17 test for 

‘automatic’ and ‘manual’ systems are not equivalent, and comparing the results between these 

systems not valid for the purpose of demonstrating the effect of particular A/C technologies. 

Where possible, we expect that manufacturers will use the AC17 test for comparing the 

performance of vehicles with identical or substantially similar A/C control systems (manual 

vs automatic for example). 

Further analysis of the AC17 test results has shown that there are differences in the 

A/C system operation on automatic- and manually-controlled systems which explain why 

these differences can affect the A/C load applied to the engine, and the resulting CO2

emissions. EPA is examining data such as instrument panel temperature, compressor inlet 

temperature, coolant temperature, engine control algorithms, recirculation control to 

understand test result differences among similar vehicles, and trying to identify patterns 

which may improve our understanding of how AC17 test results and/or engineering analyses 

will be used to demonstrate the effectiveness of advanced A/C technologies. 

Chrysler 300 

The third pair of vehicles was the 2011 and 2012 Chrysler 300C with a 5.7L engine. 

The 2011 Chrysler 300C tested is a rear wheel drive vehicle, and the 2012 Chrysler 300C is 

an all wheel drive vehicle. The 2012 Chrysler 300C A/C system has a default to 

automatically recirculate air when the cabin temperature is 75oF. 

The 2011 Chrysler 300C had average A/C “off” CO2 emissions of 328.4 g/mi and fuel 

economy of 25.4 mpg, and average A/C “on” CO2 emissions of 358 g/mi and fuel economy of 

23.4 mpg. This corresponds to a CO2 emissions delta of 30 g/mi. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-47 

The 2012 Chrysler 300C had average A/C “off” CO2 emissions of 348.7 g/mi and fuel 

economy of 24.2 mpg, and average A/C “on” CO2 emissions of 378.5 g/mi and fuel economy 

of 22.1 mpg. The CO2 emissions delta is thus 30 g/mi. 

Again, for both vehicles, the test-to-test variability was low. Here, both systems have 

the same CO2 emissions delta. Possibly due the all wheel drive driveline, the A/C “off” CO2

emissions of the 2012 vehicle are higher than the 2011 vehicle, and therefore, the 2012 

vehicle has a comparatively more efficient A/C system operation than the 2011 vehicle, as 

both vehicles had the same delta of 30 g/mi CO2. 

In each of the three vehicle comparisons, there were confounding factors which 

prevented a direct assessment of the A/C technology alone: in the case of the GM trucks, it 

was automatic vs. manual control of the A/C system; in the case of the Buick LaCrosse, it was 

ECO mode vs. non-ECO mode; in the case of the Chrysler 300, it was rear wheel drive vs. all 

wheel drive. In follow-on testing, EPA will be testing vehicle pairs with A/C control 

strategies, powertrains, and drivelines which are as close to identical as possible. The 

preliminary EPA testing has shown that the AC17 test is capable of low test-to-test variability, 

and is suitable for evaluating the relative efficiency improvement of A/C technologies, when 

confounding factors are minimized. EPA also believes that in cases where comparison of the 

AC17 results do not directly demonstrate the effectiveness of a technology, the test results can 

still be useful within an engineering analysis for justifying the test methodology to determine 

A/C CO2 credits. EPA will analyze the data from the other vehicles as soon as they are 

collected, and in the future, EPA plans to collect more data on this test procedure and monitor 

the results of the reported results of AC17 starting in MY 2014. 

5.1.3.8 Options for Generating A/C Efficiency Credits 

In MYs 2014-2016, to demonstrate that a vehicle’s A/C system is delivering the 

efficiency benefits of the new technologies on the credit menu, instead of running the Idle 

Test , manufacturers will have the option to run the AC17 test procedure on each vehicle 

platform that incorporates the new, credit-generating technologies, and report the results from 

all 4 phases of the test to EPA. In addition to reporting the test results, EPA is requiring that 

manufacturers provide information about each test vehicle and its A/C system (e.g. vehicle 

class, model type, curb weight, engine size, transmission type, interior volume, climate 

control type, refrigerant type, compressor type, and evaporator/condenser characteristics). 

For model years 2017 and beyond, EPA is eliminating the A/C Idle Test and threshold 

requirement, and replacing it with the AC17 test. Thus, for MYs 2017 and beyond, 

manufacturers will run the AC17 test to quantify the A/C-related CO2 emissions on a limited 

number of vehicles. For model years 2017 through 2019, to access the A/C credit menu (i.e. 

to be eligible to generate A/C efficiency credits), manufacturers will report the results of 

AC17 test results on the required number of vehicles to EPA as part of the certification 

process. For model years 2020 and beyond, manufacturers will be required to demonstrate 

that the results (delta between A/C on and A/C off) of the new model year vehicle are lower 

than the results (delta) from a previously-tested ‘baseline’ vehicle. This comparison helps to 

validate that the A/C technologies for which credit is generated are actually reducing A/Crelated CO2 emissions, To receive the full amount of A/C credit generated from the menu, the 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-48 

difference between the new and baseline results should be equal to or greater than the sum of 

the menu-based credits for technologies present on the new vehicle. The baseline vehicle 

should be one with characteristics which are similar to the new vehicle, but not be equipped 

with A/C efficiency-improving technologies, or be equipped with those technologies without 

the technologies being implemented (e.g. forced cabin air recirculation). This baseline 

vehicle would be one previously tested by the manufacturer, where AC17 results were 

reported to the EPA, and will be from the same platform, but from a prior (re)design. 

We recognize that it may not be possible to find a baseline vehicle that is identical (in 

terms of powertrain characteristics, as well as aerodynamic and parasitic losses) to the new 

vehicle. The Alliance and others commented that any comparison to a prior redesign would 

be an unfair comparisons because of other changes on the vehicle that may have occurred. 

However, as we described in section 5.1.3 above and Chapter 5 of the EPA RIA, based on the 

simulated behavior of A/C systems in a variety of vehicles, we believe that the amount of fuel 

used to operate the A/C system is largely dependent on the compressor size and cooling 

capacity of the system, and much less dependent on engine displacement or efficiency. As 

such, we believe that it is technically appropriate for manufacturers to compare vehicles from 

different generations of redesign cycles in order to demonstrate that their efficient A/C 

systems can provide CO2 and fuel consumption reductions commensurate to the amount of 

credit that a particular vehicle can generate. 

For cases where a comparison of a baseline vehicle to a new vehicle with additional 

A/C technologies (which generate additional credits) is possible, the difference between the 

baseline and new AC17 test results must be equal to or greater than the amount of additional 

credit generated for the new vehicle for the vehicle to receive the full credit (based on the 

technology menu). In cases where the A/C technologies between the baseline and new 

vehicle are identical, we expect that the difference between the baseline and new vehicle 

AC17 test results should be near zero. If the difference in AC17 test results on this “same 

technology” comparison was greater than zero (i.e. the “new” vehicle had greater AC17 

emissions than the baseline vehicle), or in cases where no baseline comparison test result is 

available (e.g. a brand-new platform has been created), we will require that manufacturers 

submit an engineering analysis that justifies the generation of credits. The engineering 

analysis would describe why the new vehicle had higher AC17 results, or why a comparison 

to a baseline vehicle AC17 test result is neither available nor appropriate, and why the 

generation of credits in either case is justified 

However, starting in MY 2020, if the difference between the baseline and new AC17 

test results is less than the sum of the menu credit generated for the new technology (or 

technologies), and an engineering analysis cannot justify a higher-than-expected AC17 test 

result, partial credit can still be generated. A manufacturer can use the credit scaling factor in 

Equation 5-5, which is proportional to the ratio of the difference in the AC17 test results 

divided by the menu credit amount and can be applied to the new technology menu credits as 

follows:: 

Equation 5-5 – AC17 Credit Scaling Factor 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-49 

Credit Scaling Factor = (Baseline AC17 Result – New AC17 Result) ÷ Sum of New 

Technology Menu Credits

For MY 2017 (and optionally for MYs 2014-2016), the AC17 testing will first be 

required on the highest-production-volume configuration from each platform for which 

credits are generated. Because the new A/C test requires significant amount of time for each 

test (nearly 4 hours) and must be run in SC03-capable facilities, EPA believes that it is 

appropriate to limit the number of vehicles a manufacturer must test in any given model year 

by limiting the testing to no more than one vehicle from each platform for each model year. 

For the purpose of the AC17 test and generating efficiency credits, a single platform will be 

defined in a manner similar to that which EPA has used to define a “car line” (see 40 CFR 

§600.002-08), and reads as follows: 

“Platform” means a segment of an automobile manufacturer’s vehicle fleet in which 

the vehicles have a degree of commonality in construction (primarily in terms of body and , 

chassis design). Platform does not consider the model name, brand, marketing division, or 

level of decor or opulence, and is not generally distinguished by such characteristics as 

powertrain, roof line, number of doors, seats, or windows. A platform may include vehicles 

from various fuel economy classes, including both light-duty vehicles and light-duty 

trucks/medium-duty passenger vehicles. 

 

This definition was modified from the proposal based on comments received. In particular, 

we agree with the Alliance that vehicle powertrain is not a key characteristic of a platform for 

purposes of this rule, and the final definition of “platform” clarifies this. 

EPA recognizes that a vehicle manufacturer may only utilize one or two A/C system 

designs across many vehicle models within a platform, and it is not the intention of EPA that 

a manufacturer measure the performance of each A/C system design on each model within the 

platform, but simply that each A/C system design be tested on the highest expected sales 

volume configuration within each platform, as defined above. For the first year in which a 

manufacturer performs AC17 testing – either as required in model year 2017, or as an option 

to the Idle Test in model years 2014 through 2016 - what the manufacturer expects to be the 

highest sales volume configuration from a given platform will need to be tested. In 

subsequent model years, a unique A/C system design, where it exists and has not yet been 

tested in this program, would be tested, continuing each model year until all A/C system 

designs have been tested in their highest expected sales volume models. 

For the purpose of this rule, a “unique A/C system design” will be defined as one in 

which substantially-different A/C component designs or types and/or system control strategies 

are used (e.g. fixed-displacement vs. variable-displacement compressor, orifice tube vs. 

thermostatic expansion valve, manual vs. automatic climate control, single vs. dual 

evaporator, etc.). A/C system designs which have similar cooling capacity, component types, 

and control strategies, yet differ in terms of compressor pulley ratios or condenser or 

evaporator surface area will not be considered to be unique designs. The test results from one 

system design will apply to all design variants. EPA will require that manufacturers use good 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-50 

engineering judgment to identify the unique A/C system designs which will require AC17 

testing in subsequent model years. Manufacturers would indicate the basis for their 

engineering judgment at certification. 

Starting in MY 2017, for each model year in which a manufacturer is using the AC17 

test (including optionally in MYs 2014-16), no more than one vehicle from each platform will 

need to be tested on the AC17 test. A manufacturer may choose to perform replicate tests (to 

address concerns about test-to-test variability or to generate more robust data to support 

credits for later use), but data from a single test is acceptable, As long as the necessary AC17 

tests are performed each model year, the credits generated for all model configurations within 

a given platform can be carried over until there is a significant change in the platform design, 

at which point a new AC17 test on the highest sales volume configuration of the new platform 

design will be required. The following flowcharts in Figure 5-8 illustrate the process for 

determining the testing and reporting requirements for generating A/C efficiency credits. 

Figure 5-8 Process for A/C Efficiency Credit Generation: Model Years 2014 through 2025 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-51 

Menu Credits for 2014-

2016?, 2017-2019?, or 2020+?

Start Idle Test?, or 

AC17 test? 2014-2016 Run Idle Test on 

each vehicle and 

A/C system which 

is generating credits

Is Idle Test 

result > 

Threshold?

Idle

Credits are 

generated for 

vehicle according 

to menu values, 

and carry forward 

until A/C system is redesigned

No Calculate credit 

scaling factor 

(proportional)

Credits are 

generated for 

vehicle according 

to menu value * 

scaling factor

Yes

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

highest sales 

volume model for platform generating credits

Credits are 

generated for 

platform according to menu, and 

AC17 results and 

system description 

are reported to EPA

Initial model year?, or a 

subsequent model year test 

on unique A/C system?

AC17

Initial year

A

2017-2019

Is there a unique A/C 

system that differs 

substantially from 

prior AC17 testing? Credits for 

platform carry 

forward: no new 

AC17 test is 

required

Subsequent model year

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

unique A/C system 

on the highest 

sales volume 

model for platform 

generating credits

Yes

End

B

2020-2025

No
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-52 

Credits are 

generated for 

vehicle according 

to menu values

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

highest sales 

volume model for platform generating credits

Credits are 

generated for 

platform according 

to menu, and 

AC17 results and 

system description 

are reported to EPA

2017-2019 Initial model year?, 

or a subsequent model year 

test on unique A/C system? Is there a unique A/C 

system that differs 

substantially from prior AC17 testing? Credits for 

platform carry 

forward to 2019: 

no new AC17 test 

is required unless platform is 

redesigned

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

unique A/C system 

on the highest 

sales volume 

model for platform 

generating credits

End

Initial model year No Yes Subsequent model year

A
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-53 

B

Proportional 

credits are 

generated for 

vehicle according 

to menu value * 

scaling factor, and 

AC17 results and 

system description 

are reported to EPA

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

highest sales 

volume model for platform generating credits

Credits are 

generated for 

platform according to menu, and 

AC17 results and 

system description 

are reported to EPA

2020-2025 initial model year?, 

or a subsequent model year 

test on unique A/C system? Is there a unique A/C 

system that differs 

substantially from 

prior AC17 testing?

End

Run AC17 test (or 

multiple tests if 

COV is >= x%) on 

highest sales 

volume model for platform generating credits

Initial model year

Credits for 

platform carry 

forward to 2025: 

no new AC17 test 

is required unless platform is 

redesigned Subsequent model year No Yes

Compare 

AC17 result 

from new 

vehicle to 

baseline 

vehicle

Is the difference between 

baseline and new AC17 test 

results equal to or greater 

than the sum of applicable menu credits?

Can the higher-thanexpected AC17 result on 

new vehicle be justified 

with an engineering analysis? Prepare and submit and 

engineering analysis 

which explains the new AC17 result and 

justifies the full menu credit amount

Calculate credit 

scaling factor 

(proportional) for 

applicable menu credits

Yes No

No

Yes
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-54 

In addition, EPA is requiring that manufacturers provide detailed information about 

the A/C systems in vehicles tested, both baseline and new, as well as a plot with the interior 

temperature of both vehicles, to confirm that there is equivalent or better cooling system 

performance in the new vehicle configuration. EPA requires that interior temperature be 

measured at three locations: at the outlet of the center duct on the dash panel, and behind the 

driver and passenger seat headrests. For the headrest locations, the temperature measuring 

devices shall be nominally 30 millimeters behind the center of the headrest. 

5.1.3.9 A/C Efficiency Credits and Quantification of Credits 

EPA and NHTSA believe that it is possible to identify the A/C efficiency-improving 

components and control strategies most likely to be utilized by manufacturers and will 

continue to assign a CO2 credit and fuel consumption improvement value to each. In addition, 

the agencies recognize that to achieve the maximum efficiency benefit, some components can 

be used in conjunction with other components or control strategies. Therefore, the system 

efficiency synergies resulting from the grouping of three or more individual components are 

additive, and will qualify for a credit commensurate with their overall effect on A/C 

efficiency. A list of these technologies – and the amount of credit (and estimated fuel 

consumption improvement value derived from the credit) associated with each technology – is 

shown in Table 5-12. If more than one technology is utilized by a manufacturer for a given 

vehicle model, the A/C credits or fuel consumption improvement values can be added, but the 

maximum credit and fuel consumption improvement value possible is limited to 5.0 g/mi for 

cars (equivalent to 0.000563 gal/mi) and 7.2 g/mi (equivalent to 0.000810 gal/mi) for trucks. 

In the proposal, NHTSA sought comment on setting fuel specific conversion factors. 

The agencies did not receive any comments on the use of fuel specific conversion factors. 

The agencies believe that since both the CAFE target curves and the AC credits are derived 

using the gasoline conversion factor, it is appropriate to use the gasoline conversion factor for 

all fuels. If different conversion factors were used based on the type of fuel, there would be 

misalignment between the A/C compliance credits, which would be based on the type of fuel 

the vehicle uses, and the stringency of the target curves, which are based on gasoline. 

Therefore, the agencies are finalizing the use of the gasoline conversion factor to determine 

A/C improvement values for all vehicles. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-55 

Table 5-12 Efficiency-Improving A/C Technologies and Credits 

Technology Description 

A/C CO2

Emission 

and Fuel 

Consumption 

Reduction

Car A/C 

Credit and 

Adjustment

(g/mi CO2

and gal/mi)

Truck A/C 

Credit and 

Improvement 

(g/mi CO2 

and gal/mi)*

Reduced reheat, with externallycontrolled, variable-displacement 

compressor

30% 1.5 (30% of 

5.0 g/mi 

impact) / 

0.000169

2.2 (30% of 

7.2 g/mi 

impact) / 

0.000248

Reduced reheat, with externallycontrolled, fixed-displacement or 

pneumatic variable displacement 

compressor

20% 1.0 / 

0.000113

1.4 / 

0.000158

Default to recirculated air with closedloop control of the air supply (sensor 

feedback to control interior air quality) 

whenever the outside ambient 

temperature is 75 °F or higher (although 

deviations from this temperature are 

allowed if accompanied by an 

engineering analysis)

30% 1.5 / 

0.000169

2.2 / 

0.000248

Default to recirculated air with open-loop 

control of the air supply (no sensor 

feedback) whenever the outside ambient 

temperature is 75 °F or higher (although 

deviations from this temperature are 

allowed if accompanied by an 

engineering analysis)

20% 1.0 / 

0.000113

1.4 / 

0.000158

Blower motor control which limit wasted 

electrical energy (e.g. pulsewidth 

modulated power controller)

15% 0.8 / 

0.000090

1.1 / 

0.000124

Internal heat exchanger (or suction line 

heat exchanger)

20% 1.0 / 

0.000113

1.4 / 

0.000158

Improved evaporators and condensers 

(with engineering analysis on each 

component indicating a COP 

improvement greater than 10%, when 

compared to previous design)

20% 1.0 / 

0.000113

1.4 / 

0.000158

Oil Separator (internal or external to 

compressor)

10% 0.5 / 

0.000056

0.7 / 

0.000079

* This factor is a gasoline conversion from CO2 using 8887 g/CO2 per mpg, NHTSA 

will set this constant independent of fuel. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-56 

Even though EPA is finalizing a design-based A/C credit program that introduces 

some minor revisions to the menu values from the MYs 2012-2016 rule, EPA continues to 

believe that a full performance-based test procedure is the most appropriate way for 

quantifying A/C credits. Performance-based procedures place no limits on the technological 

choices made by a manufacturer to improve efficiency. Ideally, performance based standards 

would be the most appropriate method of quantifying A/C credits, however there are many 

challenges to accurately quantifying a small incremental decrease in emissions and fuel 

consumption compared to a relatively large tailpipe emissions and fuel consumption rate. For 

example, it would be nearly impossible to distinguish and measure the impact of a 0.5g/mi 

improvement in tailpipe emissions due to an improved oil separator system incremental to a 

tailpipe 250g/mi test procedure result. The 0.5 g/mi increment would be well within the noise 

of a test measurement or test-to-test variability. Even if a number of the technologies were to 

be packaged together to account for a 5.0g/mi improvement, this is still only 2% of the 

tailpipe emissions value and still may be within test-to-test variability. 

The other major challenge to quantifying credits is that it is not practical (from a 

compliance standpoint) to measure the CO2 emissions from a vehicle with and without a 

series of technologies that include hardware and software integrated in a complex fashion. 

This could only be done with an “A” to “B” comparison where the “B” condition includes the 

technologies and the “A” condition does not. Such A to B test comparisons require the 

manufacture of a prototype vehicle that is in all respects identical to the certified vehicle with 

the exception that the technologies being evaluated are removed. This would be impossible to 

do for every vehicle certified for a fuel economy test. It would even be prohibitive for a 

single vehicle demonstration for each manufacturer. This might only be practical on a single 

vehicle research level program as was done in the IMAC study. The comparison of the AC17 

test result to AC17 result for the baseline vehicle with the older technology will likely show a 

small change in emissions, based on the vehicle simulation results presented above. A more 

direct comparison of individual technologies is likely to give even more accurate 

quantification of credits such that the menu may no longer be required. 

The IMAC study successfully demonstrated that there are methods by which the 

efficacy of technologies can be measured. In the IMAC study, the efficiency of A/C 

components were measured on a test bench where the conditions can be precisely controlled. 

Test bench measurements are, by their nature, much more repeatable than chassis 

dynamometer tests. They can also easily be used to do A to B comparisons of technology 

effectiveness since components can be relatively easily swapped out. The limitations of test 

bench measurements primarily lie in the fact that they cannot capture the impact of the 

component integration into the vehicle. The test bench only measures the efficiency of the 

A/C components, it cannot account for the controls strategy (for example), such as forced 

recirculation, not defaulting to reheat, and smart cycling of fixed displacement compressors. 

Another disadvantage of test benches are that there are few such facilities available in the 

United States and typical OEMs do not possess such extensive test benches as they do not 

manufacture A/C components. 

One option to circumvent the limitations of both the test bench and the chassis tests 

are to merge the two in a combined test procedure that will utilize the strengths of each to 

supplement the weaknesses of the other. The test bench can generate the A to B comparison 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-57 

portion of the credit on the hardware changes, while the chassis test generates the A to B 

comparison of the (software) controls strategy changes. 

An A/C test bench typically measures the efficiency of a system by measuring its 

Coefficient of Performance (COP). The COP of a heat pump is the ratio of the change in heat 

at the output to the supplied work (also equivalent to the SEER seasonal energy efficiency 

ratio rating on a residential A/C unit).p

 The IMAC procedure employed the SAE procedure 

J2765 in order to bench test systems in a fashion that reflects national average A/C usage. 

This test procedure could be used to generate the efficiency of any production A/C system. 

The challenge lies in the comparison to the baseline “A” system for the A to B comparison. 

This could be done either with a defined hardware baseline system or a typical baseline COP 

value agreed upon by the industry. 

Combining the bench test together with a chassis test requires a model, simulation, or 

some calculation procedure (algorithm) to convert the test bench results to fuel economy and 

GHG emissions. There are a number of options for this model. The Lifecycle Climate 

Performance or LCCP model (also known as SAE J2766), developed by General Motors in 

partnership with SAE, NREL, EPA, is one such model, and was utilized for the IMAC 

project. While the LCCP model took into account many factors concerning lifecycle 

emissions and fuel use (including the energy needed to manufacture a particular refrigerant), 

it may be possible to employ a portion that model, and only discern the effect of the A/C 

system efficiency of annualized fuel use due to A/C operation. As updates to the LCCP 

model occur, EPA will evaluate the appropriateness of using such tools to quantify the effect 

of efficiency-improving A/C technologies. Another option is for the test bench to produce 

charts like the one in Figure 5-4. This can then be used as an input into EPA’s vehicle 

simulation tool. Whatever the method, such a series of models can convert a system COP into 

a change in fuel economy and CO2 emissions from the hardware changes in an A/C system. 

