A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory

Innovation for Our Energy Future 

Milestone Report

NREL/MP-540-40609

November 2006

Plug-In Hybrid Vehicle Analysis 

T. Markel, A. Brooker, J. Gonder, M. O’Keefe, 

A. Simpson, and M. Thornton 

National Renewable Energy Laboratory

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
Milestone Report

NREL/MP-540-40609

November 2006

Plug-In Hybrid Vehicle Analysis 

T. Markel, A. Brooker, J. Gonder, M. O’Keefe, 

A. Simpson, and M. Thornton 

National Renewable Energy Laboratory

Prepared under: 

Task No. FC06.2000 

B&R Code VT0103000 

Prepared for the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

In fulfillment of: 

FreedomCAR and Vehicle Technologies Program

September 2006 Milestone/Deliverable 

6.3 Grid-Connected Hybrid Vehicle Efficiency Improvement Analysis

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov

Operated for the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Preface 

The Vehicle Systems Team in the Center for Transportation Technologies and Systems at the 

National Renewable Energy Laboratory (NREL) performed this work. This report documents 

completion of the September 2006 milestone as part of NREL’s 2006 FreedomCAR and Vehicle 

Technologies (FCVT) Annual Operating Plan with the U.S. Department of Energy (DOE). The 

objective of the work was to provide objective systems simulations and analysis of plug-in 

hybrid electric vehicle technologies. 

This report supports the goals of the DOE/FCVT Program to quantify the potential benefits of 

plug-in hybrid electric vehicle technology through analysis and modeling. Specifically, this 

effort supports Task 1: Modeling and Simulation, discussed in the FCVT Multi-Year Program 

Plan. 

This work was funded by the Advanced Vehicle Technology Analysis and Evaluation activity in 

support of the FCVT Program of the Office of Energy Efficiency and Renewable Energy within 

the U.S. DOE. We wish to thank our sponsor, Lee Slezak, for his guidance and support. Terry

Penney, as NREL’s FCVT technology manager, and Matt Thornton, as task leader for NREL’s 

Vehicle Systems Analysis Task, supported this project. 

We would also like to express our appreciation to the members of the FreedomCAR Vehicle 

Systems Analysis Technical Team: Larry Laws (GM), Mark Biernacki (DaimlerChrysler), and 

Asi Perach (Ford) for providing technical insight and industry review. 

Tony Markel, project leader 

 iii
Table of Contents 

Executive Summary 

Section 1: Plug-In Hybrid Electric Vehicle Fuel Economy Reporting Methods

Section 1.1: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Vehicles,” 

presented to the Vehicle Systems Analysis Technical Team, Feb. 1, 2006 

Section 1.2: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric 

Vehicles,” published at the 22nd International Battery, Hybrid, and Fuel 

Cell Electric Vehicle Symposium and Exposition, October 2006 

Section 2: Plug-In Hybrid Electric Vehicle Cost and Consumption Benefit Analysis 

Section 2.1: “Cost/Benefit Analysis of Hybrid-Electric and Plug-In Hybrid-Electric 

Vehicle Technology,” presented to the Vehicle Systems Analysis Technical

Team, March 1, 2006 (updated May 2006) 

Section 2.2: “Cost-Benefit Analysis of Plug-In Electric Vehicle Technology,” 

published at the 22nd International Battery, Hybrid, and Fuel Cell Electric 

Vehicle Symposium and Exposition, Oct. 26, 2006 

Section 2.3: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, 

and Key Challenges,” presented at the Federal Network for Sustainability

Meeting, July 20, 2006 

Section 2.4: “Plug-In Hybrid Electric Vehicle Energy Storage Analysis,” presented to 

the FreedomCAR Electrochemical Energy Storage Technical Team Plug-In

Hybrid Electric Vehicle Battery Working Group Meeting, July 25, 2006 

Section 2.5: “Plug-In Hybrid Modeling and Application: Cost/Benefit Analysis,” 

presented at the Third AVL Summer Conference on Automotive 

Simulation Technology, Aug. 24, 2006 

Section 3: Plug-In Hybrid Electric Vehicle Real-World Performance Expectations

Section 3.1: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, 

and Key Challenges,” presented at the Clean Cities Congress and Expo, 

May 8, 2006 

Section 3.2: “Plug-In Hybrid Vehicle Real-World Performance Expectations,” 

presented to the Vehicle Systems Analysis Technical Team, June 14, 2006 

Section 3.3: “Using GPS Travel Data to Assess the Real-World Driving Energy Use of

Plug-In Hybrid Electric Vehicles,” to be published at the Transportation 

Research Board 86th Annual Meeting, February 2007 

 iv
Section 4: Plug-In Hybrid Electric Vehicle Energy Management Strategies 

Section 4.1: “Plug-In Hybrid Electric Vehicle Energy Storage System Design,” 

presented at the Advanced Automotive Battery Conference, May 19, 2006 

Section 4.2: “Plug-In Hybrid Electric Vehicle Energy Storage System Design,” 

published at the Advanced Automotive Battery Conference, May 19, 2006 

Section 4.3: “Dynamic Programming Applied to Investigate Energy Management 

Strategies for a Plug-In Hybrid Electric Vehicle,” to be published at the 

22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle 

Symposium and Exposition, October 2006 

Summary

 v
Plug-In Hybrid Vehicle Analysis 

Milestone Report

September 2006 

Tony Markel

Aaron Brooker, Jeffrey Gonder, Michael O’Keefe, Andrew Simpson, Matthew Thornton 

In fulfillment of FreedomCAR and Vehicle Technologies Program September 2006 

Milestone/Deliverable 6.3 Grid-Connected Hybrid Vehicle Efficiency Improvement Analysis

Executive Summary 

NREL’s plug-in hybrid electric vehicle (PHEV) analysis activities have made great strides in FY06 to 

objectively assess PHEV technology, support the larger U.S. Department of Energy PHEV assessment 

effort and complementary activities at other national laboratories, and share technical knowledge with 

the vehicle research community and vehicle manufacturers through the FreedomCAR Vehicle Systems 

Technical Team and the Electrochemical Energy Storage Technical Team. The key contributions of 

this activity include: 

1. Proposed improvements to the existing test procedure for reporting PHEV fuel economy

2. A thorough exploration of the PHEV design space, including an evaluation of the trade-offs 

between cost and fuel consumption 

3. The application of real-world driving data to quantify the impacts of travel behavior on the 

potential benefits of PHEVs 

4. The optimization of energy management strategies focusing on petroleum displacement. 

The NREL research team has participated in many key industry meetings, and its research has been 

documented in eight formal presentations and five technical papers that have been published or have 

been submitted for publication within the next 6 months. This milestone report is a compilation of

these papers and presentations for future reference. 

The following is a summary of important insights that emerged from the four areas of emphasis. 

Plug-In Hybrid Electric Vehicle Fuel Economy Reporting Methods

PHEVs differ significantly from existing vehicles. They consume two fuels (petroleum and electricity) 

at rates that depend on the distance driven and the aggressiveness of the cycle. The Society of 

Automotive Engineers J1711 Recommended Practice, created in 1999, provides the fundamentals for 

measuring fuel economy of off-vehicle charge-capable vehicles (i.e., plug-in hybrids and electric 

vehicles). Seven years later, with a much better understanding of how PHEVs will likely operate, 

some improvements to the original procedure are recommended. The team’s specific 

recommendations are:

• Report gasoline and electricity consumption separately, which allows the reported results to 

be used for vehicle operating cost comparisons, fuel consumption, and CO2 emissions 

estimates. 

• Revise the end-of-test criteria to more accurately determine the distance driven in chargedepleting mode, fully capture the petroleum displacement potential of longer-range PHEVs, 

and improve the reporting accuracy for short-range PHEVs. 

 1
• Assume that vehicles will be fully charged once per day because there is an economic 

incentive for consumers to recharge their vehicles at least once per day, if not more often. 

The recommended improvements to the fuel economy reporting methods have been adopted in our 

analyses, and the team intends to work with other labs and regulatory agencies to enact similar 

improvements in their procedures. The analysis also identified the need to develop a new utility factor 

relationship, based on the best available travel survey data, and to explore the implementation of a 

driving-type specific utility factor to account for the fact that most short-distance travel will be urban 

in nature and most long-distance travel will be highway in nature. Finally, it has been determined that 

the current Environmental Protection Agency certification cycle adjustment factors provide an 

inaccurate prediction of real-world PHEV consumption and should also be revised. 

For a more detailed discussion of this effort, please refer to sections 1.1 and 1.2. 

Plug-In Hybrid Electric Vehicle Cost and Consumption Benefit Analysis 

“Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options,” a report published by the 

Electric Power Research Institute in July 2001, was a unique study that stands as a comprehensive 

analysis of hybrid electric vehicle and PHEV technology implementation. NREL contributed vehicle 

system simulation results to this report. The analysis scope was limited to just a few vehicle scenarios, 

including conventional, HEV0, PHEV20, and PHEV60. These vehicles were designed to achieve allelectric operation on the urban cycle for the specified distance. 

NREL recently developed a rapid design exploration methodology and applied the methodology to an 

expanded PHEV analysis spectrum that includes PHEVs with a wide range of power and energy

capabilities. In particular, the scope included PHEVs with limited all-electric capabilities that are still 

able to realize tangible petroleum displacement by operating in a “blended” charge-depleting mode. 

Key conclusions from the analyses are:

• The PHEVx definition should be based on the energy equivalent all-electric range of the 

energy storage system, rather than on actual all-electric range (the distance before first engine 

turn-on event). The current all-electric range focused definition constrains the PHEV design

space and is not necessarily directly related to petroleum displacement. 

• The expected petroleum reduction of a PHEV is substantial, but the incremental costs may

present a barrier to broad market penetration. A PHEV20 would likely reduce petroleum

consumption by 50% but cost $8,000 more than a conventional vehicle. The PHEV40 would cost

$11,000 more than a conventional vehicle and may reduce petroleum consumption by 62%. 

