NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Comparison of Vehicle 

Efficiency Technology 

Attributes and Synergy 

Estimates 

G. Duleep

ICF Incorporated, LLC.

Fairfax, Virginia

Subcontract Report

NREL/SR-6A20-47806

February 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Comparison of Vehicle 

Efficiency Technology 

Attributes and Synergy 

Estimates 

G. Duleep

ICF Incorporated, LLC.

Fairfax, Virginia

NREL Technical Monitor: Caley Johnson

Prepared under Subcontract No. KACX-8-88312-00

Subcontract Report

NREL/SR-6A20-47806

February 2011
This publication received minimal editorial review at NREL.

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1 

1.0 Introduction

Analyzing the future fuel economy of light-duty vehicles (LDVs) requires detailed 

knowledge of the vehicle technologies available to improve LDV fuel economy. The 

National Highway Transportation Safety Administration (NHTSA) has been relying on 

technology data from a 2001 National Academy of Sciences (NAS) study (NAS 2001) on 

corporate average fuel economy (CAFE) standards, but the technology parameters were 

updated in the new proposed rulemaking (EPA and NHTSA 2009) to set CAFE and 

greenhouse gas standards for the 2011 to 2016 period. The update is based largely on an

Environmental Protection Agency (EPA) analysis of technology attributes augmented by 

NHTSA data and contractor staff assessments. These technology cost and performance 

data were documented in the Draft Joint Technical Support Document (TSD) issued by 

EPA and NHTSA in September 2009 (EPA/NHTSA 2009). 

For these tasks, the Energy and Environmental Analysis (EEA) division of ICF 

International (ICF) examined each technology and technology package in the Draft TSD 

and assessed their costs and performance potential based on U.S. Department of Energy 

(DOE) program assessments. ICF also assessed the technologies’ other relevant attributes 

based on data from actual production vehicles and from recently published technical 

articles in engineering journals. ICF examined technology synergy issues through an ICF 

in-house model that uses a discrete parameter approach.

The review of the technology attribute data in the TSD and in the regulatory impact 

analysis (RIA) showed that the attribute data are, in most cases, consistent with data 

developed by ICF, and that some of the differences in retail price equivalent (RPE) in 

those data are attributable to differences between the markup for retail price used in the 

TSD and that used by ICF. Tables 1-1 through 1-4 show the comparisons of the ICF data 

with those published in the TSD. The ICF data are based on a comprehensive review of 

estimates published by manufacturers and suppliers to the industry. In a few areas of 

concern, we believe the TSD data are inconsistent with most other analyses of attributes 

or internally inconsistent with the TSD analysis of other comparable technology. This 

report first comments on technology RPE in the TSD and then on the fuel economy 

benefit for each technology. 
2 

Table 1-1. Spark Ignition Engine Technologies

Technology RPE ($) Fuel Consumption 

Reduction (%) 

EEA 2008 

V6a

EPA/NHTSA

2009b

EEA 2008 

V6a

EPA/NHTSA

2009b

DOHCc Variable Valve Timing 

(Intake)

104 80 1.5 1 

Variable Valve Timing (Intake + 

Exhaust) SOHCd

104 80 1.6 2 

Variable Valve Timing (Dual) 

DOHC

184 157 2.2 2 

Variable Valve Lift and Timing—

Intake Continuous (DOHC)— 

Constant Displacement

460 449 7.4 4 

Cylinder Deactivation with Noise 

Control

310 150

(noise control?)

6.1 6 

Camless Valve Actuation—

Constant Displacement

900 501 13 5–15

Stoichiometric Gasoline Direct 

Injection (SGDI)

230 287 3.4 2–3

(page 3-29)

Turbocharging with Engine 

Downsized (no credit)

500 329 6.5 5–7

Turbocharging with Gas Direct 

Injection (GDI) and Engine 

Downsizing

220

(V6 to l4)

490 (est.) 13.0 12–14

(uncertain)

Improved Lubricating Oil 20 3 1.0 0.5

Engine Friction Reduction III 65 75 1.5 1–3 a EEA baseline is a port fuel injected (PFI), four-valve, fixed-valve V6 engine with a compression ratio (CR) of 

9.5. Unless otherwise noted, the numbers are at constant performance. b The EPA baseline vehicle is defined as one with fixed timing, four valves per cylinder, PFI, and a four-speed 

automatic transmission, unless specifically noted. Minivan class is used because all minivans use V6 engines. c DOHC stands for double overhead cam

d SOHC stands for single overhead cam
3 

Table 1-2. Body and Accessory Technologies

Technology RPE ($) Fuel Consumption 

Reduction (%) 

EEA 2008 EPA/NHTSA

2009

EEA 2008 EPA/NHTSA

2009

Weight Reduction by 5% 1.00/lb 1.48/lb 

(page 3-73)

3.1 3–4 

Rolling Resistance Reduction by 10% 20 6 1.5 1–2 

Drag Reduction by 20% 70 42 3.8 3 

Alternator Improvements 17 76 0.5 1–2 

Electric Accessories 50 (water 

pump)

0.5

Electric Power Steering 80 94 2.0 2 

Table 1-3. Transmission Technologiesa

Technology RPE ($) Fuel Consumption 

Reduction (%) 

EEA 2008 EPA/NHTSA

2009

EEA 2008 EPA/NHTSA

2009

Five-Speed Automatic Transmissions 200 90 2.4 2.5

Six-Speed Automatic Transmissions 220 150 4.3 4.5–6.5

Automated Manual Transmissions (sixspeed, wet clutch)

240 289 6.5 9.5

Continuously Variable Transmissions 380 224 5.7 6 

Early Torque Converter Lockup 5 25 0.5 0.5

Aggressive Shift Logic 30 28 1.5 1–2 

a

All numbers compared to four-speed automatic, unless otherwise noted.
4 

Table 1-4. Hybrid and Diesel Technologies

Technology RPE ($) Fuel Consumption 

Reduction (%) 