The controls strategy changes in the menu will have to be measured with an A to B 

comparison on the chassis dynamometer test procedure described above. To do this, the 

manufacturer would test a vehicle with a baseline controls strategy compared with a modified 

more efficient strategy. Though EPA has not yet conducted a test program to test the 

feasibility of this concept, combining the results from the bench and dynamometer tests 

should give a quantitative assessment of the credits from an improved A/C system compared 

to a baseline system. Such an approach could be used in a manufacturer’s engineering 

analysis submission, to demonstrate effectiveness of a technology (or technologies) in the 

absence of a of a baseline vehicle test. 

The Alliance and others commented that the bench testing methodology is too 

complex and costly (and thus impractical) to employ as a compliance mechanism. The 

agencies tend to agree with this assessment. Due to the relative complexity (and expense) of a 

 

p

 According to the second law of thermodynamics, the COP of a real heat pump system is limited to the Carnot 

cycle efficiency, which is the ratio of the low Temperature to the difference between the high and low 

temperatures (in Kelvin). 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-58 

bench test and model demonstration, it would be practical for a manufacturer to do this testing 

for only a small number of vehicle and A/C configurations in any given year. The EPA has 

met with a few manufacturers and also received comments regarding test vehicle selection, 

and they have informed EPA that on any single vehicle platform, the A/C systems usually 

share similar configurations. Most full line manufacturers only have a handful of vehicle 

platforms (in order to save on engineering and manufacturing costs). Therefore, this 

compliance demonstration and engineering analysis should only have to be conducted 

infrequently on a brand-new vehicle platform or A/C system design. AS described above, 

based on the limited number of platforms and the relative infrequency of platform redesigns, 

EPA expects that any manufacturer may ultimately only be required to do a compliance 

demonstration and engineering analysis of A/C credits for a given platform perhaps one or 

two times, depending on the number of unique A/C system designs used on the various 

models within that platform in order to generate credits. 

One clarification that is being added to this final rule is to note that air conditioner 

efficiency is an “off-cycle” technology. It is thus appropriate for a manufacturer to employ 

the standard off-cycle credit approval process described in Section II.F and III,C of the 

preamble, as well as in the MYs 2012-2016 rule if the manufacturer believes it can 

demonstrate that a greater amount of credit is justified. Utilization of bench tests in 

combination with dynamometer tests and simulations (similar to the SAE IMAC study) would 

be an appropriate alternate method of demonstrating and quantifying technology credits (up to 

the maximum level of credits allowed for A/C efficiency). A manufacturer can choose this 

method even for technologies that are not currently included in the menu starting as early as 

model year 2012 (2017 for CAFE). 

5.1.4 Air Conditioner System Costs 

A/C system technologies include improved hoses, connectors and seals for leakage 

control. They also include improved compressors, expansion valves, heat exchangers and the 

control of these components for the purposes of improving tailpipe CO2 emissions and fuel 

economy as a result of A/C use. The GHG and fuel economy effectiveness is unchanged 

from estimates used for 2016 model year vehicles in the 2012-2016 final rule. 

In the MYs 2012-2016 rule, EPA estimated the direct manufacturing costs (DMC) of 

direct/leakage reduction A/C controls at $17 (2007$) and of indirect/efficiency improvement 

controls at $53 (2007$). These DMCs become $18 (2010$) and $55 (2010$), respectively, for 

this analysis. EPA continues to consider those DMCs to be applicable in the 2012MY and 

continues to consider the technologies to be on the flat portion of the learning curve. For this 

rule, the 2012-2016 rule technologies represent the reference case in terms of controls and 

costs. We have applied to those DMCs low complexity ICMs of 1.24 through 2018 then 1.19 

thereafter to generate the indirect costs for this reference case. The resultant reference case 

costs are shown in Table 5-15.

New for this rule, and consistent with the proposal, are additional costs for 

indirect/efficiency improvement control as those 2012-2016 MY vintage systems penetrate to 

the entire fleet. In addition, as new costs are assumed which are associated with the 

alternative refrigerant—both the alternative refrigerant itself and the system changes to 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-59 

accommodate that refrigerant. For the first of these—indirect controls—the agencies have 

estimated the DMC at $15 (2010$) applicable in the 2017MY. The agencies consider this 

technology to be on the flat portion of the learning curve and have used a low complexity 

ICM of 1.24 through 2018 then 1.19 thereafter. For the alternative refrigerant, the agencies 

have estimated a DMC of $67 (2010$) applicable in the 2016MY. The agencies consider this 

technology to be on the steep portion of the learning curve because it is only now starting to 

be used in a limited number of vehicles. For this technology, the agencies have used a low 

complexity ICM of 1.24 through 2022 then 1.19 thereafter. For the alternative refrigerant 

system costs (i.e., the hardware changes necessary to accommodate the alternative 

refrigerant), the agencies have estimated a DMC of $15 applicable in the 2016MY The 

agencies consider this technology to be on the flat portion of the learning curve and have used 

a low complexity ICM of 1.24 through 2018 then 1.19 thereafter. The resultant control case 

costs are shown in Table 5-16. 

Note that these costs are expected to be incurred consistent with our estimated ramp 

up of manufacturer use of A/C credits. For example, the direct credit for low GWP 

refrigerant use is 13.8 g/mi in MYs 2017-2025, but we estimate that the average credit earned 

by manufacturers would be 5.5 g/mi on cars in MY 2018 and 5.8 g/mi on trucks in that MY. 

Table 5-13 shows the credits by MY as we estimate they will be used for both cars and truck. 

Table 5-14 then shows how we have used these estimated credits to scale A/C-related costs by 

MY for both cars and trucks. The percentages shown in Table 5-14 are included in the costs 

shown in Table 5-15 and Table 5-16. 

The total A/C related costs are shown in Table 5-17.

Table 5-13 Projected Average Estimated Use of A/C Credits in g/mi 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car (g/mi)

Direct 

(Leakage) 

Credit if 

All R-134a 

AC

5.4 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3

Direct 

Credit for 

Low GWP 

AC

0.0 2.8 5.5 8.3 11.0 13.8 13.8 13.8 13.8 13.8

Indirect 

Credit 4.8 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Total Credit 10.2 12.8 14.3 15.8 17.3 18.8 18.8 18.8 18.8 18.8

Truck (g/mi)

Direct 

(Leakage) 

Credit if 

All R-134a 

AC

6.6 7.0 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8

Direct 

Credit for 

Low GWP 

AC

0.0 0.0 5.8 10.3 13.8 17.2 17.2 17.2 17.2 17.2

Indirect 

Credit 4.8 5.0 6.5 7.2 7.2 7.2 7.2 7.2 7.2 7.2
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-60 

Total Credit 11.5 12.0 17.5 20.6 22.5 24.4 24.4 24.4 24.4 24.4

Table 5-14 Scaling of A/C Costs to Estimated Use of Credits 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

2012-2016 

Rule (reference 

case)

C

A

R

Leakage 

Reducti

on

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

5.4/6.3 

=85%

Low 

GWP 

Refriger

ant & 

Hardwar

e

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

0.0/13.8 

=0%

Efficien

cy 

Improve

ments

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

4.8/5.0 

=97%

T

R

U

C

K

Leakage 

Reducti

on

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

6.6/7.8 

=85%

Low 

GWP 

Refriger

ant

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

0.0/17.2 

=0%

Efficien

cy 

Improve

ments

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

4.8/7.2 

=47%

2017-2025 

Rule(control 

case)

C

A

R

Leakage 

Reducti

on

1-85%

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

Low 

GWP 

Refriger

ant & 

Hardwar

e

2.8/13.8 

=20%

5.5/13.8 

=40%

8.3/13.8 

=60%

11.0/13.

8 =80%

13.8/13.

8 =100%

13.8/13.

8 =100%

13.8/13.

8 =100%

13.8/13.

8 =100%

13.8/13.

8 =100%

Efficien

cy 

Improve

ments

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

1-97% 

=3%

T

R

U

C

K

Leakage 

Reducti

on

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

1-85% 

=15%

Low 

GWP 

Refriger

ant

0.0/17.2 

=0%

5.8/17.2 

=34%

10.3/17.

2 =60%

13.8/17.

2 =80%

17.2/17.

2 =100%

17.2/17.

2 =100%

17.2/17.

2 =100%

17.2/17.

2 =100%

17.2/17.

2 =100%

Efficien

cy 

Improve

ments

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%

1-47% 

=53%
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-61 

Table 5-15 Costs of A/C Controls in the Reference Case (2012-2016 Final Rule) (2010$)

Car/

Truck

Cost 

type A/C Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car

DMC Leakage reduction $13 $13 $13 $13 $12 $12 $12 $12 $11

DMC Efficiency 

improvement $46 $45 $44 $43 $42 $41 $41 $40 $39

IC Leakage reduction $4 $4 $3 $3 $3 $3 $3 $3 $3

IC Efficiency 

improvement $13 $13 $10 $10 $10 $10 $10 $10 $10

TC Leakage reduction $17 $17 $16 $16 $15 $15 $15 $15 $14

TC Efficiency 

improvement $59 $58 $54 $53 $52 $52 $51 $50 $49

Truck

DMC Leakage reduction $13 $13 $13 $13 $12 $12 $12 $12 $11

DMC Efficiency 

improvement $32 $31 $31 $30 $29 $29 $28 $28 $27

IC Leakage reduction $4 $4 $3 $3 $3 $3 $3 $3 $3

IC Efficiency 

improvement $9 $9 $7 $7 $7 $7 $7 $7 $7

TC Leakage reduction $17 $17 $16 $16 $15 $15 $15 $15 $14

TC Efficiency 

improvement $41 $40 $38 $37 $36 $36 $35 $35 $34

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 5-16 Costs of A/C Controls in the Control Case (2017-2025 Rule) (2010$)

Car/

Truck

Cost 

type A/C Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car

DMC Leakage reduction $2 $2 $2 $2 $2 $2 $2 $2 $2

DMC Low GWP 

refrigerant $13 $22 $32 $34 $42 $41 $39 $38 $37

DMC

Low GWP 

refrigerant 

hardware

$3 $6 $9 $11 $14 $13 $13 $13 $13

DMC Efficiency 

improvement $1 $1 $1 $1 $1 $1 $1 $1 $1

IC Leakage reduction $1 $1 $1 $1 $1 $1 $1 $1 $1

IC Low GWP 

refrigerant $3 $6 $10 $13 $16 $16 $13 $13 $13

IC

Low GWP 

refrigerant 

hardware

$1 $1 $2 $2 $3 $3 $3 $3 $3

IC Efficiency 

improvement $0 $0 $0 $0 $0 $0 $0 $0 $0

TC Leakage reduction $3 $3 $3 $3 $3 $3 $3 $3 $3

TC Low GWP 

refrigerant $17 $28 $42 $47 $58 $57 $52 $51 $50

TC

Low GWP 

refrigerant 

hardware

$4 $7 $10 $14 $17 $16 $16 $16 $16

TC Efficiency 

improvement $2 $2 $2 $2 $2 $2 $2 $2 $2

Truck

DMC Leakage reduction $1 $2 $2 $2 $2 $2 $2 $2 $2

DMC Low GWP 

refrigerant $0 $18 $32 $34 $42 $41 $39 $38 $37
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-62 

DMC

Low GWP 

refrigerant 

hardware

$0 $5 $9 $11 $14 $13 $13 $13 $13

DMC Efficiency 

improvement $1 $11 $15 $15 $14 $14 $14 $13 $13

IC Leakage reduction $0 $1 $1 $1 $1 $1 $1 $1 $1

IC Low GWP 

refrigerant $0 $5 $10 $13 $16 $16 $13 $13 $13

IC

Low GWP 

refrigerant 

hardware

$0 $1 $2 $2 $3 $3 $3 $3 $3

IC Efficiency 

improvement $0 $3 $3 $3 $3 $3 $3 $3 $3

TC Leakage reduction $1 $3 $3 $3 $3 $3 $3 $3 $3

TC Low GWP 

refrigerant $0 $24 $42 $47 $58 $57 $52 $51 $50

TC

Low GWP 

refrigerant 

hardware

$0 $6 $10 $14 $17 $16 $16 $16 $16

TC Efficiency 

improvement $1 $14 $18 $18 $18 $17 $17 $17 $17

DMC=Direct manufacturing cost; IC=Indirect cost; TC=Total cost

Table 5-17 Total Costs for A/C Control Used in This Final Rule (2010$)

Car/

Truck Cost type Case 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car

TC Reference $76 $75 $70 $69 $68 $67 $66 $65 $64

TC Control $25 $40 $57 $65 $79 $77 $72 $71 $69

TC Both $101 $115 $127 $134 $147 $144 $138 $135 $133

Truck

TC Reference $58 $57 $54 $53 $52 $51 $50 $49 $49

TC Control $2 $46 $73 $82 $95 $93 $88 $86 $84

TC Both $60 $103 $127 $134 $147 $144 $138 $135 $133

Fleet TC Both $86 $111 $127 $134 $147 $144 $138 $135 $133

TC=Total cost

The agencies received no public comments on A/C costs, though the EPA did have 

confidential meetings with alternative refrigerant suppliers. Due to the confidential nature of 

the information shared, the costs and supply discussions from these meetings are not relied 

upon to determine the costs in the tables above. 

5.2 Off-Cycle Technologies and Credits 

EPA employs a five-cycle test methodology to evaluate fuel economy for fuel 

economy labeling purposes. For GHG and CAFE compliance, EPA uses the established twocycle (city, highway or correspondingly FTP, HFET) test methodology. EPA recognizes that 

there are technologies that provide real-world GHG benefits to consumers, but that the benefit 

of some of these technologies is not represented on the two-cycle test. Therefore, EPA is 

continuing the off-cycle credit program from the MYs 2012-2016 rule with some changes 

such as providing manufacturers with a list of pre-approved technologies for which EPA can 

quantify a default value that would apply unless the manufacturer demonstrates to EPA that a 

different value for its technology is appropriate. This list is similar to the menu driven 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-63 

approach described in the previous section on A/C efficiency credits. In meetings with 

vehicle manufacturers prior to the proposal, the EPA received comments that the regulatory 

process for generating off-cycle credits was too cumbersome to utilize frequently if at all, and 

that the burden of proof to demonstrate a small incremental improvement on top of a large 

tailpipe measurement was impractical. This is similar to the argument described above for 

quantifying air conditioner improvements. These same manufacturers believed that such a 

process could stifle innovation and fuel efficient technologies from penetrating into the 

vehicle fleet. EPA generally agrees with these comments, and proposed and is finalizing a 

menu with a number of technologies that the agency believes will show real-world CO2 and 

fuel economy benefits which can be reasonably quantified a priori. The estimates of these 

credits were largely determined from research, analysis and simulations, rather from full 

vehicle testing, which would have been cost and time prohibitive. However, actual vehicle 

testing was used to either support or refine the credit estimates in cases where it was available. 

In the proposal, the agencies requested comment on all aspects of the off-cycle credit 

menu technologies and derivations. EPA and NHTSA received a number of comments and, 

in addition, several stakeholders requested meetings and met with the agencies including 

Denso, Enhanced Protective Glass Automotive Association (EPGAA), ICCT and Honda. 

Overall, there was general support for the menu based approach and the technologies 

included in the proposed list, but there were also suggestions to re-evaluate the definition of 

some of the technologies included in the menu, the calculation and/or test methods for 

determining the credits values, and recommendations to periodically re-evaluate the menu as 

technologies emerge or become pervasive. 

In the proposed off-cycle credit menu, credit values were fixed for most of the 

technologies while others values were based on a step-function (e.g., x amount of credit for y 

amount of reduction or savings) on the off-cycle credit list. In response to the proposal, the 

agencies received comments requesting the use of a scalable credit value approach rather than 

a fixed values or step-function derived values for high efficiency exterior lighting, waste heat 

recovery (formerly termed “engine heat recovery”), solar panels (formerly termed “solar roof 

panels”), and active aerodynamics. After much evaluation of and in response to these 

comments, the agencies have revised the credit determination approaches for these 

technologies by allowing scalable off-cycle credit values derived from a specific technologies 

implementation affecting their relative reductions or savings. However, a by-product of 

moving to this calculation strategy is the deviation, in some cases, from the proposed 

methodology of subtracting a technology’s 2-cycle test procedure benefits from the benefits 

determined on the 5-cycle test procedure as the agencies, in their evaluation, determine this 

approach was not easily or accurately to scalable. As a proxy, EPA employed a vehicle 

simulation tool, and applied varying load reduction values to determine benefits shown during 

5-cycle testing, where applicable, to develop tables and/or curves to provide sound data 

properly scale credit values. This revised calculation approach is discussed in greater detail 

for each applicable technology in the following sections. 

Another complication that arises from scaling, is that extremely small credit values 

can now be quantified. Although we are allowing scaling of the credits, we cannot accept a 

request or grant credit for any level of credit less than 0.05 g/mi CO2. As proposed and 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-64 

finalized, the agencies will be reporting CO2 values rounded to the nearest tenth of 

gram/mile, as a result, any reported values below 0.05 g/mi of CO2 would be rounded down 

to zero. Therefore, only credit values equal to 0.05 g/mi or greater will be accepted and 

approved for any credit requested as part of the off-cycle credit program (e.g., scalable or 

fixed; via the pre-defined technology list or alternate method approval process).. 

Some commenters suggested that technologies should be added to the list such as high 

efficiency alternators (Alliance, Denso, VW, Porsche, Ford), electric cooling fans (Bosch), 

HVAC eco-modes, transmission cooler bypass valves (Ford), navigation systems (Garmin), 

engine block heaters (Honda) and an “integral” approach utilizing a combination of 

technologies (Global Automakers). Daimler commented that the agencies should provide 

“congestion mitigation credits based on crash avoidance technologies.” 

Conversely, some commenters were opposed to adding any technologies to the menu 

(CBD) and others suggested some of the proposed values should be re-evaluated (ICCT) or 

that the values should be based on real test data, not simulation modeling (NRDC). 

In most cases, there was either insufficient supporting data, dependence on unique, 

manufacturer-specific designs or implementation, or dependence on driver interaction and 

usage that led to our decision not to include these technologies within the menu of off-cycle 

technologies. These comments are discussed in more detail in the Preamble Section II.F. 

Finally, the agencies carefully assessed all of the comments and conducted additional 

analysis in response to the comments, as well as to support the agencies’ ongoing work. The 

resulting adjustments to off-cycle credit menu values that are being finalized are detailed in 

the following sections. 

In addition to comments about the individual technologies, the agencies received a 

number of comments on the proposed minimum penetration thresholds, the proposed cap on 

the amount of menu based off-cycle credit that can be applied, and suggestions to allow the 

proposed menu and credit values to be applied to MY2012-2016 vehicles. These comments, 

and EPA’s response to them, are discussed in preamble sections II.F. and III.C.5. 

5.2.1 Reducing or Offsetting Electrical Loads 

The EPA test cycles do not require that all electrical components be turned on during 

testing. Headlights, for example, are always turned off during testing; including daytime 

running lights (DRLs). Turning the headlights on during normal driving will add an 

additional load on the vehicle’s electrical system and will affect fuel economy. More efficient 

lighting, electrical systems or technologies that offset electrical loads will have a real world 

impact on fuel economy but are not captured in the EPA test cycles. Therefore, the EPA 

believes that technologies that reduce or offset electrical loads related to the operation of the 

vehicle or safety deserve consideration for off cycle credits. 

To evaluate technologies that reduce or offset electrical loads, the EPA conducted an 

analysis of the reduction in emissions corresponding to a general reduction of electrical 

demand in a vehicle. Using EPA’s vehicle simulation tool described in Chapter 2 of EPA’s 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-65 

RIA, the agency evaluated the change in fuel consumption for a 100W reduction in electrical 

load for a typically configured vehicle. The impact of this load reduction was modeled on 

both the combined FTP/Highway cycles (2-cycle), and over the 5-cycle drive tests. The 

results of this analysis form the basis for a consistent methodology that the EPA applied to 

several technologies to determine the appropriate off-cycle credits for those technologies. In 

the NPRM, a single conversion factor was proposed to convert Wattage to the CO2 and fuel 

consumption benefit. For the final rule, the agencies have determined that this conversion 

should depend on the technology. Based on this determination, the solar energy capture and 

high efficiency exterior lighting credit are now calculated differently from the waste heat 

recovery credit. The method by which each technology converts and uses electrical energy is 

discussed below. 

5.2.2 Waste Heat Recovery 

The combustion process that powers most of today’s vehicles results in a significant 

amount of exhaust heat. Most of this heat leaves the engine in the form of waste hot exhaust 

gasses which are expelled from the vehicle through the exhaust system, or through hot coolant 

which that transfers heat from the engine through the radiator to the atmosphere. Recapturing 

some portion of this wasted heat energy and using it to offset the electrical requirements of 

the vehicle will lead to improved fuel efficiency. 

Regardless of the design of the heat recovery system, whether it is exhaust or coolant 

based, the EPA assumes that any recovered energy will be in the form of electricity and will 

be used to recharge the vehicle’s battery (primarily for HEVs or PHEVs). This is consistent 

with currently proposed waste heat recovery system designs. The GHG and fuel economy 

benefit of generating a Watt of energy is estimated though a full vehicle simulation analysis. 

For the vehicle simulation, EPA assumed that high-efficiency alternators will be 

prevalent in most vehicles within the MY2017-2025 timeframe of this rule; thus, the 

simulation includes a high-efficiency alternator. Figure 5-4 below shows a sample efficiency 

map of a high-efficiency alternator. Based on this map (used in the proposal for waste heat 

recovery), the global average alternator efficiency is 65%. For this final rule, in order to be 

consistent with the analysis conducted by Ricardo to inform the efficiency of powertrain and 

vehicle technologies,39 EPA used a global efficiency of 70% (from high efficiency alternators) 

for use in its modeling calculations as presented below.
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-66 

Figure 5-9: Alternator efficiency map (Delco-Remy, 200840) 

Table 5-18 below shows the results of the revised simulation using 70% efficiency for 

four vehicle classes. Reducing the electrical load on a vehicle by 100W will result in an 

average of 2.5 g/mile reduction in CO2 emissions over the course of a combined 

FTP/Highway test cycle, or 3.2 g/mile over a 5-cycle test. A 100W reduction in electrical 

load yields a reduction in required engine power of roughly 0.15 kW (=0.1 kW / 65%), or 1-

2% over the FTP/HWFE test cycles. 