• Data in open literature support an inverse correlation between battery cycle life and cycle 

depth of discharge. To provide equivalent cycle life performance, the usable state-of-charge 

window of a short-range PHEV must be significantly less than that of a long-range PHEV. For 

example, for a 15-year life, a PHEV10 can only use 41% of the capacity, while a PHEV60 

may use up to 73% and still achieve battery life targets.

• If PHEVs are to provide a payback relative to a hybrid electric vehicle within 10 years, based 

on fuel cost savings and purchase cost alone, battery costs must reach long-term, high-volume 

cost estimates (<$300/kWh), and gasoline costs must increase to more than $4/gal. In the 

absence of both lower battery costs and higher gas prices, alternative value propositions (e.g., 

government incentives, vehicle-to-grid revenue, battery leases, the value of a “green” image, 

avoided trips to the gas station, and the feel of electric operation) must be considered to 

overcome the cost premium of PHEVs. 

 2
The analysis thus far has not allowed vehicle platform engineering as a strategy to reduce costs and 

improve fuel economy. Aerodynamics and vehicle light-weighting will likely have a more pronounced

impact on PHEVs than any other configuration. Future analysis will focus on vehicle platform

enhancements and their impact on the relative costs and benefits of PHEVs. In addition, design options 

and alternative business models will be explored to address the high cost of batteries for PHEVs. 

For more extensive discussion of this topic, please refer to sections 2.1, 2.2, 2.3, 2.4, and 2.5. 

Plug-In Hybrid Electric Vehicle Real-World Performance Expectations

The consumption of electricity and petroleum by a PHEV will be strongly influenced by the daily

distance traveled between recharge events and the aggressiveness of driving. Rather than rely on 

standard test profiles for a prediction of PHEV fuel consumption, we have collaborated with 

municipalities to use existing drive cycle databases as inputs to our simulation models. The simulation 

results provide key insights into consumer travel behavior and quantify the real-world potential for 

PHEVs to displace petroleum. The first data set was from the St. Louis, Missouri, metropolitan area 

and includes 227 unique driving profiles, with daily travel distances ranging from less than a mile to

more than 270 miles. 

Conclusions from the travel survey data are:

• Approximately 50% of the vehicles traveled less than 29 miles a day. A PHEV with 20–30 

miles of electric range capability provides sufficient energy to displace a large percentage of 

daily petroleum consumption. Because many vehicles drive less than 30 miles a day, the 

battery of a PHEV with 30 or more miles of electric range capability would likely be underutilized on a daily basis.

• The travel survey data demonstrated that there is a broad spectrum of driving behavior, 

varying from short to long distances and from mild to aggressive driving intensities. The 

Urban Dynamometer Driving Schedule and Highway Fuel Economy Test driving profiles 

used for fuel economy reporting today fall short of capturing the typical driving behavior of

today’s consumer. 

• Contrary to experience with hybrid electric vehicles, which typically deliver fuel economies 

significantly less than their rated values, simulations of real-world driving suggest that a large 

percentage of drivers of PHEVs will likely observe fuel economies in excess of the rated fuel

economy values. However, because of high power requirements in real-world cycles, drivers 

are unlikely to experience significant all-electric operation if PHEVs are designed for allelectric range on the Urban Dynamometer Driving Schedule.

• If all vehicles in the travel survey “fleet” were PHEV20 vehicles designed for all-electric 

range on the Urban Dynamometer Driving Schedule, petroleum consumption would be 

reduced by 56% relative to a conventional vehicle fleet. The PHEV40 reduced consumption 

by an additional 12% and was equivalent to ≈1 gal/vehicle/day of petroleum savings. 

Including electricity costs, the average annual fuel costs savings for the “fleet” of PHEVs is 

more than $500/vehicle/year. 

• The time-of-day usage pattern obtained from global positioning system (GPS) travel survey

data and the recharge requirements from simulation will be extremely valuable for 

determining the impact of PHEV recharging scenarios on the electric utility grid. 

 3
Since the St. Louis analyses were completed, data from five other metropolitan GPS travel surveys 

have been obtained. The driving profile database will expand from 227 to more than 2,000 vehicles. 

Additional analyses will be completed using the full collection of more than 2,000 driving profiles. 

Real-world travel simulations will be executed to consider variations in platform, aerodynamics, 

performance, control, and recharge scenarios. In addition, the database will be used to explore the 

emissions control implications of potential engine cold-starts and the fuel consumption impacts of 

location-specific air conditioning use. 

For more extensive discussion of this topic, please refer to sections 3.1, 3.2, and 3.3. 

Plug-In Hybrid Electric Vehicle Energy Management Strategies 

Discussion in many PHEV forums has focused on how the PHEV will function and, more specifically, 

on how the vehicle will use the battery and engine in combination to improve efficiency and displace 

petroleum.

NREL’s vehicle systems analysis team has a long history of applying optimization tools to explore 

hybrid electric vehicle energy management strategies. During the past fiscal year, two parallel efforts 

were initiated. The first explored the extensive PHEV design space and identify promising regions 

(using the modeling techniques developed for the cost-benefit study). The second applied dynamic 

programming techniques to determine the “near optimal” power distribution among the engine, motor, 

and battery in a PHEV for a known driving profile. NREL’s energy management strategy work is

critical for maximizing the petroleum savings while protecting the batteries of future hybrid vehicles.

The conclusions from these analyses are: 

• The misconception that a PHEVx must drive using electricity for the first x miles and then use 

the engine for the remaining travel must be clarified. This is one strategy that a manufacturer 

may choose to pursue, but it is not the only strategy. As long as the strategy is achieving a net 

discharge of the battery, petroleum will be displaced, regardless of whether the vehicle is 

operated on battery only or on a combination of battery and engine power (known as a 

“blended” control strategy).

• The selection of strategy and component sizing are not entirely independent. Reducing the 

rated power and size of the electric traction components is one way to reduce the cost of a 

PHEV. Reducing electric components also necessitates the use of a “blended” strategy. The 

“blended” strategy can still utilize electric propulsion to the maximum extent possible to 

minimize the vehicle’s instantaneous fuel use. NREL’s analysis shows that a PHEV with 

electric traction components half the size (based on power) of an all-electric PHEV can 

provide nearly the same petroleum reduction as an all-electric PHEV. 

• Dynamic programming optimization of PHEV energy management strategies indicated that

optimum control based on a priori knowledge of the driving cycle provided marginally better 

petroleum savings than a strategy that used stored electric energy to the greatest extent

possible. On the other hand, if the real-world driving distance turned out to be less than that

predicted for dynamic programming, then the “optimally” blended strategy would consume 

significantly more fuel than the electric energy-focused strategy over the length of the 

shortened driving distance. Note, however, that the simulations supporting these results were 

limited to repetitions of identical drive cycles. It is possible that drive cycle variation (e.g., an 

urban followed by a highway followed by an urban pattern) may impact this conclusion. 

 4
As PHEV technology evolves, energy management strategy will become increasingly important. It

will be used to ensure satisfactory battery life, maximize petroleum displacement, gain performance 

improvement, and manage vehicle thermal and emissions transients. NREL’s future work will apply

optimization to more varied driving scenarios and include aspects beyond fuel displacement in the 

objective function. 

For more extensive discussion of this topic, please refer to sections 4.1, 4.2, and 4.3. 

Summary

NREL’s assessment of PHEV technology has added to the body of knowledge and continues the 

Vehicle Systems Analysis team’s long history of timely, innovative, objective, and quality

contributions to advanced vehicle technology development. The President’s Advanced Energy

Initiative defines the goal of developing a plug-in hybrid vehicle with 40 miles of electric range as a 

means of changing the way we fuel our vehicles. The PHEV research completed in FY06 explored this 

and many other potential PHEV design scenarios. PHEV technology has great potential to transition

our nation’s transportation energy demand away from petroleum. However, finding ways to address 

the high component costs and narrow the gap between vehicle design and consumer behavior through 

technology optimization will be critical to achieving the petroleum displacement potential of PHEVs. 

NREL will execute a continuation of its PHEV research in FY07. The goal will be to develop and 

demonstrate potential solutions to technical barriers identified by past research. Emphasis will be 

placed on fuel economy and emissions test procedures and reporting methods, real-world travel 

behavior analysis, exploration of alternative economic scenarios, and engine and emissions control

system modeling for PHEV duty cycles. These tasks will contribute to the overall FreedomCAR

PHEV research plan in the areas of analysis, research and development, and test and validation. 

Finally, the team plans to continue strengthening its collaborative relationships with industry

colleagues. With NREL’s contributions and the contributions of others, the auto industry and the U.S. 

Department of Energy can lead to way toward widespread introduction of PHEV technology. 

 5
Section 1 

Plug-In Hybrid Electric Vehicle Fuel Economy Reporting Methods

PHEVs differ significantly from existing vehicles. They consume two fuels (petroleum and electricity) 

at rates that depend on the distance driven and the aggressiveness of the cycle. The Society of 

Automotive Engineers J1711 Recommended Practice, created in 1999, provides the fundamentals for 

measuring fuel economy of off-vehicle charge-capable vehicles (i.e., plug-in hybrids and electric 

vehicles). Seven years later, with a much better understanding of how PHEVs will likely operate, 

some improvements to the original procedure are recommended. The team’s specific 

recommendations are:

• Report gasoline and electricity consumption separately, which allows the reported results to 

be used for vehicle operating cost comparisons, fuel consumption, and CO2 emissions 

estimates. 

• Revise the end-of-test criteria to more accurately determine the distance driven in chargedepleting mode, fully capture the petroleum displacement potential of longer-range PHEVs, 

and improve the reporting accuracy for short-range PHEVs. 

• Assume that vehicles will be fully charged once per day because there is an economic 

incentive for consumers to recharge their vehicles at least once per day, if not more often. 