EEA 2008a EPA/NHTSA

2009

EEA 2008a EPA/NHTSA

2009

Stop-Start 12V 250 118

(page 3-30)

3.8 2-2.5

(page 3-30)

Belt Alternator 

Starter (BAS) 

Hybrid

600 351

(small car, 42V)

10 7.5

(small car, stopstart 42V)

Integrated Motor 

Assist (IMA) 

Hybrid 

2,500 2,854

(small car, w/ 

engine downsize)

25 30

(small car, w/ 

engine 

downsize)

Two-Motor Hybrid 3,800 3,967

(small car, power 

split)

33 35

(small car, 

power split)

Diesel I4

Diesel V6

2,700

3,800

2,164

2,961

(Table 3-10, SCR 

based)

25

27

19

25

(page 3-37)

a For hybrids, EEA RPE estimates are for hybrid components only, and fuel consumption reduction is for 

system level and includes engine downsizing credits. For the diesel, the value range represents engine 

cylinder count from I4 to V6. The diesel RPE includes after-treatment cost. All RPE numbers reflect 2015 

timeframe scale and learning-based cost reduction. 

Figures from:

• ICF-EEA, “Advanced Technologies to Improve Fuel Economy of Light Duty 

Vehicles.” Fuel consumption figures derived from fuel economy. (ICF-EEA 

2007) 

• The U.S. EPA and NHTSA, “Draft Joint Technical Support Document, Proposed 

Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards 

and Corporate Average Fuel Economy Standards,” September 2009 

(EPA/NHTSA 2009). 

• EPA’s figures are used for comparison. EEA RPE values are compared against 

Table 3-25 through 3-29 values, the Incremental Piece Costs for Technologies 

Marked-up to Include both Direct and Indirect Costs in 2016 (2007$). EPA’s 

technology effectiveness is expressed as absolute CO2% reduction from baseline 

vehicle in Tables 3-30 through 3-34 of EPA/NHTSA (2009). 
5 

For fuel economy differences, the TSD gives a range and appears to have rounded the 

estimates to the nearest percent. Ignoring round-off errors, the largest differences in 

relative terms are as follows: 

• Continuously variable valve lift

• Turbocharged GDI engines with downsizing, where there is some uncertainty 

regarding the TSD estimate

• Dual-clutch, six-speed, automated manual transmissions 

• Stop-start technology. 

In some cases, as for the turbocharged GDI engine, the TSD assumptions are not clear 

because the quoted marginal benefits are inconsistent with the total benefit of 

turbocharging plus GDI. 

For prices, several issues make direct comparisons difficult because the TSD uses a 

different method to convert cost to RPE than the ICF one used. We see, however, several 

other specific issues, which we discuss in Section 2. Fuel economy related issues are 

considered in the following section. 
6 

2.0 Technology Unit Retail Price

ICF has concerns about the RPE data in four areas: the RPE for technologies replaced 

over the life of the vehicle, cost savings of engine cylinder count reduction, costs of 

diesel engines, and the multiplier used to convert cost to RPE. A more general issue is the 

adjustment of costs estimated in different years to current dollars using the gross 

domestic product deflator. It is not clear why this was chosen to adjust costs because it is 

well known that auto-industry costs have been falling in real terms for the last decade. 

The Producer Price Index for Automobiles (Bureau of Labor Statistics (BLS) 2010) 

should be used to adjust costs to constant year numbers. 

The first issue is applicable to lubricants and reduced rolling resistance tires. Lubricants 

are replaced multiple times over a vehicle’s life—some may be covered under 

manufacturers’ free service policy and others are paid for by the vehicle owner. 

Similarly, tires are replaced three to four times over a vehicle’s life. The regulatory 

analysis in the TSD counts only the incremental cost of the first cost.

For the second issue, the cost savings from engine downsizing, the TSD estimates the 

cost for various engine configurations by using a formula that assumes $50 per cylinder, 

$10 per valve, and $100 per camshaft, per Table 3-5 in the TSD. Because the cost savings 

and the cost must be equivalent for not-common parts, the base cost of a four-cylinder, 

16-valve DOHC engine would be $200 + 160 +200, or $560, and the cost of an in-line 

six-cylinder DOHC 24-valve engine would be $300 +240 +200 or $740. As a result, the 

cost difference from cylinder count reduction in this example is only $180. There is 

considerable evidence (e.g., Argonne National Laboratory [ANL] submissions [Vyas 

1998] to DOE) that the base cost of a “dressed” four-cylinder, 16-valve engine (excluding 

emissions after treatment) is about $1,200 to $1,300, but common parts between the four 

and six-cylinder engine, like the alternator, oil pump, and water pump, cannot account for 

the $700 difference. ICF’s work suggests that the engine block changes, crankshaft 

changes, additional piston, connecting rod, and intake and exhaust system components 

result in a cost of about $200 per cylinder. We agree with the $10/valve estimate, but the 

price difference between the SOHC and DOHC system is exaggerated because the TSD 

method fails to account for the rocker arms and the more complex cylinder head 

assembly for an SOHC 16-valve cylinder head. In fact, Chrysler manufactured an 

otherwise-identical SOHC and DOHC 2L engine on the same assembly line (for the 

Dodge Neon) and publicly stated that the cost differential between the two was less than 

$50. ICF used $50 per camshaft and $5 per valve drive to estimate cost. For the 

equivalent four-cylinder engine, the cost is the $800 + 160 + 100 + 80, or $1,140, and for 

an in-line six-cylinder engine, the cost would be $1200 + 240 + 100 + 120, or $1,660. 