Table 5-18: Simulated GHG reduction benefits of 100W reduction in electrical load over FTP/HW and 5-

cycle tests 

Driving Cycle Electrical Load

Small Car

[g/mile]

MidSize Car 

[g/mile]

Large Car 

[g/mile]

Pick-up 

Truck 

[g/mile]

Average*

[g/mile]

FTP/Highway

100W Load Reduction 156.8 187.7 246.5 416.6

Base 154.2 185.5 244.1 413.9

2-Cycle Difference 2.5 2.2 2.4 2.7 2.5

5-Cycle

100W Load Reduction 217.8 256.9 331 544.5

Base 214.6 254.1 327.9 541.1

5-Cycle Difference 3.2 2.8 3.1 3.4 3.2

5-Cycle/2-Cycle 

Difference 0.7 0.6 0.6 0.7 0.7

* based on a sales average 

To determine the off-cycle benefit of certain 100W electrical load reduction 

technologies, the benefit of the technology on the FTP/Highway cycles (2-cycle test) is

subtracted from the benefit of the technology on the 5-cycle test. This determines the actual 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-67 

benefit of the technology not realized in the 2-cycle test methodology and in this case is 3.2 

g/mi minus 2.5 g/mi, or 0.7 g/mi.q 

We received two comments on this approach. The International Council on Clean 

Transportation (ICCT) commented that they felt it is inappropriate to subtract 5-cycle benefits 

from 2-cycle benefits for waste heat as well as other technologies (discussed below). Instead, 

the ICCT suggested that the 2-cycle percentage benefits should be used for the load reduction 

estimate. The Alliance commented in support of the approach of subtracting the 2-cycle 

benefits from the 5-cycle benefits. However, they also wanted to have the technologies that 

use the load reduction estimate to be scalable rather than as a single value (i.e., 0.7 g/mi CO2 

credit per 100 watts reduced). Other commenters shared the desire for more scalable credits 

for this and other technologies. While these comments apply to all of the electrical load 

technologies, it is fitting to discuss them here first. 

Regarding subtraction of the 2-cycle from the 5-cycle benefits, the ICCT did not feel 

the method of subtracting the 2-cycle benefits from the 5-cycle benefits was appropriate. 

Instead, they recommended we use the 2-cycle percentage benefits to estimate any electrical 

load reduction that does not occur on the test cycles. Supplemental comments from the 

Alliance identified the inherent contradiction of ICCT’s assertions to have “credits properly 

reflect actual in-use reductions” while advocating for the use of 2-cycle testing, which 

typically has lower electrical loads than the 5-cycle tests and the real-world. The agencies 

agree with the supplemental comments from the Alliance and, therefore, we have decided not 

to subtract the 2-cycle benefits from the 5-cycle benefits to develop a base load reduction 

estimate and off-cycle credits. 

We do agree with ICCT that utilizing the difference between 5-cycle and 2-cycle 

benefits for all of the off cycle technologies that affected electrical loads may not be 

appropriate. Based on this comment, we are only applying this methodology to waste heat 

recovery as this technology will have 2-cycle benefits. Accordingly, the other load reduction 

technologies, high efficiency exterior lighting and solar panels should have an alternate 

method of calculation (since they will not have 2-cycle benefits) which is described in greater 

detail in sections below. 

We agree with the comments from the Alliance to allow scaling and are using the base 

load reduction estimate directly for calculating the waste heat recovery credit. Accordingly, 

we have developed the table and figure below that will allow for appropriate scaling of the 

credit based on the load reduced. Therefore, we are finalizing a scalable approach using the 

table and figure below based on these comments. 

Table 5-19 Estimated electrical load reduction estimate and corresponding credit values. 

 

qHowever, other technologies (for example, lighting and solar panels) providing benefits off-cycle that cannot be 

measured on either the 5-cycle test nor the 2-cycle test be used as the credits without subtracting a 2-cycle value. 

An example of this is provided later [WHERE?]. 
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5-68 

Figure 5-10 Graph of estimated electrical load reduction and CO2 credit (g/mile) 

 We received comments from Honda requesting clarification on whether the waste heat 

recovery value is the peak value or the average value over the test cycle. Honda 

recommended that it should be based on the average value over a 5-cycle test. We agree that 

this requires clarification and are clarifying the waste heat recovery credit is based on the 

average value over 5-cycle testing. 

 Honda also requested the definition for waste heat recovery to be expanded for 

conversion to mechanical and thermal energy in addition to “electrical energy.” The 

conversion of waste heat to thermal energy is already covered elsewhere under the active 

engine and active transmission warm-up so we believe the additional inclusion of thermal 

energy conversion as part of waste heat recovery is not necessary. The conversion of waste 

heat to mechanical energy is more difficult to quantify since we do not have any data of these 
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5-69 

systems on current vehicle applications. Therefore we have not included these mechanical 

energy conversion systems on the table and we are finalizing the definition for waste heat 

recovery specifying the conversion to electrical energy only. 

 Finally, comments from Borg-Warner and the Motor Equipment Manufacturers 

Association (MEMA) mentioned that the term “engine heat recovery” was too narrowly 

defined or ambiguous regarding the type of applicable technology. Therefore, they 

recommended a more neutral approach and advocated for the term “waste heat recovery” 

currently used by the industry, academia, and the Department of Energy. We agree with these 

comments and have revised the terminology for this credit in both this section and in the 

regulatory text . 

 The revisions to the terminology and definition, and other clarifications are reflected 

in the definitions below under section 5.2.5. 

5.2.3 High Efficiency Exterior Lights 

The current EPA test procedures are performed with vehicle lights (notably, headlights 

including daytime running lamps (DRLs)) turned off. Because of this, improvement to the 

efficiency of a vehicle’s headlights is not captured in the existing test procedures and is 

appropriately addressed through the off-cycle crediting scheme. Further, since a typical level 

of improvement can be quantified, it is appropriate to include this technology within the off 

cycle credit menu. 

Similar to the waste heat recovery, EPA conducted full vehicle simulation to 

determine the impact of energy savings from high efficiency lights on fuel economy and CO2

emissions. The methodology is identical to that described above with the exception that the 

2-cycle results were not subtracted from 5-cycle test results (in response to ICCT’s 

comments). Rather, the energy levels with and without the technology were compared 

directly on the 5-cycle simulation only. This results in a CO2 reduction of 3.2g/mi per 100 

Watt saved (or generated in the case of solar panels) as shown in Table 5-18 (in the NPRM, 

this value was 3.7 g/mi per 100 Watts). 

As with residential light bulbs, the technology available for vehicle lighting has 

changed significantly in recent years. Vehicle manufacturers are commonly using advanced 

technology LEDs in taillights and offering new light producing technologies for headlights. If 

these technologies require less energy to operate, they will improve the overall fuel economy 

of the vehicle and will be eligible for an off-cycle credit. 

For the proposal, we referenced Schoettle, et al.41, which studied the effects of highefficiency LED lighting. In the draft TSD, Table 5-19 provided a summary excerpted from 

that study of average lighting power requirements for both baseline and high efficiency lights 

for late-model vehicles and Table 5-20 provided usage rates. 

We used these two tables to develop a simple activity-weighted average of the 

aforementioned categories which yielded an average nighttime power consumption (for the 

categories in question) of roughly 180W for a baseline vehicle and 120W for a vehicle with 

high efficiency lights as shown in the draft TSD that accompanied the proposal. This 
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5-70 

difference of 60W (180W-120w) was discounted to 30W since 50% of all VMT occurs at 

night based on MOVES activity data. Using this 30W and the base load reduction values of 

3.7 g/mi CO2 benefit per 100 watts on the 5-Cycle test, we proposed a credit value of 1.1 g/mi 

(e.g., (30 watts/100 watts) x 3.7 g/mi). 

We received several comments suggesting that the value shown in the table for highefficiency low beams of 108.0 watts from the Schoettle et al. report (October 2008) was 

overstated. Three commenters, the Alliance, Volkswagen and Honda, suggested separate 

values for the low beam high efficiency lighting. The Alliance suggested a value of 52.4W 

using the baseline wattage of 112.4W for a savings of 60W. Volkswagen suggested a value 

of 54W and a baseline of 137W, based on an European application, for a savings of 83W. 

Lastly, Honda suggested a value of 66W also using the baseline wattage of 112.4W as well, 

based also on a European application, for a savings of 46.4W. Since this report is slightly 

older and these values are from actual vehicle applications, we agree with the commenters 

that these suggested values may be more representative of today’s vehicles. Therefore, we 

used these numbers to revise our high-efficiency exterior light calculations. These highefficiency and baseline low beam wattages represent a percentage savings of 53% 

(52.4/112.4), 61% (54/137) and 41% (66/112.4) respectively. Out of these, we used the most 

conservative saving estimate of 41%, which is much larger than the 4% savings we originally 

assumed. Therefore, we used 66W (41% of the 112.4W for baseline low beam lighting) for 

the high-efficiency low beam menu value based on the comments and supporting data. 

The Alliance also commented that the brake/stop lamps and center high mounted stop 

lamp (CHMSL) lighting are enabled during the 2-cycle tests meaning that some of the realworld benefit would also be seen on the 2-cycle tests. Since stop/brake and CHMSL already 

have a very low usage rate, the benefit of high-efficiency lighting on these two lights would 

be minimal (and as explained above, would be rounded to zero). Therefore, these two 

lighting elements have been eliminated from the list. 

 Additional comments from the Alliance and Honda recommended that, rather than the 

bulk approach that we proposed, we allow scaling of the credit according to the lighting 

systems on the vehicles and that manufacturers be allowed to select individual lighting 

components from the list to receive credit. To address these comments, a different approach 

was required as the method above provides for a more absolute value based on a discrete 

number of components and values. In addition, the approach above uses a 2-cycle/5-cycle 

test comparison. Manufacturers currently do not operate lighting, with the exception of 

stop/brake lights and CHMSL, on the test cycles and, as a result, this approach would have 

required them to start enabling lighting during these test cycles. 

For the lighting components used only at night, we used a night time VMT discount of 

28.2%, to determine the credit for these components based on a more recent review of 

MOVES VMT data as shown in Table 5-20 below. The values in the table were determined 

by taking the total VMT distributed on a monthly basis and applying the sunrise and sunset 

times on the 15th day of each month over the VMT distribution to develop a relationship 

between VMT and the time of day on a monthly basis, and then taking the average of the 

monthly night time fraction. 
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Table 5-20 MOVES data showing fraction of VMT attributed solely to night time driving 

month Day VMT Night VMT Night Fraction

1 1,647,881 1,022,867 0.383

2 1,776,185 894,563 0.335

3 1,931,025 739,723 0.277

4 2,083,232 587,516 0.220

5 2,129,737 541,011 0.203

6 2,195,311 475,438 0.178

7 2,171,185 499,563 0.187

8 2,109,682 561,067 0.210

9 1,959,926 710,823 0.266

10 1,781,577 889,172 0.333

11 1,029,297 1,641,451 0.385

12 1,088,092 1,582,657 0.407

All 23,009,849 9,039,131 0.282

For the components used during day and night, this simply becomes 1.0, which implies there 

is no discounting based on night time only usage. 

 Therefore, we used the power demand estimates, with the revision to the low beam 

lighting element, along with the VMT fractions and developed individual lighting credits for 

each component on the high-efficiency exterior lighting list as shown in Table 5-21 below. 

Table 5-21 Individual Credit Values for High Efficiency Exterior Lighting Components 

Lighting Component Baseline High Eff Night Use Only % Day & Night Use % g/mi CO2 Credit Savings %

Low Beam* 112.4 66 91% 0% 0.38 52%

High Beam 127.8 68.8 9% 0% 0.05 46%

Parking/Position 14.8 3.3 100% 0% 0.10 78%

Turn Signal, front 53.6 13.8 0% 5% 0.06 74%

Side Marker, Front 9.6 3.4 100% 0% 0.06 65%

Tail 14.4 2.8 100% 0% 0.10 81%

Turn signal, rear 53.6 13.8 0% 5% 0.06 74%

Side Marker, rear 9.6 3.4 100% 0% 0.06 65%

License Plate 9.6 1 100% 0% 0.08 90%

Base electrical load redux 100 watts

Fuel savings per 100W 3.2 g/mi Nighttime VMT (MOVES Data): 28.2%

Total Available Credit 1.0 g/mi

*Value for high efficiency wattage changed based on comments and supporting data

Using this table, manufacturers may use all of the lighting components on this list and receive 

a maximum of 1.0 g/mi credit. Alternatively, as requested by comment, manufacturers may 

select individual lighting components from this list to determine the credits for high efficiency 

exterior lighting. To receive high efficiency exterior lighting credit using the pre-defined 

technology list, manufacturers may only use the lighting elements and the values shown in 

this table. 
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5-72 

 If a manufacturer has lighting elements that result in a higher benefit than shown on 

this table, the manufacturer may use these values under the alternate case-by-case approval 

process by performing according to the following formula below: 

 High Efficiency Exterior Lighting Credit = 

 (Baseline lighting wattage – high efficiency lighting wattage) x usage rate x VMT fraction x 3.2 g/mi CO2

100 watts; 

where usage rate is the percentage shown below for the applicable lighting component, the 

vehicle miles traveled (VMT) fraction is 28.2%, or 0.282, for lighting components used 

during night time only and 100%, or 1.0, for lighting components used during the daytime and 

night time. However, if a manufacturer has lighting elements that provide less benefit than 

shown on this table, these lighting components are not eligible for the high-efficiency exterior 

lighting credit. To implement this limitation, the agencies reserve the right to request a list of 

lighting elements from the manufacturer to support the amount of credit requested, regardless 

if it is via the pre-defined technology list or as part of the case-by-case approval process. 

 We received comments from Honda requesting a separate credit for replacing lighting 

relays as well.42 Page 5 of that comment indicates that the implementation of all lighting 

relays produces a small total savings of 1.9W. Therefore, we do not believe lighting relays 

merit a separate credit due to the small amount of credit that would be generated. However, 

they can be included in an assessment of high efficiency exterior lighting credit under the 

alternate method approval process since this would create values other than that shown above 

in Table 5-21. 

 We also received considerable comment regarding the inclusion of daytime running 

lights (DRLs) on the list of high efficiency exterior lighting. The Agencies did not propose to 

include DRLs on the high efficiency exterior lighting list, and are adhering to that decision in 

the final rule. It is difficult to assign a power demand value to DRL since some 

manufacturers may choose to use dedicated DRLs while some manufacturers may choose to 

use the low beams as DRLs. In addition, some manufacturers may implement it on all their 

vehicles while some will choose to implement DRLs on a portion of vehicles (or not at all). 

The other primary reason for rejecting inclusion of DRL on the pre-approved menu 

technology list is that DRLs are currently disabled during the 2-cycle testing and, 

consequently, there is no basis for comparison to the 5-cycle test or real-world unless test 

procedures are modified to require DRL enablement during standardized test procedure. As a 

result, it is difficult to pinpoint a single strategy to assign power demand, assess fleet 

implementation rates, and account for it on the standardized test cycles to develop a single 

credit value. Therefore, we are not including DRLs in the list of lighting elements used to 

grant a high-efficiency exterior lighting credit on the technology list menu. However, as 

mentioned before, manufacturers may use the alternate case-by-case demonstration methods 

finalized in today’s action to request off-cycle credit for DRLs. 

Finally, LEDs used for decorative or accent lighting are not eligible for off-cycle credits under 

either the technology menu or through a case-by-case demonstration. This is because LEDs 

are properly classified as optional accessories or “features”. 
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5-73 

5.2.4 Solar Panels 

Manufacturers are beginning to offer the option to put solar cells on the roof of a 

vehicle. The solar panel option on the new Toyota Prius is an example. The initial 

implementation of this idea has been limited to cabin ambient temperature control (this 

technology is covered under thermal/solar load control below), but manufacturers have raised 

the possibility of using roof top solar cells to charge PHEV and EV batteries and provide 

energy to operate the vehicle, increasing the vehicle’s all-electric range. This electrical 

energy cannot be accounted for on the current EPA cycles – either the two cycle test or the 

five-cycle test. Only HEVs, PHEVs and EVs are eligible for this credit. 

Using engineering judgment, the EPA estimated in the NPRM that vehicles with a 

solar roof would be parked in sunlight on average four hours a day, and that the solar panels 

would have an output of 50W. The EPA also assumed that the solar cells will produce 50% 

of their rated power of 50W (due to the solar angle, parking conditions, weather conditions, 

etc.) with a battery efficiency of 80%. A vehicle with this configuration could save up to 80 

Wh/day of electrical energy. The EPA sought comments on these assumptions and requested 

more data to refine these numbers (See draft joint TSD section 5.2.1.3). EPA also noted the 

possibility of scaling the credit for certain solar roof panels. Id.

 The ICCT commented that this credit was not appropriate and was not supported by 

actual data. However, in contrast, the Alliance comments stated that this level of credit was 

appropriate based on theoretical calculations and experimental data. 

 In addition, the Alliance recommended that 1) the credit should be scalable (e.g., 

(solar roof panel output in watts divided by 50 watts) times 3.0 grams/mile), 2) the credit 

should apply for solar panels in locations other than the roof, and 3) the credit should be 

available for other vehicles, not just PHEVs and EVs. 

 The comments from Guardian stated that solar roof panel technology is “rapidly 

evolving” to the point where the 50 watt threshold we proposed “will be quickly surpassed or 

[is] being surpassed.” To address this, they also proposed that we use a simple, formulabased credit similar to the Alliance comments on scaling. Similar to the Alliance, Guardian 

recommended that this credit should not be limited to just “roof” panels if other locations can 

provide appropriate output and, therefore, the term “roof” should be removed to reflect this. 

Finally, Guardian suggested that we use standard test conditions (STC) from the photovoltaic 

industry to define how panel power is determined of 1000 watts per meter squared (W/m2) 

direct solar irradiance and a panel temperature of 25 degrees Celsius. 

 First, we agree with the Alliance comments that we should scale solar panel credits. 

As the agencies stated in the draft joint TSD, “EPA will also consider scaling this credit for 

solar roof panels that provide more or less power than 50W.” Second, the current definition 

for “solar roof panels” does not specify that the panels must be on the roof, although the term 

“roof” implies this. Therefore, we understand the potential for confusion and will change the 

term for this credit and the associated definition to “solar panels”. However, since this term 

also creates some ambiguity, we will clarify that the term “solar panels” is limited to 

“horizontally-oriented, external solar panels with the potential for direct, uninhibited solar 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-74 

exposure.” This prevents someone from installing solar panels in less effective locations 

(e.g., underneath the vehicle or in the vehicle passenger cabin) and claiming credit for solar 

roof panels. Lastly, the reason we limited this credit to HEVs, PHEVs and EVs is this is 

where we see the most benefit for this technology. It would not aid conventional vehicles 

since this would amount to “trickle” charging of the battery and, since hybrid vehicles have 

many methods and more substantial means of energy recovery, we did not see a need to 

expand this credit beyond HEVs, PHEVs and EVs. We do see a benefit for this technology 

on conventional vehicles when combined with active cabin ventilation, and these credits are 

already included on the menu. 

 We also agree with the comments from Guardian regarding revising the terminology, 

as discussed above, and defining the test conditions to determine the solar panel power output. 

We performed a cursory literature search and verified the conditions that Guardian stated, 

along with a specification for an air mass of 1.5 (AM1.5). As a result, we are including these 

metrics as well as the revised credit terminology in the definitions section. Also, as Guardian 

stated, the power output of 50 watts seems to have been surpassed and that solar panel outputs 

of up to 150 watts are possible. Therefore, we are revising the solar panel credit formula to 

allow for scaling to more efficient and larger panels. 

Based on the comments from the Alliance and Guardian, and the agencies’ own 

suggestion in the draft Joint TSD, we revisited the credits for solar panels to provide for 

proper scaling. Similar to high-efficiency exterior lighting discussed above, we needed to use 

a different approach since the method used for the proposed solar roof panel credit assigned 

was based on the credit scalar according to Table 5-22. This scalar represents an 

improvement in CO2 emissions for every 100 W of electrical load reduced in vehicles 

equipped with conventional powertrains. Therefore it is inappropriate to use this scalar to 

represent an efficiency improvement from solar panels. For this final rule, we use some of the 

base assumptions in the proposal along with new information regarding methods for 

quantifying the energy from solar panels to improve the calculation methodology. 

To properly scale the credit, we estimated the energy generated by the solar panel and 

stored in a P/H/EV battery. Then we used a number of vehicle simulation results from 

Ricardo to determine a gram per mile displaced by running a vehicle off of electric power (as 

in a battery). First, it is important to define the industry standard for rated solar panels, which 

is: 

Ppanel = ηpv * Φsysw * A 

Where: Ppanel is the rated panel power output 

 ηpv is the Photovoltaic cell efficiency 

 Φsysw is the standard radiation flux (assumed to be 1000W/m2)

r

A is the Solar Panel Cell Area in m2

 
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5-75 

Next, we calculate the amount of energy that is captured by the solar panel on a yearly basis 

and stored in the battery of the P/H/EV considering the following factors: the average solar 

energy across the United States on a daily basis, the battery/motor efficiency, the amount of 

time that the solar panel is exposed to the sun accounting for obstructed parking by structures, 

clouds and inclement weather, and the size and efficiency of the solar panel. The formula for 

this is as follows: 

 Epanel = Eavg * Days/Year * ηbatt * Expsolar * ηpv * A 

Where: 

Eavg is the national average solar energy flux per day (approximately 4.159 

kWh/m2/day)s, including the effects of weather, season, clouds etc 

Days/Year is the number of days per year (365.25 days per year); 

ηbatt is the average battery/motor combined efficiency (assumed here to be 95% 

for battery, 92% for motor, and 98% for power electronic for a total of 

86%)t; 

Expsolar is the amount of solar exposure for the solar panel or “derate” factor,u 

assumed to be 79% and includes soiling and shading (e.g., trees, 

parking, buildings). 

To determine the average solar energy per day of 4.159 kWh/m2/day, we used the historical 

data from the National Renewable Energy Laboratory (NREL). Specifically, we used the 

State Average Insolation Values (2003-2005) Weighted by Region of Use Based on 2005 

Electricity Use Patterns in kWh/m2/day. We used all of the states except Alaska and Hawaii 

since this would tend to skew the data in a certain direction since these states tend to be at the 

extremes of solar exposure. The data is listed for several angles of incidence but we used the 

values for a horizontal panel since the solar panels on a vehicle do not automatically move to 

acquire an optimal angle for solar exposure. The values we used are shown below in Table 

5-22. 