The recommended improvements to the fuel economy reporting methods have been adopted in our 

analyses, and the team intends to work with other labs and regulatory agencies to enact similar 

improvements in their procedures. The analysis also identified the need to develop a new utility factor 

relationship, based on the best available travel survey data, and to explore the implementation of a 

driving-type specific utility factor to account for the fact that most short-distance travel will be urban 

in nature and most long-distance travel will be highway in nature. Finally, it has been determined that 

the current Environmental Protection Agency certification cycle adjustment factors provide an 

inaccurate prediction of real-world PHEV consumption and should also be revised. 

For a more detailed discussion of this effort, please refer to sections 1.1 and 1.2. 

 6
Section 1.1 

Title: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Vehicles”

Type: Presentation 

Authors: Jeff Gonder and Andrew Simpson 

Date: Feb. 1, 2006 

Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team

Abstract: Identifies modifications to the SAE J1711 Recommended Practice to better represent the fuel

economy and electric consumption of PHEVs 

 7
Jeff Gonder

Andrew Simpson, Tony Markel, 

Aaron Brooker and Michael O’Keefe

National Renewable Energy Laboratory

February 1, 2006

Measuring and Reporting 

Fuel Economy of Plug-in 

Hybrid Electric Vehicles

2

Challenges of PHEV Fuel Economy Reporting

• Two fuel sources

• Relative consumption of each fuel 

depends on duty cycle

– distance driven between recharge events 

– urban, highway, high-speed

• Must enable stakeholders to make

comparisons with other vehicles

8
3

Stakeholder Perspectives

• Motorists

– operating cost; comparison shopping

• Policymakers/Government

– national petroleum impact

• Environmentalists

– national CO2 production

• Manufacturers

– benchmarking; certification procedures

4

Misleading PHEV Fuel Economy Reporting 

Non-Standardized Testing of Prototype Vehicle

E.g. – EnergyCS reports for EDrive Prius*

– 50-60 miles “boosted driving” at 100-150 mpg

– “200 mpg possible...most users will get 100 mpg”

– “7200 Wh usable battery window”

– no charging Æ ~50 mpg (normal Prius)

* http://www.energycs.com/Edrive-FAQ.html

• Cannot compare performance with

other vehicles

– drive cycles not standardized

– driving distance not weighted

9
5

Recommendations

• Use modified SAE J1711 

Recommended Practice

• Report gasoline & electricity

consumption separately

– gasoline equivalency does not support 

range of stakeholder perspectives

• Emphasize annual cost for comparison

– current approach on FuelEconomy.gov

6

SAE J1711

Recommended Practice for Measuring 

Fuel Economy of Hybrid Electric Vehicles

10
7

Overview

• Vehicle Classifications

– Off Vehicle Charge 

Capable (OVC-capable)

– Not Off Vehicle Charge 

Capable 

• Test Types

– Partial Charge Test

– Full Charge Test

• Operating Modes

– Hybrid

– Conventional

– EV

• Driving Profiles

– UDDS

– Highway

– US06

– SC03

PCT

PCT-CV FCT-EV FCT-HEV

FCT

PCT-HEV

Final

PCT FCT-UF Only OVC-capable 

vehicles

Both OVC-capable

and not OVC-capable

vehicles

8

“Partial Charge Test” (PCT)

Charge Sustaining (CS) Operation

SOC

(%)

Distance

100%

Charge Sustaining

SOC Level

gasoline

D

mpgCS =

D = Two UDDS or Two HWFET Cycles

11
9

“Full Charge Test” (FCT)

Charge Depleting (CD) Operation

SOC

(%)

Distance

100%

Charge

Sustaining

SOC Level

All-Electric Range (AER)

Engine Turns On

Continued CD Operation

Measure 

Recharge 

At End

kWh gal

elec energy gasoline

D

mpgCD

33.44 /

_ +

=

D = Four UDDS or Three HWFET Cycles (~30 miles)

(if no engine on in first 30 miles continue to run cycles until it does turn on)

10

Weighting with National Driving Statistics

Use Utility Factor (UF) Miles Weighting

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 100 200 300 400 500

Distance, D (mi)

Probability <= 

D (%)

1995 NPTS Daily Driving Probability

UF (Miles Traveled Probability)

“typical” daily driving (median) = 30 miles

“average” daily driving (mean):

= 50 miles

50% of fleet VMT occurs within the first 40 

miles of travel

∑

∑ ∑

∞

=

=

∞

= +

+

=

0

0 ( 1)

( * )

( * ) ( * )

( )

i

i

D

i i D

i i

P i

P i P D

UF D

∑

∞

=0

( * ) i

i P i

12
11

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 100 200 300 400 500

Distance, D (mi)

Utility Factor, UF

Utility Factor Applied to FCT Fuel Economy

For 30 mile FCT, 

UF = 0.42

CD CS

CD UF

mpg

UF

mpg

UF mpg (1 )

1 , − +

=

42% of fleet VMT 

occurs within the

first 30 miles of 

travel

12

Final Steps

• Charging frequency assumption

– once every two days

• Composite fuel economy

– no discussion in SAE J1711

– assume retain 55/45% weighted harmonic 

average for city/highway

CD UF CS

cycle

mpg mpg

mpg 1 1

2

,

+

=

13
13

Important Points from SAE J1711

• Fuel consumption measured in all 

modes

– CD fuel consumption alone overstates mpg

– includes accounting of electricity usage

• Utility Factor

– properly accounts for and weights all trip 

lengths

– necessary for extrapolation to national 

benefits

14

Recommended 

Improvements to 

SAE J1711

14
15

Improvement 1

Report Electricity Separately

• Problem: Energy Equivalence Method does not satisfy

stakeholder perspectives

• Solution: Report mpg, Wh/mi & annual operating cost

PCT Results 50 mpg 50 mpg

51.8 mpg, 8.4 Wh/mi,

$634/yr

51.1 mpg, $631/yr

30 mi, 0.5 gal, 1.2 kWh

PHEV5

59.3 mpg, 35.0 Wh/mi, 

$591/yr

J1711 rev. 1

J1711 55.9 mpg, $577/yr

FCT Results 30 mi, 0.15 gal, 5 kWh

Example PHEVs* PHEV30

* Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh;

Annual Miles = 15,000 (per EPA annual cost calculation)

16

Improvement 2

Determination of UF Weighting Distance

E.g. –PHEV5 and PHEV30 UDDS Scenarios:

SOC

(%)

Distance

100%

Charge Sustaining

SOC Level

Engine

On

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 6 Cycle 7

Engine On

P Current Proposed roposed

Cycle 5

Charge Sustaining

SOC Level

D D D

For PHEV5: recharge energy 

accurate but D is too long

For PHEV30: recharge 

energy is low and D is too 

short

15
17

Improvement 2

Determination of UF Weighting Distance

• Problem: CD distance not accurately captured

• Solution: End FCT test at the end of the cycle in which CS SOC 

is observed 

– most significant for PHEVs with larger batteries

Revised FCT 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh

PCT Results 50 mpg 50 mpg

52.1 mpg, 9.6 Wh/mi,

$632/yr

51.8 mpg, 8.4 Wh/mi,

$634/yr

30 mi, 0.5 gal, 1.2 kWh

PHEV5

63.3 mpg, 40.5 Wh/mi, 

$564/yr

J1711 rev. 1+2

59.3 mpg, 35.0 Wh/mi, 

$591/yr

J1711 rev. 1

FCT Results 30 mi, 0.15 gal, 5 kWh

Example PHEVs* PHEV30

* Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh;

Annual Miles = 15,000 (per EPA annual cost calculation)

18

Improvement 3

Change Charging Frequency Assumption to Once/Day

• Problem: Assumes recharge every other day

• Solution: Assume charge daily

– economic incentive to charge

– could charge multiple times per day

Revised FCT 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh

PCT Results 50 mpg 50 mpg

54.3 mpg, 19.2 Wh/mi, 

$619/yr

52.1 mpg, 9.6 Wh/mi,

$632/yr

PHEV5

86.4 mpg, 80.9 Wh/mi, 

$482/yr

J1711 rev. 1+2+3

63.3 mpg, 40.5 Wh/mi, 

$564/yr

J1711 rev 1+2

Example PHEVs* PHEV30

* Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh;

Annual Miles = 15,000 (per EPA annual cost calculation)

16
19

Open Issue: Driving Type by Daily Distance

• Currently same procedure used for both 

City & Highway tests, but...

– CD operation likely to include more short 

city trips

– CS operation likely to include more long

highway trips

• Need additional national data

– for now, keep same approach for each

– use future data to adjust procedures

20

Conclusions

• Current SAE J1711 recommended 

practice provides guideline for consistent 

reporting of hybrid vehicle fuel economy

for a range of vehicle types

• Revisions needed to,

– fully satisfy the needs of stakeholders

– more accurately report PHEV performance

17
21

51.1 mpg, $631/yr 55.9 mpg, $577/yr J1711 Original 

Result

51.8 mpg, 8.4 Wh/mi, $634/yr 59.3 mpg, 35.0 Wh/mi, $591/yr Keep electricity 

separate

54.3 mpg, 19.2 Wh/mi, $619/yr

52.1 mpg, 9.6 Wh/mi, $632/yr

PHEV5

86.4 mpg, 80.9 Wh/mi, $482/yr Daily Charging

(New Final Result)

63.3 mpg, 40.5 Wh/mi, $564/yr Capture all CD 

operation

Example PHEVs* PHEV30

* Assumptions: mpgCS = 50 mpg; Gas = $2.15/gal; Electricity = $0.09/kWh; 

Annual Miles = 15,000 (per EPA annual cost calculation)

Summary of Improvements to J1711

In DOE and Industry’s best interest to provide useable fuel economy values

22

Next Steps

• Revive SAE J1711 Working Group

– NREL provided analysis to initial 

recommended practice development

– ANL was liaison between industry working 

group and EPA and CARB

• Share recommendations with EPA and 

others

18
Section 1.2 

Title: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles”