Common components would add $150 to “dressed” engine cost, resulting in total cost 

estimates of $1,290 for a four-cylinder engine and $1,810 for an in-line six cylinder; 

these are consistent with cost numbers obtained from industry. DOHC V6 costs would be 

$100 higher than for an in-line six cylinder, which is more consistent with the difference 

in costs between in-line and V engines received from industry by ICF than the TSD 

estimate of $200. The domestic industry has also typically charged $900 to $1,000 for the 

V6 option relative to a base four cylinder, which is also consistent with the ICF costdifference estimate of $600 ×1.6 for calculating RPE. 
7 

The third area concerns diesel engine costs. In interviews with the trade press and in 

public forums, manufacturers have stated that the piece cost of the turbocharged diesel 

engine is approximately twice the piece cost of a similar displacement gasoline engine

(Stephens 2008). This leads to incremental cost estimates of $1,200 to $1,300 for a fourcylinder engine per the previous discussion, whereas the EPA estimate is much lower at 

$740, based on mature technology costs projected by FEV Inc. in a 2003 report to EPA

(FEV 2003). Under a 2005 work assignment for DOE, ICF staff met with German diesel 

engine manufacturers and fuel injection system suppliers. Based on the inputs received, 

we concluded that the FEV report had significantly underestimated costs for some 

components, notably the advanced fuel injection system currently employed in model 

year (MY) 2010 engines. Specific technologies in the four-cylinder diesel that appear to 

be underpriced are the advanced fuel injection system with piezo injectors (estimated at 

$900 instead of $630 for a 1800 bar system), the variable geometry turbocharger 

(estimated at $260 instead of $126), and the fuel pump, which is not included in the cost 

of the fuel injection system ($45). These three technologies appear to account for most of 

the price differential. The TSD analysis currently has a far more favorable financial 

picture for the diesel than actually exists.

The fourth area involving the RPE from costs is drawn from an EPA-sponsored study of 

this factor conducted in 2009 by RTI International (EPA 2009) using the balance sheets 

and income statements of auto manufacturers. This study apportioned total costs reported 

in the income statements to overhead or factor (labor, materials, and energy inputs) costs 

without much specificity or delineation of accounting terms. The results suggested the 

59.4% of total costs were overhead costs and 40.6% were factor costs, so that the 

multiplier ratio was about 1.46 (59.4/40.6) and the spread among manufacturers was 

quite small, with all nine manufacturers examined in the study between 1.42 and 1.49. 

Even if 7% or 8% of the costs were, however, incorrectly classified as factor costs, then 

the multiplier ratio would be 1.95 to 2.03. 

The cost-to-RPE multiplier of about 2 is a ratio widely accepted by industry analysts, but 

this multiplier is an average for all parts, ranging from those that are basic raw materials 

like steel sheet or billets to those that are complete subassemblies like a transmission. 

ICF’s analyses for the NAS found several costs that were potentially incorrectly 

accounted for in the EPA-sponsored study, including factory overhead, new vehicle 

engineering and development costs not in the R&D budget, and dealer “floor-plan” and 

facility amortization cost. More important, the EPA study does not examine the 

difference between the mark-up for a Tier I supplier-based purchase of a manufactured 

component and the markup starting from basic raw materials like steel. The report does 

examine the issue of RPE multipliers associated with incremental additions of new 

technology and develops these multipliers for three levels of technology complexity, low, 

medium, and high. The incremental multipliers are estimated at 1.02, 1.05, and 1.26 in 

the long term. 

EPA made some adjustments to the reported multipliers to develop new multipliers of 

1.07, 1.13, and 1.39, but the specific methodological changes to derive the new values are 

not documented in the TSD. ICF (unpublished) used a similar classification of 

technology complexity to derive multipliers of 1.5, 1.62, and 1.75 for subassemblies 
8 

purchased from Tier I suppliers to the industry. This results in the EPA estimates for RPE 

being 25% to 30 % lower than those developed by ICF, but some of the difference is 

recouped by EPA having a more expansive accounting of costs for some technologies. 
9 

3.0 Technology-Specific Fuel Economy Improvement 

The fuel economy benefit comparisons in Tables 1-1 through 1-3 also show only small 

differences in benefit estimates for most technologies, but estimates in Table 1-4 for 

hybrid and diesel benefits may not be directly comparable because the ICF estimates for 

these technologies include benefits from “system optimization” such as engine 

downsizing, and the TSD estimates may not include some or all of these system benefits. 

DOE has, however, concerns in two areas: the lack of technology specificity associated 

with some benefits, and the inconsistencies in the estimates for various forms of valve 

control.

3.1 Technology Specificity

The lack of technology specificity is associated with lubricants, tire improvements, and 

aerodynamic drag reduction. The benefit values are not associated with specific oil 

properties, rolling resistance values, or drag coefficients. For example, it is not clear that 

the benefit of oils is associated with changing from 5W-30 to 5W-20 or 0W-20. 

Similarly, the specific rolling resistance values of tires in each vehicle class assumed or 

the values of the drag coefficient assumed are not specified for the future. The TSD 

reliance on general manufacturer comments instead of peer-reviewed technical papers

(see references for examples) on these topics also seems insufficient. These issues may be 

important in examining manufacturer-specific improvement potential because some 

manufacturers have already adopted 5W-20 oils (Korcek and Nakada 1995)—some have 

very low drag vehicles and others have sacrificed low drag for styling statements. The 

NAS analysis of tires (NAS 2006) shows a significant rolling resistance increase using 

tires with high speed ratings of 120 to 140 mph simply for image, even though this is far 

above legal limits for driving anywhere in the United States. Hence, a negative cost fuel 

economy benefit for some vehicles may be obtained by simply using a lower speed rated 

tire in some vehicles. Although drag coefficients and tire rolling resistance data are not 

easily available, EPA has the coast-down data from which these values can be determined 

at the vehicle nameplate level. A more specific analysis of current usage and expected 

2016 usage of these technologies would be very useful. 