 

s

 Estimated from http://rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/redbook/atlas/ 

t

 Consistent with the assumption in the proposal, we are assuming that the power from the solar cell will be 

stored in the battery for the most part. CITE TO PROPOSAL. A small portion can be used directly to power 

accessories or even the traction motor during normal vehicle operation (in which case, this factor is not 

required), but most vehicles spend most of their time parked. This analysis also assumes that the battery state of 

charge is sufficiently low to be able to accept additional energy. 

u

 Estimated from http://rredc.nrel.gov/solar/calculators/PVWATTS/version1/derate.cgi. In order to determine 

Expsolar (derate), we used the following factors: shading = 0.85, soiling = 0.95 (default), system availability = 

0.98 (default), and all other factors 1.0, as most of these factors relate to stationary applications. We assumed 

the suntracking factor of 1.0 since it would be already included in Eavg.For the shading factor, we assume that any 

vehicle purchaser who is willing to pay a significant premium for a solar roof will preferentially park it in an 

area of high sunlight exposure. 
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Table 5-22 State Average Insolation Values (2003-2005) Weighted by Region of Use Based on 2005 

Electricity Use Patterns in kWh/m2/day for contiguous United States 

State

Insolation Value 

(2003-05 for a flat panel)

Alabama 4.3

Arizona 5.6

Arkansas 4.3

California 4.9

Colorado 4.8

Connecticut 3.7

Delaware 3.9

District of 

Columbia 3.9

Florida 4.7

Georgia 4.3

Idaho 4.4

Illinois 3.9

Indiana 3.9

Iowa 4

Kansas 4.4

Kentucky 4

Louisiana 4.5

Maine 3.7

Maryland 3.9

Massachusetts 3.7

Michigan 3.7

Minnesota 3.7

Mississippi 4.4

Missouri 4.2

Montana 3.9

Nebraska 4.2

Nevada 5.4

New Hampshire 3.7

New Jersey 3.8

New Mexico 5.5

New York 3.8

North Carolina 4.2

North Dakota 3.7

Ohio 3.8

Oklahoma 4.5

Oregon 3.8

Pennsylvania 3.7

Rhode Island 3.8

South Carolina 4.3

South Dakota 4.1

Tennessee 4.2

Texas 4.6

Utah 4.6

Vermont 3.7

Virginia 4

Washington 3.6

West Virginia 3.8

Wisconsin 3.8

Wyoming 4.5

For our analysis, we used the average of the values across the contiguous states of 4.159 

kWh/m2/day to represent the broad spectrum of solar conditions across the United States. 

This is equivalent to a solar panel being exposed to the solar energy from the sun (also 

known as solar radiation or flux) of 1000 watts per square meter for 4.159 hours on a daily 

basis over the course of a year. 
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 Substituting in the values above, we get: 

 Epanel = 4.159 kWh/m2/day * 365.25 days/year * 0.86 * 0.79 * ηpv * A; or 

 Epanel = 1030 kWh/m2/Year * ηpv * A 

 Next, we convert the energy from the panel to a gram per mile of CO2 equivalent to 

determine the credit. In order to do this, we estimate the amount of tractive energy (energy to 

drive the wheels in kWh) and the associated CO2 emissions for different subclasses of parallel 

hybrid (P2) vehicles. Comparing the energy generated at the battery with the amount of 

energy (and emissions) on the P2 hybrid vehicles, we estimate the amount of energy and 

hence emissions that are displaced by the solar panels. 

The following tables show the 2-cycle average emissions and tractive energy based on full 

vehicle simulations and energy analysis of the hybrid vehicles as modeled by Ricardo (see 

Chapter 3 of the joint TSD for a description of the Ricardo work): 

Table 5-23 CO2 Emissions from Each Vehicle Type 

Driving Cycle Small-Size Car Mid-Size Car Large-Size Car Pick-up Truck

FTP (CO2 g/mi) 125.44 137.54 178.13 267.95

Highway (CO2 

g/mi) 150.75 148.96 192.51 306.30

Combined (CO2 

g/mi) 135.69 142.45 184.33 283.95

Table 5-24 Vehicle Travel Distance per Energy applied at Wheel 

Traveled Mile per 

Energy applied at 

Wheel [mi/kWh]

Small-Size Car Mid-Size Car Large-Size Car Pick-up Truck

FTP 5.7461 4.6861 3.8528 2.6663

Highway 5.5348 5.1317 4.0623 2.6553

Combined 5.6510 4.8867 3.9470 2.6613

Table 5-25 CO2 Emissions per Energy applied at Wheel 

CO2 per Energy 

applied at Wheel 

[g/kWh]

Small-Size Car Mid-Size Car Large-Size Car Pick-up Truck

FTP 720.76 644.52 686.29 714.45

Highway 834.37 764.42 782.03 813.32

Combined 766.78 696.12 727.54 755.69
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The sales weighted average CO2 emissions is 745.8 g/kWh. This is using the sales and VMT 

schedules consistent with the rest of this rule. If we combine the emissions per unit energy 

with the annual energy generated from a typical panel along with an assumption of 15,000 

miles traveled per year for an average vehiclev, we get: 

Amount of CO2 reduced by Panel = Epanel * 745.8 g/kWh * ηbatt / 15000 

mi/year; or 

 Amount of CO2 reduced by Panel = 43.85 g/mi * ηpv * A 

By rated panel power relationship (defined above), but rewritten in the form: 

Ppanel / (1000W/m2) = ηpv * A 

we get a scalable credit such that: 

 Solar Panel Credit, Csolar = 0.04385 g/mi/W * Ppanel 

This equation is a function of only the rated power of the panel. These are standard 

specifications for solar panels and are provided by the panel manufacturers. 

Therefore, for a 100 Watt rated panel, the credit would be 4.4 g/mi. This value is (per 

the public comments) now scalable to the solar cell. 

As an illustrative example, the 2012 Toyota Prius solar panel is currently used for 

active ventilationw

(equivalent to 2.1g/mi credit). However, if the solar panel were to be used 

to charge the battery it would get a higher credit: In the Prius, the approximate specifications 

for the solar panel are an efficiency rating of 16.5% (0.165) and an area of 0.405 m2thus 

having a rating of 67 Watts.x This would qualify for a credit of 2.9 g/mi CO2.

This value of solar panel credit is comparable to the 2.1 g/mi credit from the active 

ventilation. However, the active ventilation is not required all year round (the remainder of 

the power generated being wasted). In an effort to encourage more solar use on P/H/EV 

vehicles, and to more accurately characterize the year-round potential benefits for use of the 

solar technology on vehicles, the agencies are finalizing a credit scheme that will allow 

benefits for active ventilation as well as for electrical generation. This was not included in the 

proposal and is new to the final rule as a result of the Agencies’ effort to make this credit 

scalable. 

 

v

 Consistent (though simplified) with assumptions made elsewhere in this rule 

w

 http://global.kyocera.com/reliability/file02.html 

x

 http://techon.nikkeibp.co.jp/english/NEWS_EN/20090519/170318/: Area is 36*.15*.075 = 0.405m2, efficiency 

is 16.5%. 
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5-79 

 There are three scenarios to consider regarding the interaction between solar panels 

and active cabin ventilation: 1) using the solar panel solely for the purpose of charging the 

battery, 2) using the solar panel to only power the active cabin ventilation system, and 3) 

using the solar panel to charge the battery and power the active cabin ventilation. The first 

two scenarios are simpler and more straightforward so we will discuss them first together. 

The third scenario is more complicated due to power splitting. 

 If the solar panel is being used solely to charge the battery, the solar panel credit alone 

will be granted and the equation above can be used to determine the amount of solar panel 

credit. If the solar panel is being used solely to power the active cabin ventilation system, 

only the active cabin ventilation credit (see section 5.2.13 below) will be available. 

 If the solar panel is being used to both power the active cabin ventilation system and 

to charge the battery, the manufacturer may get credit for some combination of the solar panel 

and active cabin ventilation system credit. However, the manufacturer would be required to 

account for the amount of power required for the active cabin ventilation system, then 

calculate the applicable solar roof panel power for battery charging and subtracting the power 

for active cabin ventilation. Note that using the calculation below, a manufacturer could not 

get more credit than accounting for the solar panel and active cabin ventilation credits 

separately. 

 To account for the wattage that would be devoted for the active cabin ventilation, we 

use the equation above for the panel power and the wattage needed for fans used for active 

cabin ventilation. Based on information from Delphi, the power used to operate the fan motor 

used in active cabin ventilation is typically 19 Watts. In order to calculate the amount of the 

rated panel power that would generate the 19 W from the ideal solar flux of 1000 W/m2, we 

first estimated the fraction of the average sunlight in US, which would be used to generate the 

required power. 

 fsun = Eavg / nhr,day / Φsysw

where: 

fsun is a fraction of the average sunlight in US (dimensionless); 

nhr,day is an average daytime hours per day (assumed to be 12 hours/day). 

 After substituting appropriate values in this equation, we get 0.347 for fsun. Using this 

value, we get the amount of the rated panel power that would generate the fan power from the 

ideal solar flux of 1000 W/m2is

 Psolar/vent = Pvent / fsun

Where: 

Psolar/vent is the amount of equivalent rated panel power that would generate power for 

the vent Pvent
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Pvent is the amount of power required to run the low speed ventilation fan in the 

dashboard (assuming there is a route for the heat to escape the car). For this credit 

calculation, the value of 19 W must be used. 

With the value of fsun, Psolar/vent turns out to be 54.8 W (using the fixed value of 19W 

for the fan). Then, the remaining solar panel credit for battery charging is calculated as 

follows. 

 Csolar/vent = 0.04385 g/mi/W * (Ppanel – Psolar/vent / 3) 

Where: 

 Csolar/vent is the solar panel credit available for battery charging after the ventilation fan 

power has been dissipated. 

 Note that Pvent is divided by 3 to account for the assumption that the active ventilation 

is used only 4 months a year on average. This amount would be subtracted from the solar 

panel credit menu value for full battery charging. Substituting, the partial credit equation 

becomes: 

Csolar/vent = 0.04385 g/mi/W * (Ppanel – Pvent / (3*0.347)) 

Due to the inherent uncertainties in some of the assumptions, and for the sake of simplicity, 

we are setting the constant in the parentheses (3*0.347) to unity. Therefore the equation 

becomes simply: 

Csolar/vent = 0.04385 g/mi/W * (Ppanel – Pvent) 

 Using the Prius example to illustrate this, if the Prius used the solar panel to operate 

the active cabin ventilation system fan motor and to charge the battery, the Prius could receive 

2.1 g/mi for active cabin ventilation and additional 2.1 g/mi solar panel credit attributable to 

battery charging. This 4.2 g/mi credit is higher than if the solar panels were used for 

electricity generation alone (2.9 g/mi). This credit recognizes that the agencies are now 

providing an incentive to use that additional power that might have been wasted in a 

beneficial manner, reducing GHG emissions and using less fuel. 

 In summary, we are finalizing the credits for solar panels using the revised values, 

allowing them to be scaled according to solar panel output, and allowing for combining the 

credit with the active ventilation credits where the solar panels are used for both purposes. 

The agencies are revising the terminology and definition for solar panel credits as discussed 

above. In addition, as proposed, the solar panel credit is only available for HEV, PHEV, EVs, 

and FCEVs (fuel cell), and is not eligible for incentive multipliers. 
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5.2.5 Definitions for Electrical Load Offsetting and Reduction Technologies 

Waste heat recovery is a system that captures heat that would otherwise be lost 

through the engine, exhaust system, radiator or other sources and converting that heat to 

electrical energy that is used to meet the electrical requirements of the vehicle or used to 

augment other load reduction technologies (e.g., cabin warming, active engine/transmission 

warm-up strategies). The amount of energy recovered is based on the average value over 5-

cycle testing. 

High efficiency exterior lighting means a lighting technology that, when installed on 

the vehicle, is expected to reduce the total electrical demand of the exterior lighting system 

when compared to conventional lighting systems. To be eligible for this credit the high 

efficiency lighting must be installed on one or more of the following lighting components: 

low beam, high beam, parking/position, front and rear turn signals, front and rear side 

markers, taillights, backup/reverse lights, and/or license plate lighting. 

Solar roof panels means the installation of horizontally-oriented, external solar panels with 

direct solar exposure, uninhibited by portions of the or the entire vehicle, on an electric, fuel 

cell electric, hybrid electric or a plug-in hybrid electric vehicle such that the solar energy is 

used to provide energy to the electric drive system of the vehicle by charging the battery or 

directly powering essential vehicle systems (e.g., cabin heating or cooling/ventilation), or 

providing power to the electric motor. The rated power of the solar roof panels used to 

determine the credit value must be determined under the standard test conditions of 1000 

watts per meter squared direct solar irradiance at a panel temperature of 25 degrees Celsius 

+/- 2 degrees Celsius with an air mass of 1.5 spectrum (AM1.5). 

5.2.6 Active Aerodynamic Improvements 

The aerodynamics of a vehicle play an important role in determining fuel economy. 

Improving the aerodynamics of a vehicle reduces drag forces that the engine must overcome 

to propel the vehicle, resulting in lower fuel consumption. The aerodynamic efficiency of a 

vehicle is usually captured in a coast down test that is used to determine the dynamometer 

parameters used during both the two-cycle and five-cycle tests. This section discusses active 

aerodynamic technologies that are activated only at certain speeds to improve aerodynamic 

efficiency while preserving other vehicle attributes or functions. Active aerodynamic features 

can change the aerodynamics of the vehicle according to how the vehicle is operating, and the 

benefit of these vehicle attributes may not be fully captured during the EPA test cycles. 

Two examples of active aerodynamic technologies are active grill shutters and active 

ride height control. Active grill shutters close off the area behind the front grill so that air 

does not pass into the engine compartment when additional cooling is not required by the 

engine. Nearly all vehicles allow air to pass through the front grill of the vehicle to flow over 

the radiator and into the engine compartment. This flow of air is important to prevent 

overheating of the engine (and for proper functioning of the A/C system), but it creates a 

significant drag on the vehicle and is not always necessary. Thus, active grill shutters reduce 

the drag of the vehicle, reduce CO2 emissions, and improve fuel economy. When additional 

cooling is needed by the engine, the shutters open until the engine is sufficiently cooled. 
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Active ride height control uses the chassis and suspension components, such as hydraulic 

shock absorbers, to lower the height of the vehicle, thus reducing ground clearance, typically 

at higher vehicle speeds. This lowers the relative amount of air traveling under the vehicle 

while maintaining the amount of air around and over the vehicle. This reduces drag on the 

vehicle requiring less power to maintain the same speed, and consequently reducing fuel 

consumption. 

As proposed, EPA is limiting credits to active aerodynamic systems only (not 

passive). The reason for this is that passive systems are too difficult to define and isolate as a 

technology. For example, the aerodynamic drag on the vehicle is highly dependent on the 

vehicle shape, and the vehicle shape is (in turn) highly dependent on the design characteristics 

for that brand and model. EPA believes that it would be inappropriate to grant off-cycle 

credits for vehicle aesthetic and design qualities that are passive and fundamentally inherent 

to the vehicle. Thus, passive aerodynamic systems are not an off-cycle menu technology, and 

also could not be a candidate for off-cycle credits under the case-by-case demonstration 

procedures. 

To evaluate active aerodynamic technologies that reduce aerodynamic drag, the EPA 

conducted an analysis of the reduction in emissions corresponding to a general reduction of 

aerodynamic drag on a vehicle. Using EPA’s full vehicle simulation tool described in EPA’s 

RIA Chapter 2, the agency evaluated the change in fuel consumption for increasing reductions 

in aerodynamic drag for a typically configured vehicle. The results of this analysis form the 

basis for a consistent methodology that the EPA applied to technologies that provide active 

aerodynamic improvements. 

Vehicle aerodynamic properties impact both the combined FTP/Highway and 5-cycle 

tests. However, these impacts are larger at higher speeds and have a larger impact on the 5-

cycle tests. By their nature of being “active” technologies, EPA understands that active 

aerodynamic technologies will not be in use at all times. While deployment strategies for 

different active aerodynamic technologies will undoubtedly vary by individual technology, 

the impact of these technologies will mostly be realized at high speeds. Since aerodynamic 

loading is highest at higher speeds, EPA expects that active aerodynamic technologies will 

generally be in use at high speeds, and that the 5-cycle tests will capture the additional real 

world benefits not quantifiable with the FTP/Highway test cycles due to the higher speed in 

the US06 cycle. Active aero may also depend on weather conditions. For example, active 

aerodynamics may operate less in hot weather when air cooling is required to exchange heat 

at the condenser. Also, active grill shutters may need to stay open during snowy conditions in 

order to prevent them from freezing shut (potentially causing component failure). In fact, the 

MOVES data indicates that only 68% VMT occurs between 40 F and 80 F.

Using EPA’s full vehicle simulation tools, the impact of reducing aerodynamic drag 

was simulated on both the combined FTP/Highway cycles and the 5-cycle drive tests. To 

determine the fuel savings per amount of aerodynamic drag reduction, the fuel savings on the 

FTP/Highway test cycle was subtracted from the fuel savings on the 5-cycle tests. This is 

consistent with the approach taken for other technologies. Then, using the MOVES data, the 

vehicle simulation results were adjusted for the temperature effects on active grill shutter 

operations. Table 5-26 shows the results of the vehicle simulation. Also, Figure 5-11
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represents this GHG reduction metrics in a graphical form. These results assume that the 

active aerodynamics affects the coefficient of drag only, which is currently assumed to be 

constant over a wide range of vehicle operating speed. However, if the coefficient of 

aerodynamic drag is assumed to be vehicle speed dependent, then a different relationship 

could result. 

Table 5-26 Simulated Maximum GHG Reduction Benefits of Active Aerodynamic Improvements 

Figure 5-11 Simulated GHG Reduction Benefits of Active Aerodynamic Improvements 

We are scaling the credit for active aerodynamics using Table 5-26 and Figure 

5-11shown above. A manufacturer would simply determine the aerodynamic benefit of their 

active technology on a percent basis and find the corresponding CO2 value in grams per mile 

off using the data points in the table. 

Reduction in 

Aerodynamic Drag (Cd)

Car Reduction in 

Emissions (g/mile)

Truck Reduction in 

Emissions (g/mile)

1% 0.2 0.3

2% 0.4 0.6

3% 0.6 1.0

4% 0.8 1.3

5% 0.9 1.6

10% 1.9 3.2
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5.2.7 Definition for Active Aerodynamic Improvements 

Active aerodynamic improvements are technologies that are automatically activated 

under certain conditions to improve aerodynamic efficiency (e.g., lowering of the coefficient 

of drag or Cd using SAE J2881, while preserving other vehicle attributes or functions). 

5.2.8 Advanced Load Reductions 

The final category of off-cycle credits includes technologies that reduce engine loads 

by using advanced vehicle controls. These technologies range from enabling the vehicle to 

turn off the engine at idle, to reducing cabin temperature while a vehicle is parked and thus 

reducing A/C loading when the vehicle is restarted. Because the benefit of these technologies 

is not fully captured on the combined two cycle tests, and the real-world benefits can be 

reliably but conservatively calculated EPA has evaluated each technology and developed 

automatic off-cycle credits for each technology individually. 

5.2.8.1 Engine Idle Start-Stop 

Engine idle start-stop technologies enable a vehicle to turn off the engine when the 

vehicle comes to a rest, and then quickly restart the engine when the driver applies pressure to 

the accelerator pedal. The benefit of this system is that it largely eliminates fuel consumption 

at idle. The EPA FTP (city) test does contain short periods of idle, but not as much idle as is 

often encountered in real world driving. HEV and PHEVs can also idle-off and are thus 

eligible for this credit. EVs and FCVs do not have engines and thus are not eligible for this 

credit. 

As stated in the proposed Joint TSD, based on a MOVES estimate that 13.5% of all 

driving (in terms of vehicle hours operating) nationwide is at idle, and compared to a 9% idle 

rate for the combined (two-cycle) test, idle-off could theoretically approach an extra 50% of 

the existing benefit seen on the FTP/HWFE test. Vehicle simulation data was used to 

quantify the amount of fuel consumed in idle conditions over the FTP and HFET test across a 

range of vehicle classes. For each vehicle class reviewed, a FTP-HFET combined fuel 

consumption was calculated and compared to total fuel consumption during the combined 

test. The ratio of idle fuel to total fuel represents a maximum theoretical fuel consumption, 

and hence GHG emissions, that could be reduced by eliminating idlingy

. Table 5-27 shows 

this below: 

 

y

 Note that aggressive fuel cutoff upon vehicle decelerations are technically possible and could increase the total 

amount of avoided “idle” fuel consumption; at the same time, the idle-off enable conditions might reduce the 

total idle avoidance. Given the accuracy level of this methodology, EPA assumes these caveats to cancel each 

other out. 
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Table 5-27: Calculations Used for Off-Cycle Credit for Engine Idle Start-Stop Technologies 

Based on the data in Table 5-27 above, EPA suggested that engine idle start-stop 

technology is theoretically capable of providing 3.8 g/mi credit for passenger vehicles and up 

to 6.0 g/mi for trucks. However, cold and hot ambient conditions will prevent idle-off in all 

cases. Based on MOVES data of VMT as a function of temperature (see Table 5-28 below), 

the percentage of nationwide VMT driven above a 45 °F ambient temperature is 

approximately 75%. Therefore, EPA and NHTSA proposed 75% of the theoretical savings 

above will be appropriate for an idle off credit; equating to 2.9 g/mi for passenger vehicles 

and 4.5 g/mi for trucks. 

The comments from ICCT were critical of the underlying assumptions used to 

determine the amount of credit in two respects : 1) the idle rate assumed for the 2-cycle tests 

and 2) application of the real-world idle percentage to only the off-cycle credit value, not the 

underlying idle time used to determine the amount of the credit. 

First, the commenter stated that the16% idle rate for the 2-cycle tests solely 

contributed by the FTP and listed in the TSD should actually be 19.5% in total, with 19% of 

the idle contributed by the FTP test and 0.5% contributed by the HWY test. Thus, when 

applying the FTP/HWY weighting of 55%/45%, this produces a weighted idle rate of 10.7%, 

not 9% used in the TSD. 

Second, the commenter stated that we only applied the real-world idle percentage to 

scale the engine idle start-stop credit to the credit value, not to the underlying idle time used 

to determine the credit. This comment has technical merit. The agencies therefore we used 

Standard Large Large Full size

Car Car MPV Truck

Total FTP fuel consumption (g) 1044 1276 1412 1868

FTP fuel consumed at idle (g) 68 71 69 97

Total HWFE fuel consumption (g) 675 862 970 1240

HWFE fuel consumed at idle (g) 0.0 0.0 0.0 0.0

FTP-HWFE combined fuel consumption (g) 878 1090 1213 1585

FTP-HWFE combined fuel consumed at idle (g) 37 39 38 53

potential % GHG reduction benefit 4.2% 3.6% 3.1% 3.4%

% FTP idle time 16% 16% 16% 16%

% HWFE idle time 0% 0% 0% 0%

FTP-HWFE combined % idle time 9% 9% 9% 9%

Real-world % idle time (via MOVES) 13.5% 13.5% 13.5% 13.5%

Real-world % GHG reduction benefit 6.3% 5.3% 4.6% 5.0%

Off-cycle GHG benefit 2.1% 1.7% 1.5% 1.6%

Assumed GHG for advanced vehicle (g/mi) 165 235 255 365

Off-cycle GHG benefit 3.4 4.1 3.9 6.0
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the MOVES model to estimate that 13.5% of VMT was during idling, providing an 

opportunity for engine idle start-stop. Further, we assumed the engine would be running 25% 

of the time due to cold temperatures, leaving 75% of the real world VMT for stop start. Using 

vehicle simulation with the 13.5% real-world, idle-off time assumption, we estimated a 

potential benefit of 3.8 g/mi for cars and 6.0 g/mi for trucks and applied the real-world factor 

of 75%, resulting in a proposed credit of 2.9 g/mi for cars and 4.5 g/mi for trucks. Thus, the 

commenter’s assertion was that the 75% real world factor should have been applied to the real 

world idle off time of 13.5%, yielding a true real-world idle off time of 10.1% (e.g., 13.5% 

times 75%). As a result, according to the commenter, there is no benefit to grant an 

applicable credit for engine idle start-stop. 