Type: Paper

Authors: Jeff Gonder and Andrew Simpson 

Date: October 2006 

Conference or Meeting: Published at the 22nd International Battery, Hybrid, and Fuel Cell Electric 

Vehicle Symposium and Exposition 

Abstract: Identifies modifications to the SAE J1711 Recommended Practice to better represent the fuel

economy and electric consumption of PHEVs 

 19
1 

MEASURING AND REPORTING FUEL ECONOMY OF 

PLUG-IN HYBRID ELECTRIC VEHICLES1

JEFFREY GONDER

National Renewable Energy Laboratory (NREL)

ANDREW SIMPSON 

National Renewable Energy Laboratory (NREL)

Abstract 

Plug-in hybrid-electric vehicles (PHEVs) have recently emerged as a promising alternative technology 

to dramatically reduce fleet petroleum consumption. However, the fuel economy of many recent 

prototype and theoretical vehicles has varied widely and often been exaggerated in the press. PHEVs 

present a significant challenge as compared with conventional vehicle fuel economy reporting because 

they receive energy from two distinct sources and exhibit widely varying per-mile consumption, based 

on the drive cycle and distance driven. This paper reviews various techniques used to characterize 

PHEV fuel economy and discusses the relative merits, limitations, and best uses of each. This review 

will include a discussion of the SAE J1711 Recommended Practice issued in 1999 and will comment 

on how recent analysis indicates that the described procedures could be improved for reporting PHEV 

fuel economy. The paper highlights several critical reporting practices accurately captured by SAE 

J1711: use of standardized drive cycles; inclusion of charge depleting and charge sustaining operation; 

and using utility-factor weighting to properly combine the vehicle’s operating modes using 

representative driving statistics. Several recommended improvements to J1711 are also discussed: 

separate reporting of fuel and electricity use; better determination of the vehicle’s charge depleting 

performance; and application of a once-per-day vehicle charging assumption. As the U.S. 

Environmental Protection Agency (EPA) considers changes to window-sticker fuel economy test 

procedures, and the original issuance of SAE J1711 expires, the authors hope to stimulate the 

necessary discussion and contribute to adoption of consensus reporting metrics. In order for the 

resulting metrics to be useful, stakeholders must be able to translate the numbers into sound 

predictions of relative vehicle energy cost, petroleum use, and potential carbon dioxide (CO2) 

production. 

Keywords: Plug-in Hybrid; Grid-connected HEVs; Vehicle Performance; Energy Efficiency, Energy 

Consumption; Codes, Standards, Legislation, Regulations; Environmental Impact 

Introduction 

A PHEV is a hybrid-electric vehicle (HEV) with the ability to recharge its electrochemical energy 

storage with electricity from an off-board source (such as the electric utility grid). The vehicle can 

then drive in a charge-depleting mode that reduces the system’s state-of-charge (SOC), thereby using 

1

 This work has been authored by an employee or employees of the Midwest Research Institute under 

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States 

Government retains and the publisher, by accepting the article for publication, acknowledges that the 

United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish 

or reproduce the published form of this work, or allow others to do so, for United States Government 

purposes. 

20
electricity to displace petroleum fuel that would otherwise be consumed. PHEVs typically have 

batteries that are larger than those in HEVs so as to increase the potential for petroleum displacement. 

Plug-in hybrid-electric vehicles have recently emerged as a promising alternative to displace a 

significant fraction of vehicle petroleum consumption with electricity. This potential derives from 

several factors. First, PHEVs are potentially well-matched to motorists’ driving habits, particularly the 

distribution of miles traveled each day. Second, PHEVs can build off the success of production HEVs 

in the marketplace. Finally, PHEVs are very marketable in that they combine the beneficial attributes 

of HEVs and pure battery electric vehicles (BEVs) while simultaneously alleviating the disadvantages 

of each. As a result, PHEVs have the potential to come to market, penetrate the fleet, and achieve 

meaningful petroleum displacement relatively quickly. Few competing technologies offer this 

potential combined rate and timing of reduction in fleet petroleum consumption [1]. 

Plug-in hybrid-electric vehicles are typically characterized by a “PHEVx” notation, where “x” 

generally denotes the vehicle’s All-Electric Range (AER) – defined as the distance in miles that a fully 

charged PHEV can drive on stored electricity before needing to operate its engine. The California Air 

Resources Board (CARB) uses the standard Urban Dynamometer Driving Schedule (UDDS) to 

measure the all-electric capability of PHEVs and provide a fair comparison between vehicles [2]. 

According to this definition, a PHEV20 can drive 20 all-electric miles (32 kilometers) on the test cycle 

before the first engine turn-on. However, this all-electric definition fails to account for PHEVs that 

might continue to operate in charge-depleting mode after the first engine turn-on. 

To better capture the range of PHEV control strategies and configurations, the authors of this paper 

use a different definition of PHEVx that is more-appropriately related to petroleum displacement. 

Under this definition, a PHEV20 contains enough useable energy storage in its battery to displace 20 

miles of petroleum consumption on the standard test cycle. Note that this definition is not meant to 

imply all-electric capability because the vehicle operation will ultimately be determined by component 

power ratings, the vehicle’s control strategy, and the nature of the actual in-use driving cycle. 

The key limitation of the PHEVx designation is that it is a relative metric that only describes potential 

petroleum displacement relative to the same vehicle operating in charge-sustaining mode. It does not 

provide information about absolute vehicle fuel economy. For example, a PHEV20 sedan may 

achieve 40 miles per gallon (mpg), or 5.9 liters per 100 kilometers (L/100 km) in charge-sustaining 

operation, whereas a PHEV20 SUV may only achieve 25 mpg (9.4 L/100 km), but this is not captured 

by the PHEVx metric. Furthermore, a fully-charged all-electric PHEV20 uses no petroleum over a 20-

mile trip, leading to the impressive result of infinite miles-per-gallon (0 L/100 km) of petroleum use. 

Such a result is clearly helpful in marketing PHEVs, but does not provide much information about 

real-world potential because in reality motorists drive a variety of distances – some short, some long. 

An objective method is clearly needed for evaluating and reporting PHEV fuel economy, so as to 

avoid exaggerated claims and generate a vehicle rating that translates in some way to expectations for 

the real-world vehicle performance. 

The reader should note that this paper will emphasize imperial units (miles and gallons for driving 

distance and gasoline usage, respectively) and fuel economy rather than consumption to be consistent 

with U.S. Government regulatory standards. Also note that, although this paper was written primarily 

from a fuel economy perspective (with little discussion of emissions measurement), these 

recommended procedures for PHEV testing and reporting are designed for suitable application to both 

fuel economy and emissions measurements. 

PHEV fuel economy reporting methods 

Determining a “fuel economy rating” for PHEVs presents a particular challenge as compared with 

other vehicle technologies because the motive power for the vehicle is derived from two distinct 

sources: a chemical fuel (typically gasoline) and electricity. The relative consumption of each fuel 

depends greatly on the duty cycle over which the PHEV operates. As with other vehicles, the type of 

21

2 
3 

driving (urban, highway, high speed, etc.) is a very important factor, but more important to PHEVs is 

the distance driven between vehicle recharging events. In addition to appreciating the factors 

influencing fuel vs. electricity consumption, the presence of two energy sources presents a challenge in 

providing a rating comparable to vehicles using a single mile-per-gallon economy or liter-per-100 

kilometers consumption value. 

One approach would be to report only the fuel use of the vehicle. This method captures the petroleum 

consumption impact, but fails to account for the impacts and costs of the additional electricity 

consumption. Alternatively, the fuel and electricity use can be combined into a single metric that 

makes assumptions about the equivalent values of the two energy forms. One example is the 

commonly used energy-equivalency of gasoline and electricity (1 gallon [gal] = 33.44 kilowatt-hours 

[kWh]), which leads to a metric that accounts for both, but fails to account for differences in the 

supply-chain efficiency of each. Even if a different energy-equivalence factor is used to account for 

supply-chain efficiencies, it does not account for likely differences in the primary energy source for 

each supply chain. One megajoule of coal (for electricity) may have the same primary energy content 

as one megajoule of crude oil (for gasoline), but these sources are certainly quite different from an 

economic, environmental, and geopolitical perspective. Other examples of equivalency factors include 

cost-equivalency factors (e.g., 1 gal @ $3/gal = 30 kWh @ $0.1/kWh) and CO2 emissions-equivalency 

factors. However, all metrics based on equivalency factors suffer the disadvantage of not providing 

useful information about net petroleum consumption impact. 

Ultimately, there are a variety of stakeholder perspectives that must be addressed when devising a 

method for fuel economy reporting. Motorists may be primarily concerned with vehicle operating 

costs and therefore may want a metric that conveys the magnitude of those costs. On the other hand, 

policymakers and environmentalists may be primarily concerned with national petroleum impact and 

CO2 production levels and may want a metric that can be extrapolated to the fleet level. Vehicle 

manufacturers, however, are obliged to focus on benchmarking and certification procedures and will 

also want a metric that is well-suited to this purpose. 

The authors argue that the measurement technique ultimately selected must capture specific 

standardized performance aspects to accurately evaluate the tested vehicle with respect to annual 

operating costs, national petroleum impact, and CO2 production. Furthermore, the testing to obtain the 

performance ratings must be conducted over consistent and representative standardized driving 

profiles, with appropriate weightings applied to account for typical driving distances and to make 

comparisons with other vehicle technologies possible. 

SAE J1711 Recommended Practice 

While the various reporting approaches discussed in the previous section have been used by a variety 

of individuals for particular applications or analyses, the most formalized PHEV reporting procedure 

to date appears to be contained within the Society of Automotive Engineers (SAE) J1711 

Recommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric 

Vehicles [3]. Originally issued in 1999, the document seeks to provide a technical foundation for 

reporting procedures applied to a range of HEV designs, including those with “Off-Vehicle-Charge” 

(OVC) capability (i.e., PHEVs). Figure 1 presents a general overview of the steps in SAE J1711 that 

build to determining a final fuel economy rating over a particular test cycle. The specific test cycles 

addressed in the document include the UDDS and the Highway Fuel Economy Test (HWFET), which 

the EPA use for light-duty fuel economy testing. 