3.2 Pumping Loss Reduction through Valve Control

One area of significant difference is in benefit estimates for valve control technologies 

that reduce pumping loss. The TSD states that the fuel consumption benefits of cylinder 

deactivation scale with the ratio of engine displacement to vehicle weight, cites a benefit 

range from 0.5% to 6%, and then states that the Honda Odyssey shows a measured 

benefit of 6%. Yet, the Honda Odyssey has a very low displacement to weight ratio (3.5L 

engine in a 4,500/ 4,750-lb inertia weight vehicle), but it is at the top end of the stated 

benefit range. The analysis in the EPA model, however, appears to use the 6% rate, so 

that there is little disagreement with our estimate in terms of the analytical results. 

ICF believes the benefits of continuously variable valve lift (CVVL) should be higher 

than those of cylinder deactivation because the CVVL system covers a wider range of 

revolutions per minute (rpm) and load when pumping loss can be reduced relative to 
10 

cylinder deactivation, but the estimated benefit in the TSD is lower for CVVL at only 3% 

to 4%. The benefits are also substantially lower than the benefits claimed by BMW and 

Nissan (who have such systems on the market) based on measured data. Existing CVVL 

systems are usually designed to achieve a combination of both lift and valve opening 

duration change via an intermediate lever mechanism working in conjunction with cam 

phasers. This approach, also known as “intake throttling,” allows elimination of the 

conventional throttle unit because the engine air flow can be controlled through real-time 

adjustment of the inlet valve lift and opening times. 

• BMW’s Valvetronic adjusts the valve lift using a lever positioned between the 

camshaft and the intake valves. The distance from the camshaft is adjusted by an 

additional eccentric shaft operated by an electric motor. BMW initially marketed 

the Valvetronic on its higher-end vehicles, but now the technology is available on 

most of its engines. BMW has claimed that the system achieves a fuel economy 

improvement of about 12%, but this includes the benefit of dual-cam phasers and 

reduced friction. Subtracting the variable valve timing (VVT) and friction 

benefits, the system is expected to yield about 8% fuel economy improvement for 

CVVL alone. This comparison is for federal test procedure (FTP) cycle 

conditions and relative to a PFI engine with fixed valve timing and compression 

ratio.

• Nissan/Hitachi has developed a CVVL system, which they call VVEL (Variable 

Valve Event and Lift), launched on the MY 2008 Infinity G37 Coupe (Arinaga et 

al. 2006; Nissan 2007). The system is also designed to throttle air at the intake 

valves and works in conjunction with VVT control. Figure 2-1 illustrates the 

CVVL mechanical implementation. The profile of the output cams incorporates 

ramp section for obtaining smooth lift. The center position of the drive shaft is the 

same as that of ordinary camshafts. Driven by a chain, the drive shaft undergoes 

rotational motion that is synchronized to one-half the rotation of the crankshaft. 

An eccentric drive cam attached to the drive shaft converts the rotational motion 

of the drive shaft to oscillatory motion, which is transferred to the oscillating 

output cams by the Link A, rocker arm, and Link B. The direct-current (DC) 

motor mechanism adjusts the link and rocker arm kinematics to obtain continuous 

adjustment of the valve lift.

• Engine developers reported that, at low- to mid-load ranges, the intake throttling 

optimizes the air charge volume. Less valve travel reduces camshaft friction and 

improves acceleration response by allowing more dense air into the cylinders 

from the start of acceleration. The timing control opens the valves for a shorter 

period, preventing blowback of the air-fuel mixture and improving torque. In the 

high load range, the valve lift is increased, which allows increased air intake to 

deliver greater torque output. 
11 

Figure 3-1. Hitachi/Nissan CVVL mechanical implementation 

• The VVEL improves fuel efficiency most effectively in the low-to-medium 

operating range; thus it is best matched to higher displacement engines that 

typically operate within that range. Nissan has indicated that in high-displacement 

engines the company will combine VVEL technology with the direct-injection 

system. The system is capable of “zero lift”; therefore cylinder deactivation is 

possible in the future. Nissan and Hitachi claim that VVEL adoption, together 

with VVT, produces up to 20% more power and up to 12% fuel economy 

improvement (cycle conditions were not reported). This comparison is against an 

identical engine without CVVL, implying a fixed valve timing engine. 

• Also, both Toyota and Honda have announced CVVL systems that they claim are 

very close to commercialization. Toyota’s version is called Valvematic and is also 

designed to work with VVT (Toyota 2007). Toyota announcements indicate that 

the system, installed on a new 2.0L engine (which does include undisclosed 

additional modifications), improves fuel consumption by 5% to 10% (implying up 

to 11% fuel economy improvement, depending on driving conditions), boosts 

output by at least 10%, and enhances acceleration responsiveness. The 

comparison is likely against the baseline of current Toyota engines, all of which 

feature VVT, although cycle conditions were not specified. 

• Honda’s next-generation VTEC (Variable Valve Timing and Lift Electronic 

Control) engine will be called AVTEC (or Advanced VTEC). The new system 
12 

will combine the continuously variable valve lift system (intake only) with 

continuously variable timing control (Senate Committee on Energy and Natural 

Resources 2007). At medium loads, the AVTEC controller creates intake and 

exhaust valve overlap by advancing the intake valve timing to achieve controlled 

internal exhaust gas recirculation (EGR). The result is improved fuel consumption 

and reduced emissions. At high loads, the valve lift is set high and the opening 

period is widened for improved charging efficiency and performance. 