We reviewed these comments thoroughly and agree that some of ICCT’s comments 

have merit. In particular, the comments regarding the 10.7% idle rate for the 2-cycle test and 

the application of real-world factoring were appropriate. However, we disagree with ICCT 

regarding the real-world idle time and the lack of benefits for granting engine idle start-stop 

credits. Thus, we have revised the approach for determining engine idle start-stop credit as 

described below taking into account the issues highlighted by ICCT. 

For the 10.7% 2-cycle idle rate, when we consider the amount of time to reach proper 

operating engine temperature, a small portion of the FTP was eliminated. Our in-house test 

data showed that the average time to reach 90% maximum engine coolant was on average 324 

seconds, and due to this, eliminating the first two idle periods of the FTP. As a result, the idle 

rate we used for the 2-cycle test was 10.0% instead of the 10.7% suggested by the commenter. 

Next we reviewed the estimates of the amount of idle time in the real world. We 

reviewed the analysis for the estimate of the real-world percent idle time in MOVES and, 

since new information has been added to the MOVES model since the NPRM, this number 

has increased slightly to 13.76% from the previous estimate of 13.5%. To validate this 

number, we reviewed other data and studies to see how it compares. In the Supplemental FTP 

(SFTP) studies conducted in the 1990’s, there was a very large vehicle driving activity study 

conducted with instrumented vehicles (EPA 420-R-93-007, “Federal Test Procedure Review 

Project: Preliminary Technical Report, May 1993). The study revealed that the real-world 

percent idle rate (by time) was 22% and it is not certain how much driving patterns have 

changed since then. Therefore, we looked at more recent data, noting that , in 2003, EPA 

conducted another instrumented vehicle study in Kansas City. This study was much more 

limited in scope than the 3 cities study used in the SFTP as each of the vehicles was only 

instrumented for one day in Kansas City and the data from Kansas City was only collected for 

three seasons (fall/winter/spring). This study found that the percent idle time was 17.7%. 

Together, these two studies give evidence that idle rates in the U.S. could be higher than the 

13.76% estimated from MOVES, and is probably higher than the 10.7% on the city/highway 

test procedure. For the final rule, we are applying the more recent MOVES estimate of 

13.76%, which we believe is a conservative estimate. As new data are collected, EPA will 
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continue to review idle rates to assess whether future adjustments in the credit values are 

warranted. 

Since operating conditions, such as cabin heating and cooling, can greatly affect 

engine idle start-stop operation, we have re-evaluated the assumptions that were used in the 

proposal for the percentage of vehicle operation in various ambient temperature conditions 

that were used to estimate the percentage of vehicle operation that engine idle start-stop is 

enabled. Based on a review of MOVES data shown in Table 5-28 below, we found that VMT 

as a function of temperature is as follows: 1) 68.75% of VMT occurs between 40 deg F and 

80 deg F (mid-range), 21.95% of VMT occurs below 40 deg F (cold range), and 9.69% of 

VMT occurs above 80 deg F (hot range). 

Table 5-28 MOVES data of vehicle miles traveled (VMT) as a function of ambient temperature. 

VMT tempAvg Fraction

Temp Range

VMT Fraction

1181.656796 -25 0.00000157

4400.79767 -20 0.00000585

12905.217 -15 0.00001714

40874.20742 -10 0.00005429

174939.1854 -5 0.00023235

762497.0884 0 0.00101274

1915732.576 5 0.00254446

4924729.91 10 0.00654097

12353230.63 15 0.01640743 0.21958689

23259876.93 20 0.03089353 (< 40 deg F)

31418211.75 25 0.04172934

41033016.47 30 0.05449962

49426375.28 35 0.06564760

55404781.78 40 0.07358805

60396251.48 45 0.08021767

63018086.25 50 0.08369996

68380740.42 55 0.09082259

73176481.47 60 0.09719224 0.68343503

72473451.14 65 0.09625848 (> 40 deg F, < 80 deg F)

67073984.17 70 0.08908697

54637578.9 75 0.07256906

39382139.05 80 0.05230695

24182451.73 85 0.03211888

7635253.418 90 0.01014106

1203687.536 95 0.00159873 0.09697809

593360.565 100 0.00078810 (>80 deg F)

18352.30991 105 0.00002438

752904571.9 TotalVMT 1.00000000

We also reviewed data from the Kansas City Study and note that it had a nearly 

identical VMT-temperature distribution. Therefore, using this temperature distribution we 

assumed: 1) all the mid-temperature range is available for engine-off/stop-start operation 

since inhibiting factors (heater and A/C usage) are typically low in this range; 2) all the hot 

temperature range requires A/C operation and would prevent engine-off/stop-start and, 

consequently, none of this range is eligible for stop-start (note: it is possible that this is a 
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conservative estimate as smart A/C controls, potential cooling storage, and electric 

compressors can allow engine to idle off for some hot idles while A/C is demanded); and 3) 

there are several factors that are important to consider in the cold temperature range such as 

average starting temperature, number of cold engine starts, time to reach sufficient engine 

coolant temperature, and the average trip length. Additional information was reviewed to 

refine the assumptions in the cold temperature range. 

EPA also reviewed data from the Supplemental Federal Test Procedure (SFTP) study, 

the EPA’s MOVES model, EPA testing, and other sources to attempt to refine the estimates of 

idle time in the cold ambient temperature range by looking at two key aspects: 1) the 

percentage of time when the engine is cold and trip time is less than 5 minutes; and 2) the 

percentage of time in the field that extended idle would occur to support cabin heating 

demands. 

For the percentage of time when the engine is cold and trip time is less than 5 minutes, 

the SFTP Study showed that 49% of the time, the vehicle was running with engine 

temperatures less than 180 degrees F, which would potentially make this portion of operation 

unavailable for engine-off/stop-start because of the need to support cabin heating demands. 

However, EPA test data showed that the average time to reach 90% of maximum engine 

coolant is 324 seconds, which would eliminate only the first two idles in the FTP and, in 

addition, would mean only trip lengths shorter than 324 seconds are ineligible for engineoff/start-stop operation. Based on an estimate from MOVES data, 25% of the trips had a trip 

time less than 5 minutes which would not achieve full warm-up and are ineligible for engineoff/start-stop operation. 

For the percentage of time in the field that extended idle would occur due to cabin heating, 

based on an estimate from MOVES data, a majority of the starts (95%) have idle times less 

than 5 minutes meaning that only 5% of the starts experience extended idle and are not 

eligible for engine-off/stop-start operation. 

 Based on this information, we revised the cold temperature range to reflect the portion 

of VMT that is not eligible for engine-off/stop-start operation by adding up the portions 

ineligible for engine-off/start-stop operation. The estimated amount of time in the real world 

when vehicles are not warmed up but idle time is greater than 5 minutes is 49% based on the 

SFTP study, multiplied by 25% based on MOVES, and equaling 12.25%, is the value used for 

the a-term in the equation below. The amount of time in the real world that is extended idle 

of greater than 5 minutes based on MOVES activity data is 5% of the 22% VMT in the cold 

range, or 1.1%, which is not eligible for engine-off/start-stop operation, and is the value used 

for the b-term in the equation below. We did consider that the upper limit of 40 deg. F for the 

cold temperature range was too low. However, it is possible that the upper range of 80 deg. F 
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for mid-temperature range could be higher since they are highly dependent on the relationship 

between ambient temperature and humidity, and perceived passenger comfort. Therefore, we 

did not change the upper range of 40 deg. F of the cold regime since these factors balance 

each other. 

 Therefore, we revised our estimate of real-world % idle time by scaling it using these 

values and the following equation: 

 Adjusted real-world % idle time = 

 Real-world % idle time x (0.6875 [mid- temperature range] + 0.2195x [1-(a+b)] 

[cold temperature range]) 

 Real-world % idle time x (0.6875 + 0.2195 x [1 - (0.1225 + 0.011)] 

Real-world % idle time x (0.6875 + 0.19) = 

 Real-world % idle time x (0.8775) = 

 13.76 x 0.8775 = 12.07% 

The above calculation assumes that the engine is warm after approximately 5 minutes 

in cold weather. It also assumes that engine-off/start-stop operation occurs during these 

conditions, even if heat is demanded by the passengers. For HEVs and PHEVs, this is a 

reasonable assumption, as today’s HEVs usually have a mechanism for idling off the engine 

during cold temperatures. ICCT commented that this should not be applicable to all stop-start 

systems. The agencies agree that for the calculation to be applicable to 12 Volt stop-start 

systems, the vehicle should have some technology to continue to deliver heat to the cabin 

even if the engine is not running. This can be done in a number of ways including an electric 

heater circulation pump, secondary loops with heat reservoirs, or some other method of 

maintaining heat transfer from the coolant. For this reason, the agencies are assuming that 

future stop-start systems will include such technologies. In this final rule, the agencies have 

removed the electric heater circulation pump from the table as proposed (and discussed 

further in the next section). The manufacturer wishing to receive the full stop-start credit 

must attest that the vehicle includes some technology to allow the engine to idle off while the 

cabin heat is demanded. For those systems who do not include this technology, the real world 

idle time calculation is modified by subtracting the credit previously proposed for electric 

heater circulation pump of 1.0 g/mi for cars and 1.5 g/mi for trucks, and is reflected in the 

credit values discussed below. 

Below are the details of our model simulations using these values and recalculating the 

stop-start, off-cycle credit. The off-cycle credit was calculated using EPA’s in-house vehicle 

simulation tool, known as ALPHA (Advanced Light-Duty Powertrain and Hybrid Analysis 

Tool). The credit was calculated for cars and trucks, separately. Vehicle simulations were 

conducted using ALPHA for small, medium, large cars and pick-up trucks. The simulations 
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were run only for FTP and Highway cycles with a baseline engine. First, the start-stop was 

deactivated for the vehicle simulations. Vehicle speed trace and fuel flow simulation results 

during the FTPcycle are shown in Figure 5-12 and Figure 5-13, respectively, for a small-size 

car. Also, the vehicle speed trace and fuel flow during Highway cycle are shown in Figure 

5-14 and Figure 5-15, respectively. 

Figure 5-12 Vehicle Speed Trace during FTP Cycle 
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Figure 5-13 Fuel Flow Trace during FTP Cycle without Start-Stop 

Figure 5-14 Vehicle Speed Trace during Highway Cycle 
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Figure 5-15 Fuel Flow Trace during Highway Cycle without Start-Stop 

Next, the start-stop was activated during the vehicle simulations. It must be noted that 

the start-stop control algorithm was written such that the engine was not turned off during idle 

for the first 300 seconds of FTP cycle to allow the engine to warm-up. Figure 5-16 and 

Figure 5-17 show fuel flow traces during FTP and Highway cycles, respectively, for a smallsize car with the start-stop activated. 
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Figure 5-16 Fuel Flow Trace during FTP Cycle with Start-Stop 

Figure 5-17 Fuel Flow Trace during Highway Cycle with Start-Stop 

The simulation results are shown in Table 5-29 and Table 5-30 below. 

Table 5-29 Vehicle Simulation Results for Start-Stop in [MPG] 

Driving Cycle Start-Stop Small-Size Car Mid-Size Car Large-Size Car Pick-up Truck

FTP

Off 36.91 26.39 21.28 14.38

On 39.52 28.82 22.95 15.33

Improve 2.61 2.42 1.67 0.95

Highway

Off 52.83 43.93 32.34 20.44

On 52.93 44.04 32.42 20.48

Improve 0.10 0.12 0.07 0.04

Combined

Off 44.08 34.28 26.26 17.11

On 45.56 35.67 27.21 17.65

Improve 1.48 1.38 0.95 0.54

Table 5-30 Vehicle Simulation Results for Start-Stop in [CO2 g/mile] 

Driving Cycle Start-Stop Small-Size Car Mid-Size Car Large-Size Car Pick-up Truck

FTP

Off 240.8 336.7 417.7 617.8

On 224.9 308.4 387.3 579.6
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Improve 15.9 28.3 30.4 38.3

Highway

Off 168.2 202.3 274.8 434.7

On 167.9 201.8 274.2 434.0

Improve 0.3 0.5 0.6 0.8

Combined

Off 208.1 276.2 353.4 535.4

On 199.2 260.4 336.4 514.0

Improve 8.9 15.8 17.0 21.4

It is evident from these results that stop start has an effectiveness of about 4-5%. The 

percentage of time the engine is at idle for the FTP/Highway cycles and real world are 10% 

and 13.76% (MOVES analysis), respectively; allowing for the previously mentioned coldstart warmup. In these calculations, the agencies determined that all of the 10% idle time in 

FTP/Highway cycles is eligible for engine stop. However, for real world conditions, the 

agencies determined a reduced engine idle time is available for engine stop; specifically 

87.75%z

of the real world idle time of 13.76%. Using these values, the agencies concluded 

eligible engine off fractional values to be 10% for the FTP/HWFET test cycles and 12.07% 

for real-world conditions. Table 5-31 shows these calculations. 

Table 5-31 Percentages of Idle eligible for Engine Off

Cycle Idle Fraction Percentage of Idle 

eligible for Engine-Off

Idle Fraction eligible for 

Engine-Off

FTP/HWFET 10.00% 100.00% 10.00%

Real-World 13.76% 87.75% 12.07%

Applying the idle fraction eligible for engine off values shown in Table 5-29 to the 

FTP/Highway combined cycles simulation values shown in Table 5-32, the agencies 

calculated the following start-stop credit values for each vehicle segment shown in Table 5-

30.

Table 5-32 Start-Stop Credit for Each Vehicle Segment 

Start-Stop Credit Small-Size Car Mid-Size Car Large-Size Car Full Size Truck

CO2 [g/mile] 1.8 3.3 3.5 4.4

The impact of Stop Start is generally dependent on engine displacement because larger 

engines generally have higher friction and pumping losses than smaller displacement engines 

at idle, and therefore have high CO2 emissions and higher idle fuel consumption. The 

differences in the credits that are available to each segment are based on the different engine 

displacements typically used in each vehicle segment. 

 

z

 This is due to temperature effects. Separate analysis was given earlier. 
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5-95 

Credits for cars and trucks are obtained by sales-weighted averaging the car credits for 

model years 2017 to 2025, in Table 5-31. Note that sales-weighted averaging was used 

between large-size car, which typically has similar engine displacements as smaller trucks, 

and full size truck to determine the truck credit.aa

Table 5-33 Start-Stop Credits for 2017 to 2025 

Year 2017 2018 2019 2020 2021 2022 2023 2024 2025

Credit Car 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

Truck 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4

Based on Table 5-33, the start-stop credits are 2.5 g/mile for cars and 4.4 g/mile for 

trucks. These are values for vehicles equipped with idle-off cabin heat technologies. For 

vehicles unequipped with such technologies, the credits are reduced to 1.5 g/mi for cars and 

2.9 g/mi for trucks. These are the credit values that EPA is finalizing for use in the final offcycle credit menu. 

5.2.8.2 Electric Heater Circulation Pump 

Conventional vehicles use engine coolant circulated by the engine’s water pump to 

provide heat to the cabin during operation in cold ambient conditions. Since the coolant is 

only circulated when the engine is running, very little heat is available to the cabin occupants 

if the engine is stopped during idle in vehicles equipped with stop-start. Stop-start equipped 

vehicles generally disable the feature during cold ambient temperatures to ensure cabin heat is 

always available. However, stop-start operation can be expanded to much colder ambient if a 

means of continuing to circulate coolant during idle stop is employed. An electric heater 

circulation pump takes the place of the engine’s water pump to continue circulating hot 

coolant through the heater core when the engine is stopped during a stop-start event. Most 

HEVs, and PHEVs are currently equipped with this technology; however, the more simple 12 

Volt Stop start systems may not be. Therefore, by definition of this technology’s function, 

only vehicles equipped with stop-start technology, HEVs, and PHEVs are eligible for this 

credit. 

Because the engine does not generate any more heat when it is shut off during idle, the 

amount of heat available to be moved to the cabin is limited by the thermal mass of the 

engine. The heater core acts like a radiator to remove heat from the engine and deliver it to 

the cabin. After some period of time, depending on engine mass, ambient temperature, and 

desired cabin temperature, the coolant temperature would drop to a level where comfort 

would not be maintained and the engine could cool off to a point where cold start features 

 

aa Many of the assumptions made for the analysis were “conservative”, others were “central”. In this example, 

an average vehicle (or high sales class) was selected on which the analysis was conducted. In this case, a smaller 

vehicle may presumably be deserving of fewer credits whereas a larger vehicle may be deserving of more. 

Where the estimates are central, it would obviously be inappropriate for the agencies to grant greater credit for 

the larger vehicles since this value is already balanced by the smaller vehicles in the fleet. The agency will take 

these matters into consideration when case by case applications are submitted for technologies that are 

modifications to the ones listed on the menu. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-96 

would be needed (which increase fuel consumption). The stop-start control system would 

turn the engine back on before either of these conditions is reached. The coolant circulation 

pump is electrically powered and therefore uses some energy when in use. 

EPA evaluated the effectiveness of this system using the same approach that was used 

for start stop technology. Based on MOVES data, we assumed the percentage of nationwide 

VMT below 45 °F is 25% and that vehicles with start stop systems will have to keep the 

engine running for cabin heat if the ambient temperature is less than 45 °F, unless the vehicle 

also has an electric heater circulation system. Therefore, we assumed a vehicle with both 

systems could utilize the start stop technology 25% more of the time. However, while 

reviewing our calculations for engine idle start-stop, we determined that there may be 

conditions where engine idle start-stop may be enabled without the use of an electric heater 

circulation pump or designs that can enable engine idle start-stop without the use of an 

electric heater circulation pump. For example, Honda commented that they were planning to 

implement such a system that would “to maintain all heating functions for more than 1 

minute” when ambient conditions are as low as 30 deg F without the use of an electric heater 

recirculation pump. 

Therefore, we are eliminating the separate credit for electric heater circulation pump 

and including the benefits of an electric heater circulation pump, or similar systems as implied 

by Honda, within the credit for engine idle start-stop. Given the interaction and synergies 

between the electric heater circulation pump and engine idle start-stop, we believe this is an 

appropriate decision. For more information on how electric heater circulation pump has been 

incorporated, see the discussion on engine idle start-stop above. 

Finally, the Alliance and Honda commented that we revise our definition and 

terminology for this vehicle. The Alliance pointed out that the main purpose is to maintain 

cabin heating and “occupant thermal comfort” without using the conventional heater. 

Therefore, this credit should be renamed to reflect this goal and purpose. Similarly, Honda 

pointed out, as mentioned above, that they are planning on implementing a system that can 

accomplish this without the use of an electric heater circulation pump. As a result, the 

definition should be expanded to include any system that can maintain cabin heating and 

occupant thermal comfort, not just an electric heater recirculation pump. 

5.2.8.3 Active Transmission Warm-Up

When a vehicle is started and operated at cold ambient temperatures, there is 

additional drag on drivetrain components due to cold lubricants becoming more viscous 

which increases fuel consumption and GHG emissions. This effect is more pronounced at 

colder temperatures and diminishes as the vehicle warms up. Components affected by this 

additional drag include the engine, torque converter, transmission, transfer case, differential, 

bearings and seals. Some components, such as the transmission, can take a long time to warm 

to operating temperature. Automakers sometimes delay the application of very effective fuelsaving measures such as torque converter lockup in order to help the transmission reach 

operating temperature more quickly. 
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5-97 

Active Transmission warm-up uses waste heat from a vehicle’s exhaust system to 

warm the transmission oil to operating temperature quickly using a heat exchanger in the 

exhaust system. This heat exchanger loop must have a means of being selectable, so that the 

transmission fluid is not overheated under hot operating conditions. In cold temperatures, the 

exhaust heat warms the transmission fluid much more quickly than if the vehicle relies on 

passive heating alone. Other methods of heating the fluid can be implemented using electric 

heat for example, but these are not included in this analysis because of the additional energy 

consumption that would likely eliminate most of the benefit. This technology could also be 

used for other driveline fluids such as axle and differential lubricant on rear-wheel-drive 

vehicles or even engine oil, but only transmission fluid warming is considered here. 

There is a lot of variability in which components are affected by cold temperatures and 

for how long due to the type of vehicle and how it is operated. Active transmission warm-up 

applied to a conventional front-wheel-drive vehicle will warm the transmission, torque 

converter, and differential lubricants because in most cases these components share the same 

lubricant. On a rear-wheel-drive vehicle such as a truck, active transmission warm-up would 

only affect the transmission and torque converter. The rear axle and differential lubricant, and 

the transfer case and front axle and differential lubricants in a four-wheel-drive vehicle would 

not be heated. Additionally, a vehicle operated under a heavy load will tend to warm these 

lubricants more quickly with or without active heating. 

Using Ricardo modeling data and environmental data from EPA’s MOVES model, 

EPA calculated the estimated benefit of active transmission warm-up. The Ricardo data 

indicates that there is a potential to improve GHG emissions by 7% at 20 °F if the vehicle is 

fully warm. EPA assumed that given that this technology only affects the transmission (and 

differential on a FWD vehicle) and that the technology does take some time to warm the 

transmission fluid, one third of this benefit would be available, or 2.3%. EPA then assumed 

the benefit would decay in a linear fashion to 0% at 72 °F. This simple relationship is 

provided in Figure 5-18 below. 

Figure 5-18 Relationship showing linear decay of GHG improvement as a function of ambient 

temperature. 

Using MOVES data, EPA then calculated a nationwide VMT -weighted average 

ambient temperature for all light duty vehicles. Based on the distribution data shown in 

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0 2 0 40 6 0 80

%GHG reduction via fast 

warmup (trans/engine)

Ambient Temp deg F
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5-98 

Figure 5-18, the weighted average temperature was calculated at 58 °F and was assumed 

uniform for all vehicle classes. Combined with the relationship assumed in Figure 5-19, this 

weighted average temperature corresponds to an average benefit due to active transmission 

warm-up of 0.58% of baseline emissions. 

Figure 5-19 Distribution of national VMT by ambient temperature 

Finally, when this 0.58% reduction is applied to baseline emissions for various vehicle 

class (as per the Ricardo-simulated 2010 baseline vehicles) the available credits, by vehicle 

class, are calculated and shown in Table 5-34. 