Non-OVC-capable conventional HEVs would only complete the steps on the left side of Figure 1, 

whereas PHEVs follow the steps from both sides of the figure. The Partial-Charge Test (PCT) is 

designed to measure the vehicle’s performance in a charge-neutral hybrid operating mode, such as 

after a PHEV has depleted its energy storage system (ESS) to the desired charge-sustaining operating 

level. The Full-Charge Test (FCT) measures the vehicle’s performance when the initially fullycharged ESS is permitted a net discharge through the course of the test cycle. The bottom row in 

22
PCT

PCT CV FCT-EV FCT-HEV

FCT

PCT HEV

Final

PCT FCT-UF

Figure 1 indicates the provisions in J1711 to account for user-selectable Conventional Vehicle (CV) 

and Electric Vehicle (EV) operating modes. However, the test procedure discussion in this paper 

assumes that the PHEV is only operated in a default/hybrid operating mode. The remaining rows in 

the figure follow the steps through measuring the results of the PCT and FCT, applying a Utility 

Factor (UF) weighting to the FCT results, and then combining together the PCT and the weighted FCT 

results by making an assumption about how frequently the vehicle will be recharged. The remainder 

of this section will briefly describe each of these steps. 

- -

PCT 

PCT-CV FCT-EV FCT-HEV

FCT 

PCT-HEV 

Final 

PCT FCT-UF

CV – Conventional Vehicle mode 

EV – Electric Vehicle mode 

HEV – Hybrid Vehicle mode 

FCT – Full-Charge Test 

PCT – Partial-Charge Test 

UF – Utility Factor weighted 

Figure 1: Overview of J1711 approach for determining “final” PHEV fuel economy for a test cycle 

based on Partial-Charge Test (PCT) and Full-Charge Test (FCT) results 

Figure 2 illustrates an example of how the ESS SOC may vary over the course of the PCT. While the 

instantaneous SOC may move up and down during the test, the final SOC should return to roughly the 

same level as the initial SOC at the start of the test. The PCT fuel economy is calculated by the 

following equation, where “D” is the test distance in miles, “Vfuel” is the volume of fuel consumed in 

gallons, and “mpgCS” is taken to be the charge-sustaining mile-per-gallon rating. 

D 

mpgCS = V fuel

Distance 

SOC 

(%) 

100% 

Charge Sustaining 

SOC Level 

D = Two UDDS or Two HWFET Cycles 

Figure 2: PCT to measure Charge-Sustaining (CS) vehicle fuel economy; illustrated with application 

to the UDDS or HWFET test cycles 

Figure 3 provides a similar example of how SOC may vary over the course of the SAE J1711 FCT. 

The SOC begins the cycle at 100% and decreases as the vehicle is driven electrically. The distance 

traveled up until the PHEV engine turns on is recorded as the vehicle’s All-Electric Range (as defined 

in the introduction to this paper) for the particular test cycle. Following this initial engine turn-on, the 

vehicle may continue operating in a Charge-Depleting (CD) mode with the engine and ESS/motor 

working together in a blended manner to propel the vehicle. For the two principal test cycles, the FCT 

is terminated after four repetitions of the UDDS or three repetitions of the HWFET. However, if the 

engine has not turned on at that point, the cycles continue repeating until it does turn on. At the 

conclusion of the test, the ESS is fully recharged using off-board electricity, and the required electrical 

charging energy is recorded. The following equation is used to calculate the CD mile-per-gallon rating, 

“mpgCD,” as determined by the SAE J1711 FCT. The new terms in this equation are “Echarge,” the 

required electrical recharge energy in kilowatt-hours, and “Egasoline,” a constant equal to 33.44 kWh/gal 

23
1995 NPTS Daily Driving Probability

UF (Miles Traveled Probability)

“typical” daily driving (median) 30 miles

“average” daily driving (mean):

50 miles

50% of fleet VMT occurs within the first 40 

miles of travel

∑

∑ ∑

∞

∞

+

+

0

0 ( 1)

( * )

( * ) ( * )

( )

i

i

D

i i D

i i

P i

P i P D

UF D

∑

∞

0

( * ) i

i P i

representing the energy content of a gallon of gasoline. Note that this approach converts the electrical 

recharge energy into an energy-equivalent volume of gasoline to add to the actual volume of fuel 

consumed. 

D 

mpgCD = Ech arg e V fuel +

Egasoline 

SOC 

(%) 

100% 

Charge 

Sustaining 

All-Electric Range (AER) 

Engine Turns On 

Continued CD Operation 

Measure 

Recharge 

At End 

Distance SOC Level 

D = Four UDDS or Three HWFET Cycles (~30 miles)

(if no engine on in first 30 miles continue to run cycles until it does turn on) 

Figure 3: FCT to measure Charge-Depleting (CD) fuel economy, illustrated with application to the

UDDS or HWFET test cycles

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

= 

= 

“typical” daily driving (median) 

=

= = =

=

1995 NPTS Daily Driving Probability 

UF (Miles Traveled Probability) 

= 30 miles 

“average” daily driving (mean): 

= 50 miles 

50% of fleet VMT occurs within the first 40 

miles of travel 

∑

∑ ∑

∞

=

=

∞

= +

+

=

0 

0 ( 1)

( * )

( * ) ( * )

( )

i 

i 

D 

i i D

i i

P i

P i P D

UF D

∑

∞

=0 

( * ) i 

i P i

Probability <= D (%) Probability <= D (%)

0100200300400500

Distance, D (mi)

Figure 4: Illustration of Utility Factor (UF) weighting with U.S. national driving statistics 

The next key step in SAE J1711 is to weight the FCT result with national driving statistics. Again, 

because of the focus on U.S. standards, the weighting data is taken from information on U.S. driving 

behavior. The purpose of the weighting is to determine on aggregate how much of a vehicle’s driving 

is expected to occur in its CD mode vs. in its CS mode. Figure 4 demonstrates how the appropriate 

weighting factor is determined. The top line in the figure represents the daily driving probability 

distribution determined by the 1995 National Personal Transportation Survey (NPTS) conducted in the 

United States. For each distance, “D,” given along the x-axis, the corresponding point on the y-axis 

indicated by the curve is the probability that a vehicle’s total daily driving will be less than or equal to 

D. The point at which the NPTS probability curve crosses 50% is the median or “typical” daily 

driving distance of 30 miles. However, because longer trips consist of more driving miles, the average 

daily driving distance is greater – 50 miles as given by the top equation in Figure 4, where “i” is the 

mileage increment for driving statistics in steps of 1 mile and “Pi” is the probability that a vehicle will 

be driven i miles per day. The utility of a CD operating mode to the vehicle fleet must be calculated 

24
on a miles-driven probability basis rather than a “typical vehicle” driving basis because fuel 

consumption is related to total driven miles, and the 50% of vehicles with daily driving distances 

greater than the median account for a larger portion of all driven miles. The bottom equation in Figure 

4 determines utility on a miles-driven basis, including in the utility calculation all miles for vehicles 

with daily driving less than the CD distance, as well as the initial miles for vehicles with daily driving 

greater than the CD distance. The second curve shows the resulting UF calculation as a function of D. 

For this curve, the interpretation of the 50% probability crossing point is that 50% of fleet Vehicle 

Miles Traveled (VMT) occurs within the first 40 miles of daily driving. 

In SAE J1711, the FCT distance used to determine mpgCD is roughly 30 miles (assuming four UDDS 

cycles or three HWFET cycles). The UF value corresponding to this distance is 0.42, which would be 

used in the following equation to calculate the UF-weighted CD mile-per-gallon rating: “mpgCD,UF.” 

1 

mpg CD,UF = UF (1−UF) +

mpgCD mpgCS 

The final step in SAE J1711 for calculating the cycle fuel economy, “mpgcycle,” for a PHEV is to 

assume the vehicle is equally likely to be driven in a UF-weighted CD mode as to be driven in a CS 

mode. This is similar to assuming that the vehicle is equally likely to be charged daily as to never be 

charged at all, or that the vehicle is charged on average once every 2 days. The equation below applies 

this equal probability assumption. 

2

mpgcycle = 1 1

+

mpgCD,UF mpgCS 

Because the above-described approach only determines the fuel economy for specific test cycles, it is 

assumed that a composite PHEV fuel economy number would have to be obtained by employing the 

EPA’s multi-cycle weighting methodology. The current-status EPA approach would be to apply a 

55/45% weighted harmonic average to the results of the city/highway test cycles. 

4 Important points and recommended changes to SAE J1711 

The SAE J1711 Recommended Practice addresses several of the key issues necessary for properly 

measuring PHEV fuel economy. In particular, the document correctly recognizes that vehicle 

performance must be evaluated in both CD and CS operating modes, and that both fuel and net 

electricity consumption must be included. To account for the utility of CD operation, SAE J1711 also 

correctly applies a UF approach to account for the distribution of daily driving behavior that is 

weighted based on daily distances driven. This step is necessary to determine a PHEV fuel economy 

rating that is comparable on a national benefits scale to other vehicles’ ratings (again assuming that 

national driving statistics were used to generate the UF curve). 

There are also several aspects of SAE J1711 that the authors recommend modifying. Three of the 

most important changes include keeping fuel and electricity consumption separated, better determining 

the CD operating distance for UF weighting, and changing the charging frequency assumption from 

once every other day to once daily. The remainder of this section will discuss each of these 

recommendations in more detail and provide an example of their relative impact. 