• The ability to continuously adjust the transition between various load modes is 

crucial in successful system applications. Honda has developed a sophisticated 

controller for this design, which allows smooth transition to different engine 

speeds, optimizing fuel consumption, emissions, and performance. Among many 

resulting efficiency improvements, the new controller allows idle speed reduction 

from 850 to 600 rpm and still achieves smooth idling. Honda claims that this 

strategy alone resulted in fuel economy improvement of about 1.7% in the FTP 

cycle (Tagami et al. 2007). Overall, the new AVTEC system is claimed to 

improve fuel economy by 13% over current VTEC engines with significant torque 

increase. Honda’s announcements were made in the United States, so the 

comparison is potentially for FTP cycle conditions. Table 3-1 summarizes the fuel 

economy improvements for intake CVVL from the various designs described. 

Table 3-1. Fuel Economy Benefits for Various Intake-CVVL Designs

System Fuel Economy 

Benefit (%)

VVT Included in 

FE Benefit?

Notes

BMW Valvetronic Up to 12 Yes New European Driving 

Cycle (NEDC)

Nissan CVVL Up to 12 Yes Unspecified cycle

Toyota Valvematic 5 to 11 No Unspecified cycles

Honda AVTEC Up to 13 No Fuel economy benefit 

compared to VTEC 

engine

Note: Comparison is against PFI engine with fixed valve timing/lift and fixed CR, with exception of Honda 

AVTEC, as specified. 

Our estimates show that CVVL, when VVT contribution is subtracted, yields about 8% 

fuel economy benefit. In fact, the estimate appears to be on conservative side because

Honda’s AVTEC solution appears to yield higher benefit, if other advantages of fully 

flexible valve lift and timing control such as idle speed reduction and improved torque 

are fully exploited. 

Finally, the TSD states that product plans show this technology will not be applied by 

most manufacturers. In contrast, published reports indicate that Honda and Toyota are 

both readying such systems for market introduction, and others may follow. 
13 

3.3. Gasoline Direct Injection and Turbocharging

The TSD reviews estimates for incorporating GDI that range from 1% to 5.8%, but 

somewhat arbitrarily picks a range of 2% to 3% as “appropriate.” The main mechanism 

that allows GDI to improve fuel economy, however, is the increase in compression ratio

(CR) caused by the cooling effect of fuel spray evaporation in the cylinder. Hence, 

variability in GDI benefit estimates can be traced to the CR increase assumptions in the 

studies cited. In addition, other studies have also shown that some GDI benefits arise 

from combustion improvements facilitated by GDI. Stoichiometric naturally aspirated 

GDI engines are being used by few OEMs in the United States. Toyota has introduced 

this technology in the dual injection 3.5L V6 and 4.6L V8 engines used in the Lexus 

offerings. Volkswagen and Audi are configuring more of their vehicles to adopt the 

technology labeled FSI (fuel straight injection), and General Motors (GM) has introduced 

GDI on the 3.0L and 3.6L V6, with additional introductions planned for MY 2010. 

• Toyota has adopted a very sophisticated injection strategy that involves both PFI 

and direct-injection (DI) injector fueling, depending on vehicle operation. The 

result is a system that achieves very high thermal efficiency in the core mid-tohigh load range, which translates into high power (Ueda 2007). The 4.6L V8 was 

installed on the flagship Lexus LS460. Together with DI, the new engine features 

new VVT, friction reduction, and other improvements. The engine displacement 

was increased by about 7% compared to the previous generation LS430, but the 

new technology was more than enough to compensate for a fuel economy penalty 

resulting from additional displacement and power increase. Figure 3-2 details the 

performance and relative fuel economy improvement contribution of various 

Toyota engine technologies (Asahi et al. 2007). The total fuel economy 

improvement from the engine is 10% (23 to 25.3 mpg) of which about one-third 

(or ~3%) is from DI-related changes—CR increase and combustion 

improvements. The CR increase was only 1.2 (from 10.6 to 11.8) because of the 

high CR of the base LS430. 
14 

Figure 3-2. LS 460 and LS430 fuel economy comparison—contribution of various engine 

technologies 

The majority of mass market vehicles in the United States in 2008/2009 with PFI engines 

had CR levels of 9.5 to 10.0, and luxury models certified for premium fuel had CR levels 

of 10.5 to 11. It appears the GDI can increase CR levels by 2 points to the 11.5–12 range 

in mass market cars and to 12.5 for luxury cars by 2016. Even in 2009, all Audi GDI V6 

engines had a CR of 12.5. GM’s first DI engine, the 3.6L V6, had a CR of 11.3, but the 

newer 3L introduced for MY 2010 has a CR of 11.7. ICF agrees that if the CR 

improvement is only 1 to 1.2 points, the fuel economy benefit range in the TSD is 

reasonable, but it appears that CR will increase by 2 points by 2016 and a benefit range of 

3.5% to 4.5% is more appropriate.

Manufacturers agree that the downsized turbocharged GDI engine represents a substantial 

opportunity to improve fuel economy, and the ICF estimate from measured results on 

production vehicles comparing the fuel economy of a naturally aspirated engine with that 

of a turbo GDI engine that is downsized by one third is 13% + 2%. This is consistent with 

the statement in the TSD about “a 12 to 14 effectiveness improvement,” but confusingly, 

the TSD also states that the turbocharged GDI engine “would offer an effectiveness 

improvement of 2 to 5 percent over a naturally aspirated SGDI engine.” Because the 

SGDI engine’s benefit is estimated at only 2% to 3%, it is not clear how the two 

statements are equivalent. In the NHTSA method, the fuel economy is derived from 

stacking individual technology benefit estimates for GDI and turbocharging separately, 

and the numbers cited in the TSD would produce inconsistent results if the EPA model 

assumes a benefit of 12% to 14%. 
15 

4.0 Technology Synergy Issues

The issues regarding the net fuel economy benefit of adding multiple technologies to a 

vehicle have been a source of debate in the fuel economy analysis community, and 

NHTSA and EPA take different approaches to dealing with these synergy issues, largely 

because of model-specific differences. The synergy issues are described in more detail in 

RIAs conducted by each agency. 