Table 5-34 Available credits (g/mi) based on fuel economy and CO2 benefits by vehicle class 

Vehicle Class

FTP (City)

FE 70F

FTP (City)

CO2 70F

Benefit

g/mi

Small Car 39.8 223 1.3

Midsize Car 30.0 296 1.7

Large Car 23.8 373 2.2

Large Truck 16.2 549 3.2

Using EPA’s sales schedules (see TSD Chapter 1, 1.3.3, Tables 1-13 and 1-14) and 

VMT’s (see 5.2.8.1, Table 5-28 above) for the small car, midsize car, and large car vehicle 

classes, we get average sales-weighted credit values of 1.5 grams/mi for cars, and a non-sales 

weighted 3.2 grams/mi for trucks as trucks were not disaggregated by class. No benefit is 

assumed during the FTP, so nothing is subtracted from this result. EPA believes an off-cycle 

benefit of 1.5 and 3.2 grams/mile are possible using active transmission warm-up for cars and 

trucks, respectively. 

In their comments to the NPRM, the Alliance supported the credit value of 1.8 

grams/mile for active transmission warm-up but recommended that the definition be 

broadened to account for other methods of warm-up besides exhaust heat such as a secondary 

0 %

5 %

10%

15%

20%

25%

-1 2 8 3 9 5 0 6 1 7 2 8 3 9 4 104

Portion of national VMT

Temperature deg F

% VMT (from MOVES)
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-99 

coolant loop. This sentiment for an expanded definition was also expressed by Volkswagen. 

Although we feel that waste heat from the exhaust system is one method, we are not opposed 

to other methods that provide similar performance such as coolant loops or direct heating 

elements that, albeit more costly, may prove to be more effective. Therefore, we agree with 

the commenters that the definition can be expanded and will reflect this change in the 

definitions section below. 

The comments from Chrysler advocated for specific car and truck credits for active 

transmission warm-up similar to other advanced load reduction strategies, such as engine idle 

start stop and electric heater circulation pump. Based on these comments, we expanded the 

range of vehicle classes/categories above and agree with the commenter that there is a clear 

performance difference between active transmission warm-up systems on cars versus trucks. 

Therefore, as mentioned above, we are adopting the specific car-truck credit values of 1.5 

grams/mi and 3.2 grams/mile, respectively, for active transmission warm-up. 

Finally, Honda’s comments requested clarification on systems that use a singular heat 

exchanging loop, rather than separate loops as we proposed, for active transmission and, as 

discussed below in the next section, engine warm-up. Honda indicated that all of their 

systems use a single heat exchanging loop for the transmission and engine, and, thus, would 

potentially be eligible for an additive credit of 3.6 g/mi CO2, based on our NPRM credit 

values. It is uncertain if a single heat exchanging loop would be as effective as two separate 

loops for the transmission and engine, and ultimately eligible for a combined credit (e.g., 

under our revised credit values, a total of 3.0 g/mi CO2 for cars and 6.4 grams/mi CO2 for 

trucks). The agencies currently do not have foundational data to support the effectiveness of a 

single heat exchanging loop to increase the cold start fluid warming rate for both an engine 

and transmission under various ambient temperature conditions. 

Therefore, the agencies will not grant additive, default credit values for active 

transmission and engine warm-up to systems using a single heat exchanging loop. Rather, a 

manufacturer employing such a design will be able to initiate a credit request for such a 

system would be made via the demonstration methods for technologies not on the defined 

technology list. At a minimum, the request would need to demonstrate the performance of the 

active transmission/engine warm-up for a single heat exchanging loop versus dedicated loops 

for the transmission and engine. For such a request, if the manufacturer can demonstrate 

single heat exchanging loop performance equivalent to separate, dedicated loops when 

combined for both technologies, the manufacturer may be granted the revised additive credit 

values of 3.0 g/mi CO2 for a car or 6.4 grams/mi CO2 for a truck, depending on the 

applicable vehicle category. Otherwise, if the level of performance for a single loop system 

falls short of the performance for separate cooling loops, the additive, active transmission and 

active engine warm-up credit values above will be decreased proportionately to reflect the 

lower performance of the single loop system. If the level of performance for a single loop 

system exceeds the performance for separate cooling loops, the manufacturer may receive the 

maximum, additive credit of 3.0 g/mi CO2 for a car or 6.4 grams/mi CO2 for a truck or, 

alternatively, can seek credits above these values using the demonstration methods for 

technologies not on the defined technology list. 
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5-100 

5.2.8.4 Active Engine Warm-Up

Similar to active transmission warm-up, active engine warm-up uses waste heat from a 

vehicle’s exhaust system to warm targeted parts of the engine, reducing friction and cold start 

enrichment requirements, and thereby increasing fuel economy. EPA assumed that similar to 

active transmission warm-up, a similar magnitude benefit would also be applicable for active 

engine warm-up. As a result, credit values for active engine warm-up are identical to those 

for active transmission warm-up, and are additive if a manufacturer can demonstrate the 

presence of both technologies (independent to one another, i.e., separate heating pathways) on 

a similar vehicle. Active engine warm-up test data provided by manufacturers resulted in the 

calculation of a similar emission reduction. Accordingly, the credit values of 1.5 grams/mi 

for cars and 3.2 grams/mi for trucks also apply for active engine warm-up. 

As discussed above for Active Transmission Warm-Up, the Alliance and Volkswagen 

supported the credit value of 1.8 grams/mile for active engine warm-up but recommended that 

the definition be broadened to account for other methods of warm-up besides exhaust heat 

such as a secondary coolant loop. We agree with the commenters that the definition can 

appropriately be expanded and will discuss this further in the section below on technology 

definitions. 

Also, as discussed above for Active Transmission Warm-Up, Chrysler advocated for 

separate car and truck credits and Honda for combined credit for single loop systems for 

active engine warm-up. Accordingly, our response above for active transmission warm-up 

applies to active engine warm-up as well. Therefore, we are finalizing separate car-truck 

credit values of 1.5 g/mi and 3.2 g/mi CO2, respectively, for Active Engine Warm-Up and 

single loop systems must use the demonstration methods for technologies not on the defined 

list. 

5.2.8.5 Definitions for Non-Thermal/Solar Advanced Load Reduction Technologies 

Engine start-stop is a technology which enables a vehicle to automatically turn off the engine 

when the vehicle comes to a rest and restart the engine when the driver applies pressure to the 

accelerator or releases the brake. Off-cycle engine start-stop credits will only be allowed if the 

Administrator has made a determination under the testing and calculation provisions in 40 

CFR part 600 that engine start-stop is the predominant operating mode. This technology may 

be coupled with an electric heater circulation system (or a technology that has a similar 

function), as described below, to receive maximum credit or may be implemented without an 

electric heater circulation systems for a lower amount of credit. For systems that accomplish 

the same level of performance as but do not utilize an electric heater circulation system, the 

maximum level of credit may be granted provided that equivalent level of performance is 

demonstrated by the requestor. 

Electric heater circulation system is a system installed in a stop-start equipped vehicle, hybrid 

electric vehicle or plug-in hybrid electric vehicle that continues to circulate heated air to the 

cabin when the engine is stopped during a stop-start event. This system must be calibrated to 

keep the engine off for 1 minute or more when the external ambient temperature is 30 deg F 

and when cabin heat is demanded. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-101 

Active transmission warm-up means a system that uses waste heat from the vehicle to warm 

the transmission fluid to an operating temperature range quickly using a heat exchanger. This 

reduces the parasitic losses associated with the transmission fluid, such as losses related to 

friction and fluid viscosity, thereby increasing the overall transmission efficiency. 

Active engine warm-up means a system using waste heat from the vehicle, to warm up 

targeted parts of the engine. This reduces engine friction losses and enables the closed-loop 

fuel control more quickly allowing for a faster transition from cold operation to warm 

operation, thereby decreasing CO2 emissions, and increasing fuel economy. 

5.2.9 Thermal (and Solar) Control Technologies 

In the NPRM, EPA proposed a credit for technologies that reduce the amount of solar 

energy which enters a vehicle’s cabin area, reduce the amount of heat energy build-up within 

the cabin when the vehicle is parked, and/or reduce the amount of cooling/heating energy 

required through measures which improve passenger comfort. The State of California Air 

Resources Board (CARB) has studied the effectiveness of many of these technologies, and 

had proposed including them in their Cool Cars and Environmental Performance Label 

programs.43

 The National Renewable Energy Laboratory (NREL) conducted an extensive 

research project as part of the SAE’s Improved Mobile Air Conditioning Cooperative 

Research Program (I-MAC). The purpose of this program was to study the effectiveness of a 

variety of technologies which can reduce the amount of fuel used for the purpose of climate 

control in light-duty vehicles. In this study, known as the Vehicle Ancillary Loads Reduction 

Project, NREL estimated the effectiveness of window glazing/shades, paint, insulation, and 

seat and cabin ventilation technologies in reducing A/C-related fuel consumption and 

emissions.44 EPA has evaluated these technologies and assigned a credit amount for each,

based on their ability to reduce cabin air temperatures during soak periods and thereby reduce 

the amount of cooling/heating energy required to improve passenger comfort. 

NREL’s studies estimated that when these technologies are combined, a 12 °C 

reduction in cabin air temperature during soak will result in a 26% reduction in A/C-related 

fuel consumption, or a 2.2% reduction in fuel consumption (and by extension, CO2 emissions) 

for each 1 °C reduction in cabin air temperature.45 If the A/C-related CO2 emissions impact is 

13.8 g/mi for cars and 17.2 g/mi for trucks, this 2.2% reduction in CO2 emissions results in a 

credit of 0.3 g/mi for cars (13.8 g/mi x 0.022) and a credit of 0.4 g/mi for trucks (17.2 g/mi x 

0.022) for each degree centigrade reduction in cabin air temperature. There were no 

comments submitted on this overall approach for the thermal and solar control technologies. 

5.2.10 Glazing 

When a vehicle is parked in the sun, more than half of the thermal energy that enters 

the passenger compartment is solar energy transmitted through, and absorbed by, the vehicle’s 

glazing (or glass).44 The solar energy is both transmitted through the glazing and directly 

absorbed by interior components, which are then heated, and absorbed by the glazing, which 

then heats the air in the passenger compartment through convection and interior components 

through re-radiation. By reducing the amount of solar energy that is transmitted through the 

glazing, interior cabin temperatures can be reduced, which results in a reduction in the amount 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-102 

of energy needed to cool the cabin and maintain passenger comfort. Glazing technologies 

exist today which can reduce the amount of solar heat gain in cabin by reflecting or absorbing 

some of the infrared solar energy. NREL’s study determined that cabin air temperature could 

be reduced by up to 9.7 °C with use of glazing technologies on all window locations. 

Technologies such as window films and coatings and absorptive or solar-reflective material 

within the glazing itself are currently used in automotive glazings, both for privacy (e.g., 

tinting) and improved passenger comfort. One measure of the solar load-reducing potential 

for glazing is Total Solar Transmittance, or Tts, which expresses the percentage of solar 

energy which passes through the glazing. Lower Tts values for glazing result in lower cabin 

temperatures during solar soak periods. EPA considers the April 15, 2008 version of the 

International Organization for Standardization’s (ISO) 13837 standard to be the appropriate 

method for measuring the solar transmittance of glazing used in automotive applications. 

A method for estimating the effect of the solar performance of glazing technologies 

was developed by EPA and CARB, with input from NREL and the Enhanced Performance 

Glass Automotive Association (EPGAA). This method utilizes the measured Tts of the 

glazing used in a vehicle to estimate its effect on cabin temperature during soak conditions. 

The contribution that each glass/glazing location on the vehicle has on the overall interior 

temperature reduction is determined by its Tts (relative to a baseline level) and its area. For 

purposes of this proposal, EPA considers the baseline Tts to be 62% for all glazing locations, 

except for rooflites and rear side glazings of CUVs, SUVs, and minivans, which have a 

baseline Tts of 40%.bb

 The relationship between the Tts value for glass/glazing and a 

corresponding reduction in interior temperature is has been established using the data from 

NREL testing, as shown in Table 5-35. 

Table 5-35 Effect of Tts on Interior Temperature Reduction 

Glass/Glazing 

Position

Baseline Tts 

for Glazing 

Type (%)

Solar Control 

Tts

Measured Breath 

Air Temperature 

Reduction (°C)

Estimated 

Temperature 

Reduction from 

23.8 °C Baseline 

(°C)

All 62 (solar 

absorbing)

40 9 15

All 62 (solar 

absorbing)

40 10 16

All 75 (light 

green)

50 8 8

All 75 (light 

green)

60 6 6

 

bb Glazing materials that are not subject to the requirement of >70% luminous transmittance, per 49 CFR 

571.205, are often darkened “privacy” glass, and for this credit are subject to the lower baseline Tts of 40%. 
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Using the NREL data and estimated temperature reductions, the linear correlation 

between Tts and breath air (interior) temperature reduction was developed, and is shown in 

Figure 5-11. 

Figure 5-20 Correlation between Tts and Estimated Interior Temperature Reduction 

From the slope of this correlation between the Tts value and reduction in cabin air 

(also referred to as “breath air”) temperature, a method for estimating the amount of interior 

temperature reduction (in degrees Celsius) for a specific glazing location and its Tts 

specification was developed, and is shown in Equation 5-6 .

Equation 5-6 – Estimated Breath Air Temperature Reduction for Glazing with Improved Solar Control 

where Ttsbaseline = 62 for windshield, side-front, side-rear, rear-quarter, and backlite locations, and 40 

for the rooflite location and rear side windows for SUVs, CUVs and Minivans which are typically 

darkened privacy glass. 

To determine the total amount of glass/glazing credit generated for a given vehicle, the 

contribution (in terms of estimated temperature reduction) for each glazing location is 

calculated using the glass manufacturer’s Tts specification. The contribution of each glazing 

location is then normalized to determine the effect each glazing location on the overall vehicle 

temperature reduction. The method for normalizing the contributions is to multiply the 

estimated temperature reduction of Equation 5-6 by the ratio of the glazing area of each 

location divided by the total glazing area of the vehicle. The total vehicle temperature 

reduction is the sum of the normalized contributions for each location. To calculate the 

glazing credit generated (in grams of CO2 per mile), the sum of the total vehicle temperature 

reduction (in degrees Celsius) multiplied by 0.3 for cars, or 0.4 for trucks. 

y = -0.3987x + 30

R2 = 0.949

0

2

4

6

8

10

12

14

16

18

20

80 70 60 50 40 30 20

Temp. Reduction (deg C)

Tts
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 We received several comments on the glazing credit. The ICCT agreed with the 

proposed credit and the basis for the credit described in the draft TSD. In contrast, there were 

multiple comments that fell into three main categories: 1) accounting for the overall glazing 

surface area in the calculations and a minimum level of solar transmittance, 2) concerns 

regarding metallic glazing and incentivizing this technology, and 3) granting of credit for 

polycarbonate (PC) glazing technology. 

 The Alliance, Enhanced Protective Glass Automotive Association (EPGAA), 

Guardian and Pittsburgh Glass Works (PGW) commented on the calculation accounting for 

the glazing surface area where solar control glazing is applied. Each commenter 

recommended that a factor accounting for the total surface area of the glazing be included in 

the calculation or to account for increased effectiveness of solar control glazing for vehicles 

with larger total glazing area. In addition, the Alliance suggested that a limit of 62% Tts be 

used for a technology to be eligible for the glass/glazing credit. In proposed 40 CFR 

§86.1866-12(d)(1)(i)(C), we included an equation to calculate the glazing credit as follows: 

 

where the “G” term in this equation represents “the total glass area of the vehicle, in square 

meters and rounded to the nearest tenth.” Therefore, the current equation takes into account 

the total glazing surface area. As far as applying a limit of 62% for the glazing credit, we 

agree with the commenter and the “T” term in the equation above represents the “the 

estimated temperature reduction for the glass area of each window i.” The T-term is 

determined using the following equation: 

where the “Ttsbase” term is defined as “62 for the windshield, side-front, side-rear, rearquarter, and backlite locations, and 40 for rooflite locations and rear side windows for SUVs, 

CUVs and Minivans which are typically darkened privacy glass.” Therefore, the equation for 

solar transmittance currently takes this into account and uses the 62% level as the baseline. 

As a result, any glazing with a solar transmittance level of more than 62% (i.e., a higher value 

implies that more solar energy is transmitted to the passenger compartment/cabin) would 

result in a negative credit value. Accordingly, we are finalizing the equations above as 

proposed since they address the commenter’s concerns. 

 There were multiple comments with concerns regarding the use of metallic glazing 

from the Crime Victims Unit of California (CVUC), California Manufacturers & Technology 

Association (CMTA), California State Sheriffs' Association (CSSA), California Police Chiefs 

Association (CPCA), California Narcotic Officers' Association (CNOA), CTIA - The 

Wireless Association, Garmin, Honda and TechAmerica. Many commenters stated that low 

Tts glazing uses metallic films or small metallic particles and that the credit for glazing may 

unintentionally incentivize the use of this type of glazing, metallic glazing, which can 

potentially interfere with signals for global positioning systems (GPS), cell phones, cell-phone 

based prisoner tracking systems, emergency and/or electronic 911 (E911) calls, and other 
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5-105 

signals emanating from within or being transmitted to the vehicle’s passenger 

compartment/cabin. In addition, some commenters cited this concern as the reason that the 

California Air Resources Board (CARB) removed their mandate for metallic glazing from the 

“Cool Cars” Regulation in California. 

 To address these concerns, we met with the Enhanced Protective Glass Automotive 

Association, which represents the automotive glass manufacturers and suppliers, and 

representatives from the automotive glass industry including PGW, Guardian, and AGC to 

discuss the concerns with metallic glazing and the potential for signal interference and/or 

radio frequency (RF) attenuation (details of this meeting are available in EPA docket # EPAHQ-OAR-2010-0799 and NHTSA docket #NHTSA-2010-0131). At this meeting, evidence 

was provided to the agencies showing that, in general, any glazing material can create signal 

interference and RF attenuation, and depending on the situation, RF attenuation and signal 

interference can occur without the presence of metallic glazing material. There was no 

statistically-significant increase in signal interference and RF attenuation when metallic 

glazing was used, and there are deletion areas or zones without metallic solar control around 

the edges and specific cut-outs in the metallic solar control films near the center of the dash 

area to minimize signal interference and RF attenuation. Following the meeting, a list of 

vehicles that currently use metallic glazing was also provided to the agencies demonstrating 

that this technology is currently in-use without significant signal interference/RF attenuation 

issues being raised. 

 In addition, we received comments from the California Air Resources Board (CARB) 

in response to the comments on the Cool Cars Regulation. The CARB stated that the reason 

they did not finalize a mandate for metallic glazing in the Cool Cars Regulation was primarily 

the timing for when the signal interference and RF attenuation concerns were raised. They 

also clarified that they were not requiring a specific type of glazing and that the performancebased approach ultimately adopted in the Advanced Clean Cars Regulation accomplished the 

same objectives as proposed under the Cool Cars Regulation. Finally, CARB performed 

testing of signal interference and RF attenuation by CARB (see test results in EPA docket #

EPA-HQ-OAR-2010-0799-41752) echoing the findings of the automotive glass industry that 

there is “[n]o effect of reflective glazing observed on monitoring ankle bracelets or cell 

phones” and that any “[e]ffects on GPS navigation devices [are] completely mitigated by use 

of [the] deletion window” placing either the device or the external antennae in this area. 

CARB urged EPA to finalize the proposed credit values for glass and glazing as proposed. 

 Based on this information, the agencies are finalizing the proposed credit values and 

calculation procedures for glazing. First, we are not mandating a particular technology for 

glazing. The final version of the off-cycle technology menu is technology neutral with 

manufacturers able to select the glazing technology based on desired performance. There are 

other technologies capable of rejecting solar load from the cabin and suppliers, in their 

comments, were keen to point out these alternatives. Second, we did not see evidence 

contrary to the information that the automotive glass industry and CARB presented showing 

that there would be significant adverse effects on signal interference and RF attenuation. 

However, to allay the commenters’ concerns, we will emphasize that manufacturers strongly 

consider and evaluate the potential for signal interference and RF attenuation in their vehicle 

design and glazing technology when requesting the solar control glazing credit. 
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 Next, the American Chemistry Council (ACC), Bayer Material Science, California 

Manufacturers and Technology Association, CTIA-The Wireless Association, Garmin, 

SABIC Innovative Plastics, and the Society of Plastics Industry all commented that benefits 

of polycarbonate (PC) glazing should also be reflected in the amount of the menu credit for 

glazing, and therefore that the automatic credit amount not be restricted to metallic glazing. 

These commenters pointed to PC glazing’s reduced thermal conductivity compared to glass, 

which can reduce the amount of heat transmitted into the vehicle’s passenger 

compartment/cabin, as well as to the reduced weight of PC glazing compared to other 

materials, potentially having mass reduction benefits as well. Some commenters advocated 

for a separate credit for PC glazing equivalent to the overall glazing credit we proposed. 

Further, SABIC Innovative Plastics supplied an equation to calculate thermal conductivity 

similar to the one we proposed for calculating Tts. 

 As stated above, we are not mandating a particular technology for glazing and, 

therefore, do not need believe that it is necessary to offer a separate PC glazing credit since 

this credit covers all types of glazing technologies. Also, one of the main issues with 

allowing a separate credit for PC glazing is that it is more effective when the vehicle is in 

motion since air flow cools the glazing surface and less thermal conduction occurs. Thus, this 

application is limited to a narrower operating regime and would have less effectiveness in that 

regime considering that the cabin will be cooler and require external air flow and internal air 

conditioning. 

 Additionally, we do not have information, at this time, to support the equation that 

SABIC supplied to account for thermal conductivity. In contrast, for solar transmittance, 

there are established ISO procedures (ISO 13837) that can be used and referenced to ensure a 

consistent basis for information supporting the credit request. We need to have a similar, 

established set of procedures to validate the equations, and substantiate a credit. Therefore, 

we are not including the specific equations used to calculate thermal conductivity for the 

defined technology list at this time. If manufacturers still believe that there may be some 

additional benefit, they may apply for additional glazing credit using the demonstration 

methods for technologies not on the defined technology list. 

 Finally, there was a comment from Honda advocating for the use of direct solar 

transmittance (Tds) as a measure of solar/thermal control benefits rather than Tts. While there 

may be some benefit to the use of Tds, we are not aware of sufficient data to determine 1) the 

total effectiveness of Tds and 2) if the amount of glazing credit is appropriate based on the 

effectiveness of Tds. Therefore, we are not including Tds technology as part of the glazing 

credit on the defined technology list. However, this technology may be eligible to generate 

off cycle credits based on the case-by-case demonstration procedure in the rules. 

In summary, the credits, definitions and terminology for glazing will be finalized in 

today’s action as proposed. 

5.2.11 Active Seat Ventilation 

The NREL study investigated the effect that ventilating the seating surface has on the 

cooling demand for a vehicle. By utilizing a fan to actively remove heated, humid air that is 
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typically trapped between the passenger and the seating surface, passenger comfort can be 

improved, and NREL’s Thermal Comfort Model predicted that A/C system cooling load 

could be reduced, and a 7.5% reduction in A/C-related emissions can be realized.45 While 

seat ventilation technology does not lower the cabin air temperature, it indirectly affects the 

load placed on the A/C system through the occupants selecting a reduced cooling demand due 

to their perception of improved comfort. Using the EPA estimate for the A/C-related CO2 

emissions impact of 13.8 g/mi for cars and 17.2 g/mi for trucks, a 7.5% reduction in CO2 

emissions with active seat ventilation results in a credit of 1.0 g/mi for cars (13.8 g/mi x 

0.075) and a credit of 1.3 g/mi for trucks (17.2 g/mi x 0.075). 