4.1 Recommendation 1: Report electricity separately 

As discussed in section 2 of this paper, the energy equivalence method of treating electricity 

consumption as if it were gasoline does not support the needs of stakeholders that use the vehicle’s 

fuel economy rating. A more useful approach to that currently suggested by SAE J1711 would be to 

25
present a fuel economy and electricity consumption rating for the vehicle (such as providing a watthour-per-mile (Wh/mi) value in addition to the mile per gallon number). When combined with a 

distance driven over a period of time (that is representative of the typical daily distance distribution), 

these two numbers would provide an estimate of the volume of fuel used and the electrical charging 

energy that went into the vehicle over that operating period. A stakeholder who knew a baseline 

vehicle’s fuel consumption and the production mix of a certain region’s electrical utility could then 

take these separate fuel and electrical energy values to determine petroleum and CO2 impact. For the 

benefit of consumers who are typically most interested in their vehicle’s total energy cost (including 

fuel and electricity use), this rating approach could also consider average gasoline and electricity 

prices along with a typical annual driving distance to estimate a representative energy cost comparable 

from vehicle to vehicle. 

Table 1 provides an example of the impact this revision to J1711 would have on two hypothetical 

PHEVs. The assumptions used to generate the annual energy cost estimates for all the tables in this 

paper were fuel and electricity costs of $2.50/gal and $0.09/kWh, respectively, and an annual driving 

distance of 15,000 miles (a typical annual VMT for U.S. drivers). Note also that all of the annual cost 

estimates are for illustration purposes only, as they are extrapolated from hypothetical test results over 

one cycle only. As the results in Table 1 illustrate, this change (to report electricity separately) does 

not by itself produce a large change in the energy cost estimate, but it does provide more accurate and 

useful information about the distribution of energy use between gasoline and electricity. 

Table 1: Example impact of Recommendation 1 – reporting electricity separately* 

Example PHEVs PHEV5 PHEV30 

PCT Results 50 mpg 50 mpg 

FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh 

J1711 51.1 mpg, $733/yr 55.9 mpg, $671/yr 

J1711 Recommendation 1 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr 

*Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year 

4.2 Recommendation 2: Determination of utility factor (UF) weighting distance 

A second recommended change to the existing J1711 reporting procedure would be to improve 

determination of the CD operating distance for UF weighting. Figure 5 provides an example of the 

SOC profile during the UDDS FCT (as described in Figure 3) for the two example PHEV5 and 

PHEV30 vehicles in order to demonstrate how the existing procedure could be improved. For both 

example vehicles, the engine turns on during the first four cycle repetitions, so the existing procedure 

calls for ending the test after completing the fourth cycle and measuring the recharge energy required. 

As the figure shows for the PHEV5 vehicle, the ESS SOC drops quickly during the first half of the 

initial UDDS cycle, and continues to drop at a somewhat slower rate once it begins operating in a 

blended (engine plus ESS/motor) mode. From partway through the second cycle until the end of the 

fourth cycle, the PHEV5 operates in a CS mode. For the PHEV30, the ESS discharges during allelectric vehicle operation through the first three cycles, and then continues to discharge at a slower rate 

during the fourth cycle as the vehicle operates in a blended mode. By the end of the fourth cycle when 

the existing SAE J1711 approach calls for completing the test, the ESS has not yet reached its CS SOC 

level. By holding the FCT to the fixed length of four-cycles, the existing J1711 approach actually 

averages together roughly 50% CD operation and 50% CS operation to obtain the “CD rating” for the 

PHEV5, and it also does not credit the PHEV30 for its continued CD operation beyond the end of the 

fourth cycle (instead assuming the CS rating applies to all cycles after the first four). 

Instead of fixing the FCT length, the authors recommend ending the FCT after completing the cycle 

during which the CS SOC is reached. In a practical implementation, this would mean tracking the 

total Ampere-hour (Ah) discharge from the vehicle ESS and calculating when the manufacturer’s CS 

26
SOC level was reached, or determining when the net ESS Ah change either increases or remains 

within a tolerance during all or most of one cycle. (The latter approach could result in one full cycle of 

CS operation included at the end of the FCT, so the following steps could be adjusted accordingly in 

order to set the UF-weighting distance to only include cycles in which CD operation occurred.) 

Assuming that it could be determined when the CS operating level was reached, the end of the cycle 

during which this occurred would be used as the distance, D, in the UF-weighting, and the recharge 

energy would be measured at this point. As Figure 5 illustrates, the modified FCT would be 

completed after two cycles for the PHEV5 vehicle and the recharge energy would remain basically the 

same. For the PHEV30 vehicle, the modified FCT would be extended to seven total cycles and the 

recharge energy would be greater (accurately reflecting the energy required to return the vehicle from 

a CS SOC state to fully-charged). 

Figure 5: Hypothetical FCT SOC profiles for two example PHEVs over a UDDS cycle test 

Table 2 presents an example of the impact this change might have on estimated energy use and cost. 

The table compares the result of just modifying J1711 with the separate electricity reporting 

recommendation to the result of using J1711 with separate electricity reporting and a modified FCT to 

more accurately determine the UF weighting distance. The result of the change is minor for the 

PHEV5 vehicle, but is noticeable for the PHEV30 vehicle – producing a 5% decrease in the annual 

energy cost estimate. The impact of the change should be largest for vehicles with a large ESS, for 

which the existing procedure potentially misses many miles of continued CD operation between the 

end of the FCT and when the vehicle actually begins CS operation. 

Table 2: Example impact of Recommendation 2 – determining UF weighting distance* 

SOC 

(%) 

Distance 

100% 

PHEV5 Charge 

Sustaining SOC Level 

Engine 

On 

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 6 Cycle 7 

Engine On 

Proposed, PHEV5 Existing Proposed, PHEV30

Cycle 5 

PHEV30 Charge 

Sustaining SOC Level 

D D D 

For PHEV5: 

t 

recharge energy 

accurate but D is too long 

For PHEV30: 

r

recharge 

energy is low and D is too 

shor

SOC 

(%)

Distance

100%

PHEV5 Charge 

Sustaining SOC Level

Engine 

On

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 6 Cycle 7

Engine On

Proposed, PHEV5 Existing Proposed, PHEV30

Cycle 5

PHEV30 Charge 

Sustaining SOC Level

D D D

For PHEV5: echarge energy

accurate but D is too long

For PHEV30: recharge

energy is low and D is too 

short

Example PHEVs PHEV5 PHEV30 

PCT Results 50 mpg 50 mpg 

Original FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh 

Revised FCT Results 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh 

J1711 Recommendation 1 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr 

J1711 Recommendations 1&2 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr 

*Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year 

Note that to ensure CS operation follows completion of the FCT, the FCT and PCT could be combined 

into one single procedure to first measure CD operation and then subsequent CS operation. However, 

the authors anticipate that comprehensive emissions measurement will still necessitate completion of a 

27
cold-start PCT, and so do not suggest moving away from two separate tests. Note also that the 

procedure for determining the UF weighting distance implicitly assumes that the average mpg and 

Wh/mile values can be uniformly applied over the vehicle’s driving up to distance, “D.” In reality, the 

vehicle will likely consume more electricity and less fuel early on in the cycles, and will shift to 

consuming more fuel and less electricity as it approaches the distance, “D.” A worthwhile approach to 

consider for capturing this effect would be to segment the utility factor in whole-cycle increments in 

order to weight the fuel and electricity use over each individual cycle for determining the total 

representative energy use estimate. However, the authors do not recommend this more complicated 

approach because the uncertainty introduced through necessary estimation of the recharge energy 

required for each cycle could easily offset the improved accuracy over a uniform CD operation 

assumption. In addition, the uncertainties in the data used to generate the UF curve could be amplified 

and inadvertently propagated when assigning individual weightings to each incremental cycle segment 

distance. 

4.3 Recommendation 3: Changing the charging frequency assumption 

The third recommended change to SAE J1711 is fairly simple but can have a large impact on reported 

energy consumption and cost. As described in section 3, the current approach averages together the 

UF-weighted CD result (which is intended to approximate once daily charging) and the CS result 

(which represents no charging). Because no reliable national data exists to predict how often PHEV 

drivers will plug in their vehicles, the original J1711 task force selected this equal weighting between 

“plug-in” and “non-plug-in” operation as a placeholder for combining the effects of these two 

operating modes. However, in the absence of conclusive data to capture expected charging frequency 

for PHEVs, the authors of this paper assert that once-per-day charging (represented by the UF-weighed 

CD result) is a better placeholder for combining CD and CS operation. This is because in addition to 

charging the vehicle either zero or one time per day, the PHEV driver could charge the vehicle 

multiple times per day (known as “opportunity charging”) whenever parked at a home, work, or other 

location that had a charging outlet. 

Especially during the early years of their introduction into the market, there will likely be a large price 

increment between a conventional or hybrid and a comparable PHEV. In order to recover some of this 

initial expense, there will be a large economic incentive for PHEV drivers to take advantage of the 

significantly lower energy cost to operate the vehicle on electricity rather than on gasoline alone. The 

relatively small early market penetration levels should also require fairly little utility control over 

vehicle charging to avoid exacerbating peak daytime electricity demand. This would permit PHEV 

drivers to act on the incentive to opportunity charge several times daily. Even so, until solid data sets 

become available to support an average charging frequency assumption greater than once daily (or 

between 0-1 times per day), once daily charging provides a reasonable placeholder for this frequency 

assumption. Because of the economic incentive to charge, especially in the initial years of PHEV 

adoption and test procedure application, this once per day assumption should provide a more accurate 

placeholder than a once every other day assumption. 

Table 3: Example impact of Recommendation 3 – changing the charging frequency assumption* 

Example PHEVs PHEV5 PHEV30 

PCT Results 50 mpg 50 mpg 

Revised FCT Results 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh 

J1711 Recommendations 1&2 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr 

J1711 Recommendations 1,2&3 54.3 mpg, 19.2 Wh/mi, $716/yr 86.4 mpg, 80.9 Wh/mi, $543/yr 

*Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year 

Table 3 provides the final example results highlighting the impact of adding this third recommended 

change to the first two. For both example vehicles, the final change causes the reported fuel economy 

28
to increase at the expense of a higher per-mile electricity consumption rating, but ultimately provides 

an overall reduction in the estimated annual energy cost. The observed impact is again much greater 

for the PHEV30 with its larger ESS – resulting in a 16% reduction in the annual energy cost estimate. 