The Volpe model used by NHTSA organizes technologies into predetermined sequences 

of adoption, termed decision trees. Because the model applies technologies individually, 

NHTSA tracks synergistic effects between pairings of individual technologies in a 

manner similar to that used in the DOE’s National Energy Modeling System (NEMS). 

Inputs to the Volpe model incorporate technology pairs identified in NEMS as well as 

additional pairs from the set of technologies considered uniquely in the Volpe model. For 

this rulemaking, NHTSA updated the list of technologies and expanded the list of 

synergy pairings. 

NHTSA does not account for synergies within a particular decision tree because the 

synergies are already accounted for within the incremental effectiveness values assigned 

for each technology in the input files used by the Volpe model. Hence, the technology 

benefits represent the net benefit of the technology given all the previous technologies 

adopted in the decision tree method. For applying incremental synergy factors between 

two or more decision trees, the Volpe model uses input tables with synergy factors 

identified by vehicle class shown in Tables V-12 a-d of NHTSA 2009. 

The tables present a lengthy list of technology pairs, and list the technology synergy (or 

more accurately, “dis-synergy” because most are negative) for each pair and for several 

vehicle size classes, although we were unable to discern any change in the listing across 

size classes. These numbers were derived from the EPA lumped-parameter model, 

according to the description in the NHTSA RIA. The data in the synergy tables were not 

reviewed directly because the method and results of the EPA model were reviewed.

EPA was not satisfied with the NEMS model method because it depends on an assumed 

technology adoption path and, furthermore, the NEMS does not account for some newer 

technologies. As a result, EPA developed its own estimates using the lumped-parameter 

approach. EPA describes the lumped-parameter analysis as a first-principles-based 

energy balance, to account for all the different categories of energy loss, including: 

• Second law of thermodynamics-based losses

• Heat loss from the combusted gases to the exhaust and coolant 

• Pumping loss 

• Mechanical friction loss

• Transmission losses

• Accessory loads
16 

• Vehicle road-load tire and aerodynamic drag losses 

• Vehicle inertial energy lost to the brakes. 

EPA does not, however, document how the various losses were determined for the 

baseline vehicle, other than stating that “it is assumed that the baseline vehicle has a fixed 

percentage of fuel lost to each category.” Each technology improvement is characterized 

by the percent change to each of the loss categories. Hence, if multiple technologies are 

employed to reduce the same category of losses, each successive technology has a 

smaller impact as the category of loss is reduced to zero. EPA does not document how 

the technology characterization was done. It appears, however, that these determinations 

of losses both for the baseline vehicle and for technology improvements were not based 

on computed values but on expert opinion, as far as we can determine. 

EPA compared the lumped-parameter results with new full-scale simulation modeling 

results on several vehicle classes with different combinations of planned technological 

improvements. Ricardo Inc. performed the simulations and documented them in a 

separate report (Ricardo 2008). In a majority of the comparisons, the lumped-parameter 

model estimates were close to the Ricardo estimate, and EPA concluded the results of 

their model were plausible, with a few technology packages requiring “additional 

investigation.” EPA indicates in the RIA that the agency will continue to use the lumpedparameter approach as an analytical tool, and may adjust the lumped-parameter model as 

necessary in the future to improve its fidelity as more simulation results become 

available.

ICF- EEA also developed a lumped-parameter model that is broadly similar in scope and 

content to the EPA model. The primary difference is that all of the baseline vehicle’s 

energy losses are determined computationally, and many of the technology effects on 

each source of loss were determined from data presented at technical conferences. 

Because the model has been used primarily for internal purposes, a detailed 

documentation of all facets of the model is not available, but a short summary is as 

follows. 

First, the energy required at the wheel to move a vehicle over the driving cycle has been 

determined as a function of its weight, frontal area, drag coefficient, and tire rolling 

resistance coefficient by numerical integration over the Federal Test Procedure city and 

highway driving cycles in a paper by GM researchers (Sovran and Bohn 1981). This is 

used to compute the energy requirement at the wheel for the given baseline vehicle and 

translated to energy at the engine output shaft by using transmission and driveline 

efficiency factors (which differ by transmission type and number of gears) derived from 

the open literature. Accessory energy requirements are added as a fixed energy amount 

that is a function of engine size. This determines total engine output energy; average 

cycle power is then computed by distributing the energy over the cycle time when 

positive engine output is required; i.e., the time spent at closed throttle braking and idle 

are accounted for separately. Average cycle rpm excluding idle was obtained for specific 

vehicles from simulation models on specific vehicles, and these data are scaled by the 

ratio of the N/V for the data vehicle and the baseline vehicle. These data are used to 
17 

determine average brake mean effective pressure (BMEP) for the positive power portion 

of the cycle.

Fuel consumption is determined from the relationship: 

IMEP = BMEP + FMEP + PMEP, 

where I is for indicated, F is for friction, and P is for pumping. The fuel consumption 

model is derived from a method to estimate an engine map using a semiempirical model 

developed by researchers at Ford and the University of Nottingham, as described in 

Shayler et al. (1999). In this formulation, fuel consumption is proportional to IMEP 

divided by indicated thermal efficiency (sometimes called the Willans line), friction is 

determined empirically from engine layout and is a function of rpm only, and PMEP is 

simply intake manifold pressure–atmospheric pressure. Intake manifold pressure is 

solved for any given BMEP because IMEP is also proportional to intake pressure. This 

model explicitly derives thermal efficiency, friction loss, and pumping loss for the 

baseline vehicle. Fuel consumption at idle and closed throttle braking are modeled as 

functions of engine displacement only. The baseline engine is always modeled with fixed 

valve lift and timing, and the pumping loss is adjusted for the presence of VVT if 

applicable. The model can be construed as a 2-point approximation of a complete engine 

map and is a very reasonable representation of fuel consumption at light and moderate 

loads where there is no fuel enrichment.