We received four comments on Active Seat Ventilation. The Alliance supported our 

proposed credit values of 1.0 g/mi for cars and 1.3 g/mi for trucks. In contrast, ICCT felt 

these numbers were modest based on the limited NREL dataset we used and other studies 

from the University of Denmark and a journal on “Ergonomics” that showed up to a 6.4 deg 

Celsius change in cabin temperature could be tolerated with cooled seats, equating to a credit 

of 1.9 g/mi for cars and 2.6 g/mi for trucks. However, as ICCT also points out, this is highly 

dependent on driver comfort perception and response and the real-world impact may be lower 

than anticipated in the studies the commenter cited. Therefore, we are finalizing the menu 

default credit values of 1.0 g/mi for cars and 1.3 g/mi for trucks in today’s action as proposed. 

In addition, the Alliance and MEMA commented that the definition for Active Seat 

Ventilation was too narrowly defined since it only contemplated a suction-type system to pull 

heat and reduce moisture from the seating surface. Specifically, they stated that the use of a 

forced-air system to push heat and reduce moisture from the seating surface is just as 

effective. We agree with these comments and are finalizing a broadened definition that 

allows for forced-air as well as suction-type systems. Finally, the Alliance also suggested that 

active seat ventilation technology need only be applied to the front seats in order to qualify for 

the credit. We agree with this comment since some vehicles do not have seat heaters on the 

rear seats or in the case of vehicles that only have two seats. Therefore, we will specify that, 

at a minimum, the front driver and passenger seat, or in the case of a two-seat vehicle, driver 

and passenger seats, must have active seat ventilation for a vehicle to be eligible for credit. In 

summary, we are finalizing the credit values of 1.0 g/mi for cars and 1.3 g/mi for trucks, as 

proposed, with the modifications to the definition for active seat ventilation technology. 

5.2.12 Solar Reflective Paint 

As the vehicle’s body surface is heated by solar energy when parked, heat is 

transferred to the cabin through conduction and convection. Paint or coatings which increase 

the amount of infrared solar energy that is reflected from the vehicle surface can reduce cabin 

temperature during these solar soak periods. While the amount of heat entering the cabin 

through the body surface is less than that which enters through the glazing, its effect on cabin 

air heat gain is measureable. NREL testing estimated that solar-reflective paint and coatings 

can reduce cabin air temperature by approximately 1°C, whereas glazing technologies can 

reduce cabin air temperature by up 10°C. Using the EPA estimate for credits due to cabin air

temperature reductions of 0.3 g/mi for cars 0.4 g/mi for trucks for each degree centigrade of 

temperature reduction, a 1.2°C reduction due to solar reflective paint results in a credit of 0.4 

g/mi for cars and 0.5 g/mi for trucks. 
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The comments on solar reflective paint were primarily supportive of the credit amount 

we proposed of 0.4 g/mi for cars and 0.5 g/mi for trucks for a 1.2°C of temperature reduction. 

The Alliance supported this level of credit, although they thought it might be ambitious due to 

the worst case test conditions in the National Renewable Energy Laboratory (NREL) we used 

as the basis for calculating the credit. The ICCT supported the credit since it met their 

“general principle of being verifiable and additive.” 

Honda commented that the solar reflective paint credit should only apply to certain 

colors on a sales-weighted basis since some colors cannot reflect at least 65 percent of the 

impinging infrared solar energy as required under ASTM standards E903, E1918–06, or 

C1549–09 for measuring solar reflectiveness. In addition, Honda also stated that the credit 

should only be applicable to the horizontal surfaces of the vehicle since these areas will be 

exposed to the most solar loading where solar reflective paint would be more effective. The 

credit for solar reflective paint is performance based (i.e. it is a scalar) so a manufacturer 

would have to demonstrate what level of temperature reduction they are achieving. 

Therefore, a lower level of credit would be granted under Honda’s approach if a certain color 

of paint demonstrates a lower level of temperature reduction. Regarding the comment on 

application of the credit for only horizontal surfaces, it may be possible that other surfaces 

(e.g., side quarter/door panels) under direct solar loading may be beneficial but this benefit is 

limited by the orientation of the vehicle to sunlight. As a result, we can’t guarantee that other 

areas of the vehicle will be exposed, if at all, to the extent of the horizontal surfaces. 

Therefore, we agree that the horizontal surfaces are the prime areas of benefit and will revise 

the definition for solar reflective paint to state that only the horizontal surfaces utilizing solar 

reflective paint will receive any credit under the technology menu. For the same reason, we 

do not believe that there ever could be a demonstration that other-than horizontal surfaces 

would generate off-cycle credits and therefore other-than-horizontal surfaces would not be 

eligible to generate off-cycle credits under a case-by-case demonstration. In summary, we 

will finalize the credit of 0.4 g/mi for cars and 0.5 g/mi for trucks for a 1.2 degrees centigrade 

of temperature reduction in today’s action with the revised criteria that only the horizontal 

surfaces utilizing solar reflective paint will receive any credit. 

5.2.13 Passive and Active Cabin Ventilation 

Given that today’s vehicles are fairly well sealed (from an air leakage standpoint), the 

solar energy that enters the cabin area through conductive and convective heat transfer is 

effectively trapped within the cabin. During soak periods, this heat gain builds, increasing the 

temperature of the cabin air as well as that of all components inside the cabin (i.e. the thermal 

mass). By venting this heated cabin air to the outside of the vehicle and allowing fresh air to 

enter, the heat gain inside the vehicle during soak periods can be reduced. The NREL study 

demonstrated that active cabin ventilation technology, where electric fans are used to pull 

heated air from the cabin, a temperature reduction of 6.9 °C can be realized. For passive 

ventilation technologies, such as opening of windows and/or sunroofs are and use of floor 

vents to supply fresh air to the cabin (which enhances convective airflow), a cabin air 

temperature reduction of 5.7 °C can be realized.45 Using the EPA estimate for credits due to 

cabin air temperature reductions of 0.3 g/mi for cars 0.4 g/mi for trucks for each degree 

centigrade of temperature reduction, a 6.9 °C reduction due to active cabin ventilation results 
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in a credit of 2.1 g/mi for cars and 2.8 g/mi for trucks. For passive cabin ventilation, a 5.7 °C 

temperature reduction results in a credit of 1.7 g/mi for cars and 2.3 g/mi for trucks. 

There were two main commenters regarding passive and active cabin ventilation: the 

Alliance and ICCT. The Alliance felt that a significant credit was necessary to enable 

implementation and, thus, supported the proposed credit values. Their only suggestion was to 

broaden the definition for passive cabin ventilation from “...ducts or devices which utilize 

convective airflow to move heated air from the cabin interior to the exterior of the vehicle.” to 

include the word “methods” (e.g., “...ducts, devices or methods...”) since there are other 

ways to perform passive cabin ventilation without the use of ducts or devices. For example, 

the Toyota Prius and other vehicles will lower the side windows 1/2 to 1 inch to allow for 

convection. This works absent a duct or device and the broadened definition would provide 

for such methods. 

The ICCT commented that the NREL report used as the basis for this credit lacked 

sufficient data and identified the possibility of intrusion in the case of floor-level ventilation. 

Therefore, ICCT stated that the credit for active/passive cabin ventilation should be deferred 

to establish a real-world benefit and a proper verification benchmark can be established. In 

response, the Alliance submitted supplemental comments presenting data from vehicles in the 

existing fleet and a study commissioned by General Motors and conducted by NREL to 

examine various active and passive cabin ventilation technologies. As stated by the Alliance, 

this study demonstrated the ability to achieve cabin temperature reductions on five of the 

twelve vehicles of greater than the 6.9 degrees Celsius in the NREL report cited in the TSD, 

with temperatures reductions as high as 11.4 and as low as 7.2 degrees Celsius. The agencies 

have carefully evaluated these studies and believe that they address the ICCT’s concerns 

regarding supporting data to support the level of credit proposed. 

Therefore, we are finalizing the default credit for active/passive cabin ventilation as 

proposed. In addition, as discussed above, we agree with the comments regarding the 

definition for active/passive cabin ventilation and will expand the definition to include 

“methods” that may be employed to achieve the same objective as “ducts and devices.” 

5.2.14 Summary of Thermal (and Solar) Control Credits 

 The amount of credit that a manufacturer can generate for thermal and solar control 

technologies is shown in Table 5-36. 

Table 5-36 Off-Cycle Credits for Thermal Control Technologies 

Thermal Control 

Technology

Estimated Breath Air 

Temp. Reduction

Credit (g CO2/mi)

Car Truck

Glass or glazing up to 9.7 °C up to 2.9 up to 3.9

Active Seat Ventilation N/A* 1.0 1.3

Solar reflective paint 1.2 °C 0.4 0.5

Passive cabin ventilation 5.7 °C 1.7 2.3

Active cabin ventilation** 6.9 °C 2.1 2.8

* Active seat ventilation is not a temperature reduction technology, but rather a comfort control 

technology, capable of reducing A/C-related emissions by 7.5% 
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** Active cabin ventilation has potential synergies with solar panels as described in Chapter 5.2 of this 

joint TSD. 

To generate off-cycle thermal control credits – up to a maximum of 3.0 g/mi for cars, 

and 4.3 g/mi for trucks - a vehicle must be equipped with the thermal control technology, in 

accordance with the specifications and definitions in this proposed rulemaking. If a 

technology meets the specifications, its use in a vehicle will generate credits, in accordance 

with the value set forth in the thermal control technology list. The one exception to a single 

credit value for a technology is glazing technologies, where the method for determining the 

credit is described in section 5.2.10.

5.2.15 Definitions for Solar Control Credit Technologies 

Credit for solar control technologies can be generated for MY 2017-2025 vehicles 

which utilize them. In the absence of a performance test to measure the affect of these 

technologies, For all solar control technologies except glazing, EPA will rely on 

manufacturers complying with a specification for, or description of, each technology to assure 

that the emissions reducing benefits are be realized in real-world applications. Below are the 

descriptions and specifications that EPA is adopting for the solar control technologies listed in 

Table 5-36. EPA will use these definitions and specifications to determine whether the credits 

are applicable to a vehicle. 

Active Seat Ventilation – device which draws air, forces air or transfers heat 

from the seating surface which is in contact with the occupant and exhausts it 

to a location away from the seat. At a minimum, the front driver and passenger 

seat must utilize this technology for a vehicle to be eligible for credit. If the 

vehicle only has two seats, then these seats must have active seat ventilation 

for a vehicle to be eligible for credit. 

Solar Reflective Paint – vehicle paint or surface coating applied to the 

horizontal surfaces, including the rear decklid and cabin roof, which reflects at 

least 65 percent of the impinging infrared solar energy, as determined using 

ASTM standards E903, E1918-06, or C1549-09 

Passive Cabin Ventilation – ducts, devices or methods which utilize convective 

airflow to move heated air from the cabin interior to the exterior of the vehicle 

Active Cabin Ventilation - devices which mechanically move heated air from 

the cabin interior to the exterior of the vehicle 

5.2.16 Summary of Credits 

Table 5-37 summarizes the preapproved technologies and off-cycle credits available to 

manufacturers. If manufacturers wish to receive off-cycle credits for other technologies, they 

must follow the procedures laid out in section III.C.5 of the Preamble and in the regulations at 

40 CFR §86.1869-12 (b) and (c). 

Table 5-37: Initial off-cycle credit estimates (Maximum Available Credits) 
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Technology 

Adjustments for Cars Adjustments for Trucks

g/mi gallons/mi g/mi gallons/mi

+

High Efficiency Exterior Lights*

(at 100 watt savings)

1.0 0.000113 1.0 0.000113

+Waste Heat Recovery (at 100W) 0.7 0.000079 0.7 0.000079

+

Solar Panels (based 

on a 75 watt solar 

panel)**

Battery Charging 

Only 3.3 0.000372 3.3 0.000372

Active Cabin 

Ventilation and 

Battery Charging

2.5 0.000282 2.5 0.000282

+

Active Aerodynamic Improvements (for a 

3% aerodynamic drag or Cd reduction)

0.6 0.000068 1.0 0.000113

Engine Idle Start-Stop

w/ heater 

circulation system# 2.5 0.000282 4.4 0.000496

w/o heater 

circulation system 1.5 0.000169 2.9 0.000327

Active Transmission Warm-Up 1.5 0.000169 3.2 0.000361

Active Engine Warm-up 1.5 0.000169 3.2 0.000361

Solar/Thermal Control Up to 3.0 0.000338 Up to 4.3 0.000484

* High efficiency exterior lighting credit is scalable based on lighting components selected from high efficiency 

exterior lighting list (see Joint TSD Section 5.2.3, Table 5-21). 

** Solar Panel credit is scalable based on solar panel rated power, (see Joint TSD Section 5.2.4). This credit can 

be combined with active cabin ventilation credits. 

#

In order to receive the maximum engine idle start stop, the heater circulation system must be calibrated to keep 

the engine off for 1 minute or more when the external ambient temperature is 30 deg F and when cabin heat is 

demanded (see Joint TSD Section 5.2.8.1). 

+

This credit is scalable; however, only a minimum credit of 0.05 g/mi CO2 can be granted.

5.3 Full-Size Pickup Truck Credits 

The agencies recognize that the MY 2017-2025 standards will be challenging for large 

trucks, including full size pickup trucks that are often used for commercial purposes, and so are 

taking steps to incentivize the penetration into the marketplace of “game changing” 

technologies for these pickups, including their hybridization. EPA proposed and is adopting 

per-vehicle credits for manufacturers that sell substantial numbers of mild or strong hybrid 

full size pickup trucks. The credit is 10 g/mi and 20 g/mi for mild and strong hybrids, 

respectively. EPA also proposed and is adopting a performance-based incentive credit for full 

size pickup trucks that achieve significant emissions reductions below the target level that 

corresponds to their footprint. The credit is 10 g/mi for pickups achieving 15% better CO2

than their target, and 20 g/mi for pickups achieving 20% better CO2 than their target. Access 

to all of these credits in any given model year is conditioned on achieving a minimum 

penetration of the technology in a manufacturer’s full size pickup truck sales fleet: 

For strong hybrid credits: 10% in each model year 2017 through 2025. 

For mild hybrid credits: 20-30-55-70-80% in model years 2017-2018-2019-

2020-2021, respectively. 
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For “20 percent better” performance-based credits: 10% in each model year 

2017 through 2025. 

For “15 percent better” performance-based credits: 15-20-28-35-40% in model 

years 2017-2018-2019-2020-2021, respectively. 

A number of comments were received on the proposed minimum penetration 

thresholds. These comments, and EPA’s response to them, are discussed in preamble section 

III.C.3. Credits are not available after 2025 for strong hybrids and “20 percent better” 

performance, or 2021 for mild hybrids and “15 percent better” performance. Unlike the 

hybrid credits, the performance-based credits have no technology or design requirements. 

Automakers can use any technology as long as the vehicle’s CO2 performance is at least 15% 

or 20% below its footprint-based target. A vehicle cannot receive both the hybrid and 

performance-based credit. EPA and NHTSA are coordinating to allow manufacturers to 

include “fuel consumption improvement values”, equivalent to these EPA CO2 credits, in the 

CAFE program. 

5.3.1 Full-Size Pick-up Truck Definition 

As proposed, EPA is defining a full size pickup truck based on minimum bed size and 

hauling capability, as detailed in 86.1866-12(e) of the regulations being adopted. This 

definition is meant to ensure that the larger pickup trucks which provide significant utility 

with respect to payload and towing capacity, as well as open beds with large cargo capacity, 

are captured by the definition, while smaller pickup trucks which have more limited hauling, 

payload and/or towing are not covered. A full size pickup truck is defined as meeting 

requirements (1) and (2) below, as well as either requirement (3) or (4) below: 

1) Bed Width -- The vehicle must have an open cargo box with a minimum width 

between the wheelhouses of 48 inches, measured as the minimum lateral distance between the 

limiting interferences (pass-through) of the wheelhouses, excluding any transitional arc, local 

protrusions, and depressions or pockets (dimension W202 in SAE Procedure J1100). An open 

cargo box means a cargo bed without a permanent roof or cover. Vehicles sold with 

detachable covers are considered “open” for the purposes of these criteria. And--

2) Bed Length -- The length of the open cargo box must be at least 60 inches, as 

measured at both the top of the body and at the bed floor (dimensions L506 and L505 in SAE 

Procedure J1100). And--

3) Towing Capability – the gross combined weight rating (GCWR) minus the 

gross vehicle weight rating (GVWR) must be at least 5,000 pounds. Or--

4) Payload Capability – the GVWR minus the curb weight (as defined in 40 CFR 

86.1803) must be at least 1,700 pounds. 

This definition is being finalized as proposed. The comments that were received on 

the definition, and our responses, are discussed in preamble section II.F.3. 
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5.3.2 Hybrid Pickup Truck Technology 

5.3.2.1 Mild Hybrid Technology 

Often a mild hybrid is characterized by the addition of a belt-driven starter-alternator 

of higher power capacity than a standard alternator. The drive belt system also typically has a 

feature that enables the belt tension to be maintained at proper levels during generator 

operation as well as when the starter-alternator is used to start the engine. Alternatively, an 

axial motor can be mounted on the crankshaft, usually in the bell housing before the 

transmission. This motor can be directly attached to the engine, or can be clutched to 

decouple it from the engine. The vehicle uses a conventional transmission such as an 

automatic, manual, CVT, or DCT with an appropriate conventional coupling such as a torque 

converter or clutch. 

The battery can be between 36V to over 150V nominal; generally the higher the 

voltage, the higher the performance of the system. Most mild hybrid pickups are expected to 

offer at least 100V of battery voltage due to the higher power requirement of these heavy 

vehicles. Mild hybrids are capable of start-stop operation and regenerative braking, but unlike 

strong hybrids they are not capable of any significant electric-only operation. 

Mild hybrids are less capable than strong hybrids because of lower power capability, 

but mild hybrids are lower cost and may be easier to adapt to some vehicles without making 

major powertrain, chassis or body changes. 

5.3.2.2 Strong Hybrid Technology 

Strong hybrids can take several forms. One type has an integrated transmission-drive 

motor system with a large, powerful electric drive motor-generator (often two motors). The 

transmission usually is specifically designed to integrate the motor-generator(s) and often the 

coupling between the engine and transmission such as a torque converter is removed with its 

functions handled by the electric drive motor system. The transmission can also be replaced 

by a power split device that uses a planetary gearset and two motor-generators, or a P2 

arrangement can be used with a conventional transmission augmented by an electric motor. 

Strong hybrids typically have high voltage battery packs over 300 V to provide the high 

power necessary for their increased capability. 

Strong hybrids are capable of start-stop operation, have significant braking 

regeneration capability, and are often capable of driving exclusively on battery power up to 

35-45 mph. They are also capable of launching the vehicle on electric drive alone, although 

they typically cannot accelerate above 15-20 mph while operating on electric drive 

exclusively. 

5.3.3 Mild and Strong Hybrid Pickup Truck Definitions 

In addition to meeting the definition for a full-size truck, a vehicle must meet 

additional design and performance requirements to be eligible for the hybrid full-size truck 

incentive. Mild and strong hybrids must have both stop-start capability and regenerative 

braking. Additionally, the level of hybridization (mild or strong) is characterized by the 
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amount of energy recovered into the battery: at least 65% for strong hybrids, and at least 15% 

but less than 65% for mild hybrids. These thresholds and the methodology for determining 

the amount of recovered energy are discussed below. 

5.3.3.1 Measurement of Recovered Energy 

EPA is incorporating a metric – the total percentage of available vehicle energy 

recovered over the test cycle – as a way to define levels of hybrid vehicles. For a given 

vehicle and road load profile (characterized by ETW and A, B and C dyno test “coastdown” 

coefficients), a theoretical amount of required braking energy can be calculated over the city 

and highway test cycles. This maximum braking energy is the sum of the extra braking force 

needed to slow the vehicle enough to follow the test cycle trace upon decelerations. Hybrids 

recapture a portion of this energy by driving the electric motor (in reverse) as a generator, 

which ultimately provides electrical power to the battery pack. Depending on the level of 

hybridization, this amount of recaptured energy can range between a few percent of total 

available braking energy, up to and potentially exceeding 100% of all braking energy (since 

some manufacturers also charge the battery via excess engine load when it is beneficial to do 

so). 

This metric is a way to simplify the characterization of a hybrid as a “mild” or 

“strong” hybrid. Batteries and motors must increase in scale to recover energy at a greater 

rate. As the power rating of the motor and battery increases, a greater percentage of energy 

can be recovered on rapid decelerations. So, all key facets of a hybrid system – the battery 

pack size and power rating, the motor rating, etc. – are implicitly reflected in the percentage 

of energy recovered. 

The procedure involves calculating the available braking energy on the FTP city cycle 

using the equation derived below. This value is compared to the actual energy recovered by 

the vehicle during FTP city cycle testing. Since energy into and out of the hybrid drive 

system battery is a standard part of emissions testing of hybrid vehicles, this procedure 

introduces no additional test burden. However, energy flow into the battery must be separated 

from the sum of energy into and out of the battery which is typically less than 1% of total fuel 

energy used during the test. 

The fact that some of the energy going into the battery may come directly from the 

engine means that the measured energy flow over the FTP is not, strictly speaking, just the 

recovered braking energy. Some manufacturers commenting on the proposal expressed 

concern that this would make the categorization of mild and strong hybrids subject to gaming 

by manufacturers seeking credits. They suggested that we replace this metric with one that 

only integrates current flow during decelerations (with correspondingly revised thresholds for 

percentage of energy recovered), and that we also add a second metric based on batterysupplied tractive effort only. 

 

We have evaluated these concerns and have concluded that the proposed metric 

remains adequate for our purposes, and furthermore has the advantage of being simpler and 

easier to measure than other metrics, such as measured current flow only during deceleration 

periods with zero fuel flow, or only during applied braking. Even these metrics would not 
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completely remove the potential for inclusion of engine-to-battery energy flow as sought by 

the commenters. The data that EPA collected on a 2-mode hybrid truck, discussed below, 

indicates that there is a strong correlation between EPA’s proposed metric and the energy 

recovery metric suggested by commenters. Moreover, EPA believes that adding more 

constraints in the characterization of hybrids, such as battery-only traction, would put too 

much emphasis on the particular hybrid design strategy, and our preference is to remain 

neutral on how these technologies are implemented. More fundamentally, we feel the total 

cycle energy-to-battery metric, matched with the corresponding mild and strong HEV 

thresholds we are setting, provides a fair indication of the degree of hybridization in the 

design because, given the expense involved in using larger electrical components, we would 

expect any energy flow directly from the engine to the battery to stem from real efforts to 

optimize HEV design for performance and fuel economy rather than from gaming for credit 

generation. This view is backed by the fact that the practice is common in today’s hybrids 

where there is no potential for credits. To keep from causing confusion, we are avoiding 

calling the parameter that is derived from current measurement “recovered braking energy,” 

instead simply calling it “recovered energy.” 