4.4 Additional discussion 

There are two significant open issues not addressed in SAE J1711 that this document does not examine 

in detail. The first is the correlation between driving type and driving distance. The current-status UF 

weighting approach implicitly assumes that the daily distance distribution of the driving represented 

by a particular test cycle matches the average distribution given by national (U.S.) driving statistics. 

For instance, with the current two-cycle city and highway EPA approach, the same national driving 

statistics would determine the combined CD and CS weighting for the UDDS (city driving) and for the 

HWFET (highway driving) before merging these values into a composite rating (by applying the 

55/45% weighting of city/highway driving). This set UF weighting approach for each cycle neglects 

the fact that shorter city trips are likely to make up a larger fraction of CD operating miles, and longer 

highway trips are likely to make up a larger fraction of CS operating miles. 

If future travel surveys can begin to capture the variation of driving type by daily driving distance, 

then a unique UF curve could be selected for each cycle. In the mean time, it once again seems most 

appropriate to maintain application of the uniform UF curve to each cycle evaluated. The EPA’s 

proposed move to a five-cycle procedure [4] will present additional challenges, not the least of which 

is a dramatically increased burden of up to ten tests in order to complete the PCT and FCT for each 

cycle. An official revision to J1711 should consider the new procedure EPA officially adopts and 

balance decisions to improve accuracy with those to avoid excessive testing complexity and cost. 

The second challenging issue that will require further examination is how to apply EPA in-use fuel 

economy adjustment factors to a PHEV. The EPA introduced these adjustment factors in 1984 in an 

effort to quantify observed reductions in real-world fuel economy below certification cycle test results 

due to effects such as more aggressive driving and use of accessories (especially air conditioning). 

This adjustment approach is still in use today, although continued overestimation of in-use fuel 

economy has prompted the EPA to now consider more dramatic procedure revisions. The current 

methodology reduces the UDDS and HWFET test results by 10 and 22 percent, respectively, to 

determine the city and highway fuel economy estimates. However, the same methodology cannot be 

used to adjust a PHEV’s UF-weighted fuel economy and electricity consumption results because the 

effects that the adjustment factors are supposed to represent (such as more aggressive driving) would 

be observed prior to performing the UF weighting of CD and CS operation. Specifically, the adjusted 

cycle could impact the PCT and FCT mile per gallon and watt-hour per mile results, as well as the CD 

distance used for UF weighting. 

One possible approach to apply the EPA adjustment factors to a PHEV would be to reduce the PCT 

fuel economy in the same manner as would be done for a conventional vehicle, and determine the 

resulting increase in fuel volume consumed over a CS distance equal to the original (UF weighting) 

FCT distance. The UF weighting distance for the FCT would then be assumed to remain the same, 

with the calculated volume of fuel added into the FCT fuel economy result. An alternate approach 

would be to apply the adjustment factor to the PCT and FCT fuel economy and electricity 

consumption results, as well as to the CD distance (resulting in a reduced distance to use with the UF 

weighting curve). Further analysis will be required to determine the validity of either of these 

approaches. Fortunately, either method would maintain some applicability to the anticipated EPA 

procedure changes, as the EPA proposal retains a downward adjustment of measured fuel economy 

results to account for effects impossible to incorporate in laboratory dynamometer testing [4]. 

29
5 Summary and conclusions 

In its present form, the SAE J1711 recommended practice provides useful guidelines for consistent 

reporting of hybrid vehicle fuel economy across a range of vehicle types. Through application to 

standard drive cycles and weighting the utility of CD PHEV operation (based on national fleet 

statistics), J1711 provides a more objective comparison of PHEV performance to that of conventional 

and HEVs than do other less formalized rating approaches. J1711 nonetheless requires some revision 

to fully satisfy the needs of stakeholders using the fuel economy rating, and to further improve its 

accuracy in reporting PHEV performance. Table 4 summarizes the example impacts for the three 

major recommended changes described in this paper. 

Table 4: Summary of example impacts for recommended changes to SAE J1711* 

Example PHEVs PHEV5 PHEV30 

J1711 original result 51.1 mpg, $733/yr 55.9 mpg, $671/yr 

+ Keep electricity separate 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr 

+ Better capture CD distance 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr 

+ Assume once daily 

charging (New final result) 54.3 mpg, 19.2 Wh/mi, $716/yr 86.4 mpg, 80.9 Wh/mi, $543/yr 

*Assumes 50 mpg PCT, $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year 

The new results for the modified reporting approach provide a more accurate estimate of the petroleum 

savings each of these vehicles could provide, which was understated by the original J1711 result. 

Specifically, the petroleum consumption estimate is reduced by 6% for the PHEV5 and by 35% for the 

PHEV30. The new results also provide an estimate of the electricity consumption per mile that a 

typical user could expect the vehicle to achieve. From this more accurate description distinguishing 

fuel from electricity use, and assuming once daily charging, the results demonstrate a 2% reduction in 

the annual energy cost estimate for the PHEV5 and a 19% reduction in the annual energy cost estimate 

for the PHEV30 relative to the original J1711 result. The magnitude of the improved estimates for 

petroleum use and energy cost are greater for longer distance rated PHEVs because of the potential 

offered by their larger energy storage systems. 

It is in the best interest of all those evaluating the potential benefits of PHEVs to be able to objectively 

evaluate the technology relative to other vehicles. It should likewise be in the best interest of PHEV 

advocates to establish and follow consensus PHEV reporting procedures to avoid accusations of 

providing unfounded “hype” for the technology. In particular, the adopted procedures should 

characterize PHEV performance over a representative range of driving conditions, including proper 

weighting of typical vehicle daily driving distances. A discussion of accurate and objective PHEV 

fuel economy reporting is particularly important in the present context of increasing technical interest 

in PHEVs, expiration of the original issuance of SAE J1711 and EPA’s proposal to change the 

agency’s conventional vehicle test procedures. It is the authors’ hope that the issues raised in this 

paper help stimulate the necessary discussion and contribute to adoption of consensus reporting 

metrics. As discussed in this paper, for the resulting metrics to be useful, stakeholders must be able to 

translate the numbers into sound predictions of relative vehicle energy cost, petroleum use, and 

potential carbon dioxide (CO2) production. 

Acknowledgement 

The authors would like to acknowledge the programmatic support of the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program. 

30
List of symbols and acronyms 

AER – all-electric range BEV – battery electric vehicle 

CARB – California Air Resources Board CD – charge depleting 

CO2 – carbon dioxide CS – charge sustaining 

CV – conventional vehicle D – distance [miles] 

DOE – U.S. Department of Energy Echarge – electrical recharge energy 

Egasoline – gasoline energy content (33.44 kWh/gal) ESS – energy storage system 

EPA – U.S. Environmental Protection Agency EV – electric vehicle 

FCT – Full-Charge Test HEV – hybrid electric vehicle 

HWFET – Highway Fuel Economy Test i – mileage increment for driving statistics 

mpgX – mile-per-gallon rating in mode X OVC – off-vehicle charge 

NPTS – National Personal Transportation Survey PCT – Partial-Charge Test 

PHEV – plug-in hybrid electric vehicle Pi – probability i miles driven in a day 

SAE – Society of Automotive Engineers SOC – state of charge (of the ESS) 

UDDS – Urban Dynamometer Driving Schedule UF – Utility Factor 

Vfuel – fuel volume consumed [gallons] VMT – vehicle miles traveled 

References 

[1] Markel, T., O’Keefe, M., Simpson, A., Gonder, J., and Brooker, A. Plug-in HEVs: A Near-term Option to 

Reduce Petroleum Consumption, Milestone Report, National Renewable Energy Laboratory, 2005. 

[2] California Air Resources Board, California Exhaust Emission Standards and Test Procedures for 2005 and 

Subsequent Model Zero-Emission Vehicles, and 2002 and Subsequent Model Hybrid Electric Vehicles, in the 

Passenger Car, Light-Duty Truck and Medium-Duty Vehicle Classes, California Environmental Protection 

Agency, 2003. 

[3] Society of Automotive Engineers Surface Vehicle Recommended Practice, SAE J1711 – Recommended 

Practice for Measuring Fuel Economy of Hybrid-Electric Vehicles, Society of Automotive Engineers 

Publication, Issued March 1999. 

[4] Environmental Protection Agency, Fuel Economy Labeling of Motor Vehicles: Revisions to Improve 

Calculation of Fuel Economy Estimates, 40 CFR Parts 86, 600, EPA: Notice of Proposed Rulemaking, EPA-HQOAR-2005-0169. 

Authors 

Jeffrey Gonder, Research Engineer, National Renewable Energy Laboratory (NREL), 1617 Cole 

Blvd; Golden, CO 80401 USA; Tel: 303-275-4462; Fax: 303-275-4415; jeff_gonder@nrel.gov. 

Jeff joined the Advanced Vehicle Systems Group at NREL in 2005. His research includes systems 

analysis of plug-in hybrid electric vehicles and novel hybrid control strategies. Jeff holds a 

Master’s degree in Mechanical Engineering from The Pennsylvania State University and a 

Bachelor’s degree in the same subject from the University of Colorado. Prior to joining NREL, Jeff 

developed fuel cell systems and vehicles at Anuvu Inc. in Sacramento, CA. In graduate school, Jeff researched 

direct methanol fuel cells and helped lead the Penn State FutureTruck hybrid vehicle competition team. 