The technologies are characterized by their effect on each of the losses explicitly 

accounted for in the model, and the representation is similar in concept to the 

representation in the EPA model. The model outputs were also compared to the Ricardo 

simulation results that were used a reference standard by EPA. The Ricardo work 

modeled five baseline vehicles (standard car, large car, small multi-purpose vehicle 

(MPV), large MPV, and large truck) and 26 technology combinations, covering gasoline 

and diesel power trains used in the EPA model, but there was no simulation of hybrids. 

ICF–EEA had also performed an initial analysis for the NAS Committee on Fuel 

Economy. Based on our experience, when a number of engine, transmission, and other 

technology improvements are simultaneously added to a baseline vehicle, the net fuel 

economy benefit can be approximated by taking 90% of the additive sum of the 

individual technology benefits. We used this technique to develop a quick approximation 

of the level of agreement likely between the Ricardo simulations and our lumpedparameter model. We performed a quick analysis of 23 of 26 packages developed by 

Ricardo; we had no data on homogeneous charge compression ignition (HCCI) engines, 

which were used in 3 of the technology packages modeled by Ricardo. 

Ricardo included one technology for which we had no specific data, which they defined 

as “fast warm-up” technology. This is the control of coolant flow to the engine 

immediately after cold start. Based on the data presented by Ricardo, we estimated this 

technology’s benefit at 1%, including the benefit of the electric water pump. All other 

technology benefits were based on the data from ICF-EEA’s previous reports to the DOE 

on fuel economy technology. These benefit estimates were adjusted for the presence or 
18 

absence of technologies on the baseline vehicle, because all benefits in the DOE reports 

were typically defined relative to an engine with fixed valve timing and a four-speed 

automatic transmission. Figure 4-1 illustrates the results, and the plot shows the 

difference between the Ricardo results and the quick approximation method. 

In 16 of the 23 cases, the Ricardo estimate is within 5% of the quick estimate. In two 

cases, the Ricardo estimates were more than 10% lower relative to the quick estimates. In 

five cases, the Ricardo estimates were 10% (or more) higher than the quick estimate. 

Note that the difference implies that the benefits are larger than the simple sum of 

individual technology benefits and that technology synergies are positive. We also 

examined the technology packages in the two “low” and five “high” outliers. Both low 

outliers had technology packages with a continuously variable transmission (CVT) as one 

of the technologies. The five high outliers did not feature any major technology 

improvement in common. 

Figure 4-1. Comparison of the difference between Ricardo and EEA fuel economy 

simulation results

More detailed analysis was also done with the ICF-EEA lumped-parameter model. 

Because of resource and time constraints, we analyzed only 9 of the 23 cases with the 

lumped-parameter model, but the 9 cases included both high and low outliers from the 

previous analysis. Three technology packages were analyzed for the “standard car,” 

which used a Toyota Camry baseline; three for the compact van, which used the Chrysler 

Voyager as a baseline; and three for the standard pickup, which used the Ford F-150 as 

the baseline. Table 4-1 shows the results as well as the “quick” method results, for 

FE BENEFIT ESTIMATES (%)

-20

-15

-10

-5

0

5

10

15

20

10 20 30 40 50

EEA QUICK ESTIMATE %

RICARDO-EEA %
19 

comparison. The more detailed modeling reduced the average difference between the 

Ricardo estimates and our estimates for the Toyota Camry and the Chrysler compact van, 

but increased the difference for the Ford F150 truck. The largest observed difference is 

for package 10 on the Ford, where the baseline 5.4L V8 is replaced by a 3.6L V6 turbo 

GDI engine, and the downsizing is consistent with the 33% reduction we have used.

Table 4-1. Results from the lumped-parameter model

Vehicle Tech. 

Package

Ricardo 

Estimate

“Quick” 

Result

EEA Model 

Result

Toyota Camry Z 33.0 23.7 32.6

1 13.0 23.7 23.1

2 22.0 22.4 21.9

RMS 

Difference

8.15 5.85

Chrysler Voyager 4 26.0 30.9 29.9

6b 35.5 33.3 35.5

16 41.0 28.5 36.6

RMS 

Difference

7.85 3.39

Ford F-150 9 32.0 30.0 28.3

10 42.0 28.2 26.4

16 23.0 21.3 23.4

RMS 

Difference

8.12 9.25

In Table 4-1, the RMS difference refers to the root mean square difference between the 

ICF-EEA estimate and the Ricardo estimate. The differences seem to be in the same 

range as the differences between the EPA estimates with their lumped-parameter model 

and the Ricardo estimates. It is also important to note that the EPA model results are 

more consistent with the results of the ICF model. The “low” Ricardo result for package 

1 on the Camry is also significantly lower than the EPA estimate of 20.5% fuel economy 

benefit, which is closer to the ICF 23% estimate than to Ricardo’s 13% estimate. 

Similarly, the high Ricardo estimate for package 10 on the Ford F-150 is also 

substantially higher than the EPA estimate of 30.5% fuel economy gain, which is, in turn,

higher than our estimate of 26.4% but much less than the Ricardo estimate of 42%. 
20 

In conclusion, the EPA synergy model appears to produce results reasonably consistent 

with the ICF model, but suffers from not having computationally specific inputs on the 

baseline vehicle and a more rigorous approach to determining pumping and friction 

components of loss. We disagree with the EPA approach of using the Ricardo simulation 

as the reference standard, because we believe that the detailed simulation model is also 

subject to user input biases. In this case, for example, we believe that Ricardo used a map 

of an inefficient CVT to derive results much lower than the benefits being demonstrated 

by vehicles in certification testing.
21 

5.0 References

Arinaga, T.; Kobayashi, M.; Ushijima, K.; Takemura, S.; Aoyama, S.; Nakamura, M. 