The measured energy into the battery is divided by the total calculated braking energy 

to determine if the vehicle is a mild or strong hybrid. We proposed that the recovered energy 

for a mild hybrid must be greater than or equal to 15% and less than 75% of the calculated 

available braking energy, and that the recovered energy for a strong hybrid must be at or 

above 75%. We based these proposed thresholds on available test data collected on hybrid 

vehicles, none of which were large pickup trucks. Chrysler commented that the 75% 

threshold for strong hybrids, though it may be appropriate for passenger cars, is too 

demanding for large pickup trucks designed with powerful braking systems to safely handle 

large towing loads. In response, EPA has conducted tests, using the methodology described 

in this TSD section, on a Silverado 1500 2-mode HEV truck. This is the only large light-duty 

truck currently on the market that is generally considered to be a strong hybrid.46 47 The 

results over 6 repeat tests varied from 68% to 78%. Based on this testing, we believe 65% is a 

more appropriate threshold than the proposed 75% for defining strong hybrids, and so are 

adopting this threshold into the final regulations. We are retaining the proposed 15% 

threshold for mild hybrids, consistent with comments received. 

It should be kept in mind that these thresholds and the associated metric for evaluating 

candidate hybrids are intended to provide a general, non-technology-specific parallel to the 

hybrid technology overview in section 5.3.2 above. Their purpose is to clearly, fairly, and as 

simply as possible define eligibility for credits. They are not meant to be used in any way as 

an industry standard for these terms. We recognize too that technology evolution or new 

information may make it helpful to reconsider these criteria, and believe that the mid-term 

review may provide a suitable forum for doing so. 

5.3.3.2 Spreadsheet documentation and calculation methodology details 

Equation 5-7 defines the brake energy recovery efficiency (expressed as a percentage), 

or ηrecovery: 

Equation 5-7: 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-116 

max_

cov

cov

brake

re ered

re ery E

E

Erecovered, the total energy recovered over the 4-bag FTP test (in kWh) is calculated in 

Equation 5-8.

Equation 5-8: 

3600*1000

)(

cov

tiV dt

Ere ered

With i(t) defined as measured current into the battery (in amps) and V defined as the 

nominal battery pack voltage. Current flowing out of the battery (discharge) is not included. 

Battery current is measured via a current clamp probe, mounted directly upstream of the 

battery pack. Both battery current and vehicle speed data should be collected at a sampling 

rate of 10 Hz. 

We received comments expressing concern that nominal voltage is a poorly-defined 

term, determined in the industry in a variety of ways, and is therefore subject to gaming. In 

response, and after discussion with industry representatives including developers of SAE 

J2711,48 we are defining nominal voltage in the following manner: Determine nominal 

voltage of the battery by taking one battery voltage measurement immediately following 

“key-on” for the FTP, taking a second battery voltage measurement immediately prior to 

“key-off” for the FTP, and then averaging the pre- and post-FTP voltages. The initial voltage 

measurement may occur any time between “key-on” and up to 10 seconds following the “keyon” event. The second voltage measurement may occur up to 10 seconds before the “key-off 

event”. Based on data we have reviewed from actual vehicle testing, we expect that this 

straightforward methodology will be adequate for the purposes of this credit program, because 

current flow at these times is typically very low.49 However, if a manufacturer’s test data 

shows that the absolute value of the measured current to and from the battery during either of 

the voltage measurements exceeds 3.0% of the maximum absolute value of the current 

measured over the FTP, the manufacturer is expected to develop an alternative means of 

determining nominal voltage, subject to EPA approval. 

In order to allow verifiable measurements of nominal voltage, the manufacturer 

wishing to make use of this optional credit provision will need to broadcast battery pack 

voltage on an on-board diagnostics (OBD) parameter ID (PID) channel. Battery voltage is 

already publically available on enhanced PIDs but the protocol is not consistent across 

manufacturers. Making the data available on an OBD PID will make the procedure for 

recording battery voltage consistent across vehicle manufacturers and will allow verification 

of nominal voltage measurements during confirmatory and other testing by EPA. 
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Ebrake_max (kWh) is calculated by integrating required braking power (Pbrake) at each 

point in the test cyclecc over the entire test, shown in Equation 5-9. For clarity, the prescribed 

vehicle speed test schedule (not the recorded vehicle speed test data) is used in this 

calculation. 

Equation 5-9 

3600

)(

max_

tP dt

E

brake

brake

Pbrake (kW) – the vehicle braking power required to follow the drive trace during 

decelerations – represents the amount of braking force (expressed as power) in addition to the 

existing road load forces which combine to slow the vehicle. It is expressed in Equation 5-10. 

By convention, only negative values are calculated for braking.dd

Equation 5-10

brake PP _ reqdaccel Proadload

Paccel_reqd (kW), in represents the total applied deceleration power necessary to slow 

the vehicle. It is calculated as the vehicle speed, v (in m/s) multiplied by the deceleration 

force (vehicle mass * required deceleration rate), as shown in Equation 5-11.

 Equation 5-11

dt

dv

reqdaccel mvP ETW

** _

Where: 

mETW (kg) is the mass of the vehicle based on equivalent test weight (ETW) 

dv/dt (m/s2) is the required acceleration/deceleration for the vehicle to match the next 

point on the vehicle test trace (as recorded at a 10 Hz rate) 

Proadload (kW) is the sum of the road load forces (N) as calculated from the 

experimental vehicle coastdown coefficients, A, B and C. It is calculated in Equation 5-12. 

 

cc These calculations assume a “4-bag” FTP schedule, or 2 consecutive UDDS cycles (cold-start UDDS with a 10 

minute soak period and a second hot-start UDDS), as is common for testing HEVs for charge balancing 

purposes. 

dd All power terms are negative when power is applied to the vehicle (as in braking). Power provided by the 

vehicle (such as tractive power – in the case of acceleration) would be positive. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-118 

Per convention, road load is negative as it always represents a deceleration (resistive) force 

acting on the vehicle. 

Equation 5-12

2 * AvP Bv Cv roadload

5.3.4 Pickup Truck Performance Thresholds for Advanced Technology Credits 

This section describes how the agencies arrived at the threshold values of 15% and 

20% better than the footprint target required to qualify a pickup truck for performance-based 

advanced technology credits. 

Based on the lumped parameter model (described in Chapter 3 of this joint TSD), 

pickup truck hybrids are determined to be approximately 15% more efficient than nonhybrids. However, this can vary over a range of efficiencies depending on the weight, 

electrification level, HEV architecture, engine/transmission, utility ratings, control strategy, 

etc. Rather than comparing directly to a given HEV technology, we have instead determined 

the thresholds based on the year-on-year stringency of the standards (targets). Although we 

discuss these thresholds in terms of GHG standards, a corresponding analysis could be made 

based on fuel economy targets. 

The targets (curve standards at a given footprint) become more stringent each year. 

However, a typical vehicle model is redesigned every 5 years (6 years for some larger trucks). 

When a vehicle model is redesigned, it is assumed that the emissions will not just meet the 

footprint target, but rather exceed it so that in general the vehicle is generating credits for the 

first two or three years of the product life, and using credits for the latter two or three years, 

until the next redesign. Although no individual vehicle is required to meet its footprint target, 

the manufacturer must meet its fleet obligation based on the footprint and sales volumes of all 

the light-duty vehicles it produces. Therefore, under normal (business-as-usual) 

circumstances, each manufacturer will be designing and redesigning some of their vehicle 

models each year and some vehicles will exceed their targets (for about 2-3 years each) and 

others will fall short (for about 2-3 years each), thus allowing the manufacturer to average its 

fleet in order to comply each year. Recognition of this product development cycle is an 

important element of the program structure. 

In the following hypothetical example, illustrated in Figure 5-21, a recently redesigned 

58 square foot pickup truck is certified in MY 2018. Its target is 308 g/mi. This truck will 

not receive another redesign until 2022. Under normal circumstances, a typical vehicle would 

likely be 10% better than the standard, which would make it a credit generator for three years 

and a deficit generator for two years (consistent with the usual regulatory strategy outlined in 

the previous paragraph). At 15% below target (262 g/mi) this truck will generate credits for 

four years and deficits only in its last year. At 20% below target (246 g/mi), the truck will 

generate credits for the full five year product development cycle. 

Figure 5-21 2017-2025 Truck GHG Standard Curves, with Example Redesign of a 58 square foot truck 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-119 

The analysis depends somewhat on the footprint selected. Table 5-38 shows the truck 

footprint targets for each model year for three sample trucks with footprints: 58, 67 and 74 sq 

ft, and three scenarios: 10%, 15% and 20% better than the target value. The table also shows 

the number of years each of the sample trucks would take to start generating deficits. In the 

10% scenario, the trucks create deficits in 3.4 years on average. In the 15% scenario, it takes 

4.7 years and for the 20% scenario it takes 5.7 years on average. Based on this analysis, the 

agencies have chosen the 15% and 20% thresholds, as these are significantly better than the 

business-as-usual (~10%) scenario. The performance thresholds of 15% and 20% therefore 

represent CO2 reductions greater than what EPA expects companies would typically plan for 

during a redesign of these products, given the level of the standards and the CO2 targets for 

typical full-size pickup trucks. In addition, since the rate of improvement of using hybrid 

technology on full size pickup trucks is approximately 15% (as noted at the start of this 

section), this rate of improvement over target is comparable to what would be achieved by 

applying hybrid technologies to the same vehicles. It is consequently reasonable to provide 

an equivalent credit amount. These levels are also technically within reach of the companies 

if they pull ahead technologies which they may not otherwise need until the later years of the 

program, or in the case of the later years of the program, a pull-ahead of technologies beyond 

what is needed for MY2025. 

Table 5-38: Truck CO2 Footprint Targets for 10%, 15% and 20% Thresholds 

200

220

240

260

280

300

320

340

360

50 55 60 65 70 75 80 CO2 Targets (g/mi)

Vehicle Footprint (square feet)

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

58 sq. feet

5% below 2018

20% below 2018

15% below 2018

10% below 2018
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-120 

Footprint 10% better than std

# of yrs before creating

deficits

58.0 67.0 74.0 58.0 67.0 74.0 58.0 67.0 74.0

2017 315 347 347 2017 283 312 312 4 4 6

2018 308 342 342 2018 277 308 308 3 3 5

2019 299 339 339 2019 269 305 305 2 3 5

2020 290 331 337 2020 261 298 303 2 2 4

2021 268 307 335 2021 241 276 301 3 3 3

2022 255 292 321 2022 230 263 289 3 3 3

2023 243 278 306 2023 219 250 275

2024 231 265 291 2024 208 238 262

2025 220 252 277 2025 198 227 249

avg 3.4

Footprint 15% better than std # of yrs before creating deficits

Footprint 58.0 67.0 74.0 58.0 67.0 74.0 58.0 67.0 74.0

2017 315 347 347 2017 268 295 295 5 5 7

2018 308 342 342 2018 262 290 290 4 5 7

2019 299 339 339 2019 254 288 288 4 4 6

2020 290 331 337 2020 246 281 286 3 3 5

2021 268 307 335 2021 228 261 285 4 4 4

2022 255 292 321 2022 217 248 273

2023 243 278 306 2023 207 236 260

2024 231 265 291 2024 197 225 247

2025 220 252 277 2025 187 214 236

avg 4.7

Footprint 20%

better than 

std # of yrs before creating deficits

Footprint 58.0 67.0 74.0 58.0 67.0 74.0 58.0 67.0 74.0

2017 315 347 347 2017 252 278 278 6 7 8

2018 308 342 342 2018 246 273 273 5 6

2019 299 339 339 2019 239 271 271 5 5

2020 290 331 337 2020 232 265 269 4 5

2021 268 307 335 2021 215 245 268

2022 255 292 321 2022 204 234 257

2023 243 278 306 2023 194 223 244

2024 231 265 291 2024 185 212 233

2025 220 252 277 2025 176 202 222

avg 5.7
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Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-122 

References:

1

Schwarz, W., Harnisch, J. 2003, “Establishing Leakage Rates of Mobile Air Conditioners,” 

Prepared for the European Commission (DG Environment), Doc B4-

3040/2002/337136/MAR/C1. This document is available in Docket EPA-HQ-OAR-2009-

0472-0157. 

2 Vincent, R., Cleary, K., Ayala, A., Corey, R., “Emissions of HFC-134a from Light-Duty 

Vehicles in California,” SAE 2004-01-2256, 2004. This document is available in Docket 

EPA-HQ-OAR-2009-0472-0186. 

3

EPA, 2009, “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007,” 

http://www.epa.gov/climatechange/emissions/usinventoryreport.html. This document is 

available in Docket EPA-HQ-OAR-2009-0472. 

4 General Motors Press Release, “GM First to Market Greenhouse Gas-Friendly Air 

Conditioning Refrigerant in U.S.,” July 23, 2010. 

5 Malvicino, C., Seccardini, R,, et al, “B-COOL Project – Ford Ka adn Fiat Panda R-744 

MAC Systems,” SAE 2009-01-0967, 2009. 

6 Ghodbane, M., Craig, T., Baker, J., “Demonstration of Energy-Efficient Secondary Loop 

HFC-152a Mobile Air Conditioning System – Final Report,” U.S. Environmental Protection 

Agency Report EP07H001055, 

http://www.epa.gov/cpd/mac/152a/EPASec%20Loop_07Jan08.pdf. 

7 Ghodbane, M., Baker, J., Kadle, P., “Potential Applications of R-152a Refrigerant in 

Vehicle Climate Control Part II,” SAE 2004-01-0918, 2004. 

8

Peral-Antunez, E., “Recent Experiences in MAC System Development – New Alternative 

Refrigerant Assessment Technical Update,” SAE Alternative Refrigerant System Efficiency 

Symposium, Scottsdale, AZ, September 28, 2011. 

9

 Li, X., “Alternative Refrigerant AC6 Performance Evaluation and Optimization Potential,” 

SAE Alternative Refrigerant System Efficiency Symposium, Scottsdale, AZ, September 28, 

2011. 

10 Society of Automotive Engineers Surface Vehicle Standard J2727, issued August, 2008, 

http://www.sae.org. This document is available in Docket EPA-HQ-OAR-2009-0472-0160. 

11 State of California, Manufacturers Advisory Correspondence MAC #2009-01, 

“Implementation of the New Environmental Performance Label,” 

http://www.arb.ca.gov/msprog/macs/mac0901/mac0901.pdf. This document is available in 

Docket EPA-HQ-OAR-2009-0175. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

5-123 

12 State of Minnesota, “Reporting Leakage Rates of HFC-134a from Mobile Air 

Conditioners,” http://www.pca.state.mn.us/climatechange/mac-letter-082908.pdf. This 

document is available in Docket EPA-HQ-OAR-2009-0472-0178. 

13 Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy, 

“Transportation Energy Data Book: Edition 27,” 2008. This document is available in Docket 

EPA-HQ-OAR-2009-0472 

14 EPA, “Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel 

Economy Estimates - Final Technical Support Document,” 179 pp, 2.1MB, EPA420-R-06-

017, December, 2006. This document is available in Docket EPA-HQ-OAR-2009-0472. 

15 Ikegama, T., Kikuchi, K., “Field Test Results and Correlation with SAEJ2727,” 

Proceedings of the SAE 7th Alternative Refrigerant Systems Symposium, 2006. This 

document is available in Docket EPA-HQ-OAR-2009-0472. 

16 Atkinson, W., Baker, J., Ikegami, T., Nickels, P., “Revised SAEJ2727: SAE Interior 

Climate Control Standards Committee Presentation to the European Commission,” 2006. 

This document is available in Docket EPA-HQ-OAR-2009-0472. 

17 Burnette, A., Baker, R., “A Study of R134a Leaks in Heavy Duty Equipment,” CARB 

Contract No. 06-342, http://www.arb.ca.gov/research/seminars/baker3/baker3.pdf. 

18 Society of Automotive Engineers, “IMAC Team 1 – Refrigerant Leakage Reduction, Final 

Report to Sponsors,” 2006. This document is available in Docket EPA-HQ-OAR-2009-0472-

0188. 

19 Minnesota Pollution Control Agency, “Model Year 2009 Leakage Rate List,” 

http://www.pca.state.mn.us/climatechange/mobileair.html. This document is available in 

Docket EPA-HQ-OAR-2009-0472-0161. 

20 Society of Automotive Engineers, “IMAC Team 4 – Reducing Refrigerant Emissions at 

Service and Vehicle End of Life,” June, 2007. This document is available in Docket EPAHQ-OAR-2009-0472. 

21 Schwarz, W., “Emission of Refrigerant R-134a from Mobile Air-Conditioning Systems,” 

Study conducted for the German Federal Environmental Office, September, 2001. This 

document is available in Docket EPA-HQ-OAR-2009-0472. 

22 “A/C Triage – Ensuring Replacement Compressor Survival, “ AC Delco Tech Connect, 

Volume 12, Number 5, January/February, 2005, 

http://www.airsept.com/Articles/CompressorGuard/ACDelcoTechConnectJanFeb05.pdf. This 

document is available in Docket EPA-HQ-OAR-2009-0472-0167. This document is available 

in Docket EPA-HQ-OAR-2009-0472-0167. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

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23 Johnson, V., “Fuel Used for Vehicle Air Conditioning: A State-by-State Thermal ComfortBased Approach,” SAE 2002-01-1957, 2002. This document is available in Docket EPA-HQOAR-2009-0472-0179. 

24 Rugh, J., Johnson, V., Andersen, S., “Significant Fuel Savings and Emission Reductions by 

Improving Vehicle Air Conditioning,” Mobile Air Conditioning Summit, Washington DC., 

April 14-15, 2004. This document is available in Docket EPA-HQ-OAR-2009-0472-0179. 

25 California Environmental Protection Agency Air Resources Board, “STAFF REPORT: 

Initial statement of reasons for proposed rulemaking, public hearing to consider adoption of 

regulations to control greenhouse gas emissions from motor vehicles,” 2004. This document 

is available in Docket EPA-HQ-OAR-2009-0472. 

26 “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast States 

Center for a Clean Air Future, September, 2004. This document is available in Docket EPAHQ-OAR-2009-0472. 

27 Lee, S., Lee, B., Zheng, H., Sze, C., Quinones, L., and Sanchez, J., “Development of 

Greenhouse Gas Emissions Model for 2014-2017 Heavy- and Medium-Duty Vehicle 

Compliance,” SAE 2011 Commercial Vehicle Engineering Congress, Chicago, September 

2011, SAE Paper 2011-01-2188.

28 Forrest, W.O., “Air Conditioning and Gas Guzzler Tax Credits,” Society of Automotive 

Engineers, International Congress & Exposition, Detroit, Michigan, March 2002, SAE Paper 

2002-01-1958. 

29 Forrest, W.O. and Bhatti, M.S., “Energy Efficient Automotive Air Conditioning System,” 

Society of Automotive Engineers, International Congress & Exposition, Detroit, Michigan, 

March 2002, SAE Paper 2002-01-0229. 

30 Johnson, V.H., “Fuel Used for Vehicle Air Condition: A State-by-State Thermal ComfortBased Approach,” Society of Automotive Engineers, International Congress & Exposition, 

Detroit, Michigan, March 2002, SAE Paper 2002-01-1957. 

31 Rugh, J.P., Hovland, V., Andersen, S.O., “Significant Fuel Savings and Emissions 

Reductions by Improving Vehicle Air Conditioning,” Presentation at the 15th Annual Earth 

Technologies Forum and Mobile Air Conditioning Summit, April 15, 2004. 

32 Northeast States Center for a Clean Air Future, “Reducing Greenhouse Gas Emissions from 

Light-Duty Motor Vehicles,” September, 2004. 

33 EPA and DOT, “Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate 

Average Fuel Economy Standards: Final Rule,” May 7, 2010. 

34 Society of Automotive Engineers, “IMAC Team 2 – Improved Efficiency, Final Report,” 

April 2006. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

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35 Memo to docket, “Meeting with Delphi and Presentation to EPA,” March, 2009. This 

document is available in Docket EPA-HQ-OAR-2009-0472-0196. 

36 Society of Automotive Engineers Surface Vehicle Standard J2765, issued October, 2008, 

http://www.sae.org. This document is available in Docket EPA-HQ-OAR-2009-0472-0171. 

37 Barbat, T. et. Al., “CFD Study of Phase Separators in A/C Automotive Systems,” SAE 

2003-01-0736, 2003. This document is available in Docket EPA-HQ-OAR-2009-0472-0187. 

38 Memo to docket, “Idle Test Data Submitted by USCAR to EPA,” July, 2011. 

39 U.S. EPA, “Project Report: Computer Simulation of Light-Duty Vehicle Technologies for 

Greenhouse Gas Emission Reduction in the 2020-2025 Timeframe”, Contract No. EP-C-11-

007, Work Assignment 0-12, Docket ID: EPA-HQ-OAR-2010-0799, Document ID: EPAHQ-OAR-2010-0799-1144 

40 Bradfield, M., “Improving Alternator Efficiency Measurably Reduces Fuel Costs”, Remy, 

Inc., 2008. 

41 Schoettle, B., et al., “LEDS and Power Consumption of Exterior Automotive Lighting: 

Implications for Gasoline and Electric Vehicles,” University of Michigan Transportation 

Research Institute, October, 2008. 

42 American Honda Motor Co. comments, NHTSA Docket ID# NHTSA-2010-0131-0239, 

EPA Docket ID# EPA-HQ-OAR-2010-0799-9489.

43 State of California Air Resources Board, Draft Manufacturer’s Advisory Correspondence – 

Optional Test Procedure for Certifying Model-Year 2016 and Later Vehicles Under the Cool 

Cars Alternative Performance Compliance Option, March 3, 2010. 

44 Rugh, J., Farrington, R. “Vehicle Ancillary Load Reduction Project Close-Out Report,” 

National Renewable Energy Laboratory Technical Report NREL/TP-540-42454, January, 

2008. 

45 Rugh, J., et al., “Reduction in Vehicle Temperatures and Fuel Use from Cabin Ventilation, 

Solar-Reflective Paint, and New Solar-Reflective Glazing,” SAE 2007-01-1194, 2007. 

46 EPA Technical Memorandum from James Sanchez and Ben Ellies, May 21, 2012: “Energy 

Recovery Testing for GM Silverado 2-Mode Hybrid and Other Hybrid Light-duty Vehicles”. 

47 EPA e-mail memorandum from Ben Ellies to Don Kopinski, May 23, 2012, “Data for GM 

2-mode hybrid pickup truck”.

48 E-mail correspondence from James Sanchez to Eric Rask, May 3, 2012. 
Air Conditioning, Off-Cycle Credits, and Other Flexibilities 

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49 EPA memorandum from James Sanchez to Docket EPA-HQ-OAR-2010-0799, July 23, 

2012: “Analysis of ANL’s HEV Data to Evaluate Methods for Determining Nominal 

Voltage”.