Hara, S. (2006). A Study of Friction Characteristics of Continuously Variable Valve 

Event and Lift (VEL) System. SAE Technical Paper 2006-01-0222. Warrendale, PA: 

SAE. 

Asahi, T.; Yamada, T.; Hashizume, H. (April 2007). “The New 4.6L V8 SI Engine for 

the Lexus LS460.” Presented at the Vienna Motor Symposium, Vienna, Austria. 

BLS. (2010). Producer Price Index for Automobiles. Available at www.bls.gov/ppi/. 

Washington, DC: BLS 

EPA. (2009). Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers.

EPA Report 420-R-09-003. Washington, DC: EPA.

EPA and NHTSA. (September 2009). Draft Joint Technical Support Document, 

Proposed Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel Economy Standards. Washington, DC: EPA and 

NHTSA.

FEV Inc. (2003). FEV Engine Technology, Cost and Fuel Economy Comparison of 

Diesel and Gasoline Powertrains in Passenger Cars and Light Duty Trucks. FEV report 

to EPA under Contract No. 68-C-01-155. Aachen, Germany: FEV. 

ICF. Unpublished analysis in support of the National Academy of Sciences Committee 

on Fuel Economy. 

ICF-EEA (2007) . Advanced Technologies to Improve Fuel Economy of Light Duty 

Vehicles. Report to the US DOE. Fairfax, VA: EAA

Korcek, S.; Nakada, M. (1995). “Engine Oil Performance Requirements and 

Reformulation for Future Engines and Systems.” Presented at the International Tribology 

Conference, Yokohama, Japan, October. 

NAS. (2001). Effectiveness and Impact of Corporate Average Fuel Economy (CAFÉ) 

Standards. Report of the Committee on the Effectiveness and Impacts of CAFE 

Standards. Washington, DC: NAS Press.

NAS. (2006). Tires and Passenger Vehicle Fuel Economy. National Academy of 

Sciences Special Report 286. Washington, DC: NAS Press.

NHTSA. (2009). Preliminary Regulatory Impact Analysis: Corporate Average Fuel 

Economy for MY 2012-MY2016 Passenger Cars and Light Trucks. Washington DC: 

NHTSA
22 

Nissan. (March 29, 2007). “Nissan Introduces Innovative Engine Valve Control 

Technology—Variable Valve Event & Lift (VVEL) Delivers Significant Improvements 

in Dynamic and Environmental Performance.” News release. 

Ricardo, Inc. (2008). A Study of the Potential Effectiveness of Carbon Dioxide Reducing 

Vehicle Technologies. Report to the U.S. EPA. June 26. Van Buren, MI: Ricardo Inc.

Shayler, P.; Chick, J.; Eade, D. (March 1999). A Method of Predicting Brake Specific 

Fuel Consumption Maps. SAE Paper 1999-01-0556. Warrendale, PA: SAE.

Sovran, G.; Bohn, M. (February 1981). Formulae for the Tractive Energy Requirements 

of the Vehicles Driving the EPA Schedules. SAE Paper 810184. Warrendale, PA: SAE.

Senate Committee on Energy and Natural Resources. (January 30, 2007). Statement of 

John German, Manager, Environmental and Energy Analysis, Product Regulatory Office, 

American Honda Motor Company, Inc. Washington, DC: U.S. Senate. 

Stephens, T. (2008). “GM’s Advanced Propulsion Technology Strategy.” Presented at the 

29th International Vienna Motor Symposium, Vienna, Austria, April. 

Tagami, H.; Yasui, Y.; Sato, M.; Ito, H. (2007). Management System for Continuously 

Variable Valve Lift Gasoline Engine. SAE Technical Paper 2007-01-1200. Warrendale, 

PA: SAE.

Toyota. (June 12, 2007). “Toyota Develops Next-Generation Engine Valve Mechanism.” 

News release. 

Ueda, T. (2007). “Innovative Development Methodology Based on the Toyota Way.” 

Presented at the Vienna Motor Symposium, Vienna, Austria, April. 

Vyas, A. (December 1998). HEV Component Cost Information. ANL report to DOE. 

Argonne, IL: ANL. 
F1146-E(10/2008) 

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Comparison of Vehicle Efficiency Technology Attributes and 

Synergy Estimates

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14. ABSTRACT (Maximum 200 Words)

Analyzing the future fuel economy of light-duty vehicles (LDVs) requires detailed knowledge of the vehicle technologies available 

to improve LDV fuel economy. The National Highway Transportation Safety Administration (NHTSA) has been relying on 

technology data from a 2001 National Academy of Sciences (NAS) study (NAS 2001) on corporate average fuel economy (CAFE) 

standards, but the technology parameters were updated in the new proposed rulemaking (EPA and NHTSA 2009) to set CAFE 

and greenhouse gas standards for the 2011 to 2016 period. The update is based largely on an Environmental Protection Agency 

(EPA) analysis of technology attributes augmented by NHTSA data and contractor staff assessments. These technology cost and 

performance data were documented in the Draft Joint Technical Support Document (TSD) issued by EPA and NHTSA in 

September 2009 (EPA/NHTSA 2009). For these tasks, the Energy and Environmental Analysis (EEA) division of ICF International 

(ICF) examined each technology and technology package in the Draft TSD and assessed their costs and performance potential 

based on U.S. Department of Energy (DOE) program assessments. ICF also assessed the technologies’ other relevant attributes 

based on data from actual production vehicles and from recently published technical articles in engineering journals. ICF 

examined technology synergy issues through an ICF in-house model that uses a discrete parameter approach.

15. SUBJECT TERMS

NREL; light-duty vehicles; LDV; fuel economy; vehicle efficiency; vehicle synergies; EPA; ICF International; 

technology synergy; technology cost; technology performance; Department of Energy; DOE; energy model

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