Andrew Simpson, Vehicle Systems Engineer, National Renewable Energy Laboratory (NREL), 

1617 Cole Blvd, Golden CO 80401 USA; Tel: 303-275-4430; Fax: 303-275-4415; 

andrew_simpson@nrel.gov. Andrew joined the Advanced Vehicle Systems Group at NREL in 

2005 and his current focus is plug-in hybrid-electric vehicles. He holds a Bachelor of Mechanical 

Engineering (2000) and Ph.D. in Electrical Engineering (2005) from the University of Queensland, 

Brisbane, Australia. Prior to NREL, Andrew worked as a CFD consultant for Maunsell Australia. 

He co-founded the Sustainable Energy Research Group at The University of Queensland and was a coordinating 

member of the University’s “SunShark” solar racing team which competed successfully from 1996-2000. 

31
Section 2 

Plug-In Hybrid Electric Vehicle Cost and Consumption Benefit Analysis 

“Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options,” a report published by the 

Electric Power Research Institute in July 2001, was a unique study that stands as a comprehensive 

analysis of hybrid electric vehicle and PHEV technology implementation. NREL contributed vehicle 

system simulation results to this report. The analysis scope was limited to just a few vehicle scenarios, 

including conventional, HEV0, PHEV20, and PHEV60. These vehicles were designed to achieve allelectric operation on the urban cycle for the specified distance. 

NREL recently developed a rapid design exploration methodology and applied the methodology to an 

expanded PHEV analysis spectrum that includes PHEVs with a wide range of power and energy

capabilities. In particular, the scope included PHEVs with limited all-electric capabilities that are still able

to realize tangible petroleum displacement by operating in a “blended” charge-depleting mode. 

Key conclusions from the analyses are:

• The PHEVx definition should be based on the energy equivalent all-electric range of the energy

storage system, rather than on actual all-electric range (the distance before first engine turn-on 

event). The current all-electric range focused definition constrains the PHEV design space and is 

not necessarily directly related to petroleum displacement. 

• The expected petroleum reduction of a PHEV is substantial, but the incremental costs may present a 

barrier to broad market penetration. A PHEV20 would likely reduce petroleum consumption by 50% 

but cost $8,000 more than a conventional vehicle. The PHEV40 would cost $11,000 more than a 

conventional vehicle and may reduce petroleum consumption by 62%. 

• Data in open literature support an inverse correlation between battery cycle life and cycle depth 

of discharge. To provide equivalent cycle life performance, the usable state-of-charge window of 

a short-range PHEV must be significantly less than that of a long-range PHEV. For example, for 

a 15-year life, a PHEV10 can only use 41% of the capacity, while a PHEV60 may use up to 73% 

and still achieve battery life targets. 

• If PHEVs are to provide a payback relative to a hybrid electric vehicle within 10 years, based on 

fuel cost savings and purchase cost alone, battery costs must reach long-term, high-volume cost 

estimates (<$300/kWh), and gasoline costs must increase to more than $4/gal. In the absence of

both lower battery costs and higher gas prices, alternative value propositions (e.g., government 

incentives, vehicle-to-grid revenue, battery leases, the value of a “green” image, avoided trips to 

the gas station, and the feel of electric operation) must be considered to overcome the cost 

premium of PHEVs. 

The analysis thus far has not allowed vehicle platform engineering as a strategy to reduce costs and 

improve fuel economy. Aerodynamics and vehicle light-weighting will likely have a more pronounced

impact on PHEVs than any other configuration. Future analysis will focus on vehicle platform

enhancements and their impact on the relative costs and benefits of PHEVs. In addition, design options and 

alternative business models will be explored to address the high cost of batteries for PHEVs.

For more extensive discussion of this topic, please refer to sections 2.1, 2.2, 2.3, 2.4, and 2.5. 

 32
Section 2.1 

Title: “Cost/Benefit Analysis of Hybrid-Electric and Plug-In Hybrid-Electric Vehicle Technology”

Type: Presentation 

Authors: Andrew Simpson 

Date: March 1, 2006 (updated May 2006) 

Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team

Abstract: Explores the spectrum of PHEV design space with respect to battery options and quantifies the 

most cost-effective scenarios 

 33
Cost / Benefit Analysis of Hybrid-Electric and

Plug-In Hybrid-Electric Vehicle Technology

A presentation to the FreedomCAR Vehicle Systems Analysis Technical Team by

Andrew Simpson

National Renewable Energy Laboratory

Presented Wednesday, 1st March 2006

REVISED May 2006

With support from the

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

FreedomCAR and Vehicle Technologies Program

2

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy

(the “DOE”). The United States Government (the “Government”) retains

and the publisher, by accepting the work for publication, acknowledges

that the Government retains a non-exclusive, paid-up, irrevocable,

worldwide license to publish or reproduce the published form of this work

or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof,

nor any of their employees, makes any warranty, express or implied, or

assumes any liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights.

Reference herein to any specific commercial product, process, or service

by trade name, trademark, manufacturer, or otherwise does not constitute

or imply its endorsement, recommendation, or favoring by the

Government or any agency thereof. The views and opinions of the

authors and/or presenters expressed herein do not necessarily state or

reflect those of MRI, the DOE, the Government, or any agency thereof.

34
3

The Perfect Storm

• Petroleum consumption has steadily

increased while domestic production has

continued to decline

• World oil production predicted to peak

within the next 5-15 years

• Recent increase in gasoline price is

indicator of growing tension between 

supply and demand

WHAT’S OUR PLAN?

0

5

10

15

20

25

1940 1950 1960 1970 1980 1990 2000 2010

Petroleum (mmb/day)

Domestic Production

Domestic Consumption

Source: U.S. Department of Energy, Energy Information Administration

Gasoline price - 200% rise in 8 years!

Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews

$-

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

1990 1993 1995 1998 2001 2004 2006

Weekly National Average Gasoline Price ($/gallon)

Source: US Dept of Energy, Energy Information Administration

4

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

0

2

4

6

8

10

12

14

16

2000 2005 2010 2015 2020 2025 2030

Year

Oil Consumption (MPBD)

2005 AEO Base Case

HEV Scenario

Oil Use Reduction with HEVs

3 MBPD Oil use same as today!

This highly aggressive scenario assumes 100% HEV sales from 2010 onwards...

Produced using VISION model, MBPD = million barrels per day

35
5

Key Messages

1. There is a very broad spectrum of HEV-PHEV

designs.

2. Reduction in per-vehicle fuel consumption via 

HEVs is limited, whereas PHEVs can reduce 

per-vehicle fuel use much further.

3. PHEVs have higher powertrain costs than 

HEVs, but have lower “fuel” costs than HEVs.

4. PHEVs are the most-cost-effective choice in a 

scenario of projected (low) battery costs and

high fuel costs.

6

Presentation Outline

• Study Objectives

• Potential petroleum reduction from PHEVs

• Simulation of PHEV efficiency and cost

— Baseline vehicle assumptions

— Components models (cost, mass, volume, efficiency)

— Control strategy

• Results

— Component sizing

— Fuel Economy

— Incremental cost

— Payback scenarios

• Conclusions & Next Steps

36
7

Study Objective

Question:

Under what circumstances do PHEVs

“make sense”?

Subject to:

— battery energy / PHEV range

— battery power / degree of hybridization (DOH)

— control strategy including SOC/DOD window

— battery chemistry

— fuel prices

— driving habits

8

Potential Gasoline Savings from Plug-In HEVs

WHAT ARE THE

RELATIVE COSTS?

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 20% 40% 60% 80% 100%

Reduction in Charge-Sustaining Mode Petroleum

Consumption (%)

Reduction in Total Petroleu

m

Consumption (%)

Challenging for

HEV technology

Prius (Corolla)

Civic

Accord

Highlander

Escape

Vue

PHEV20

+40% cs mpg

HEV 

+100% cs mpg

HEV

PHEV20

PHEV40

PHEV60

PHEV40

+0% cs mpg

3 ways to reduce petroleum consumption by 50%

Reduction in charge-sustaining (cs) mode petroleum

consumption (%)

37
9

PHEV Battery Options ESS Technology Comparison - P/E Ratios

SAFT VLE 45

SAFT VLM 41

SAFT VLP 7

1

2

5

10

Avestor SE 48S63

SAFT VLM 27

SAFT VLP 20

SAFT VLP 30

Cyclon D

Cyclon E

Varta Li 60Ah

Varta Li 6Ah

Prius PEVE SAFT VLE module

SAFT VL41M module

SAFT VL30P module

Cobasys 1000

Cobasys 4500

Cobasys 9500

1000 500 200 100 50 20

SAPHION U1-12FN40

SAPHION U24-12FN100

SAPHION U27-FN130

Optima D51

Optima D34

Optima D35

PEVE 7.5Ah

Kokam SLPB30205130

Kokam SLPB41205130

Kokam SLPB55205130

Kokam SLPB78216216

Kokam SLPB100216216

Kokam SLPB60460330

Kokam SLPB80460330

SAFT NiMH 12

SAFT VHF 10S SAFT VHF 20S

SAFT VHF 30S

SAFT STM 5-100 MR

SAFT STM 5-140 MR

Sprinter Varta

Varta Ni HP

Varta Ni UHP

Prius+ SAPHION

Sprinter SAFT

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 30 60 90 120 150

Available specific energy (Wh/kg)

Specific power (W/kg)

PHEV

design space

Expanded

10

Approach

• Power-flow simulation in 

spreadsheet

• Mass-compounding produced via 

circular referencing

• Vehicle configurations:

— conventional automatic

Simulation of PHEV Efficiency and Cost

— pre-transmission parallel hybrid: HEV or PHEV

• Calculates component sizes and costs including 74% retail markup

• Measures fuel/electricity consumption using NREL-proposed 

revisions to SAE J1711 (uses national average driving statistics)

• 2 powertrain technology scenarios – near term and long term

• Battery definition is key input to the simulation

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

0 10 20 30 40 50 60 70 80

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

engine

motor

SOC

38
11

Powertrain Technology Scenarios

Efficiency 35% peak efficiency curve, see slide 13 Same

Cost From EPRI study, see slide 16 Same

Engine Near-Term Scenario Long-Term Scenario

Mass Based on MY2003 production engines, see slide 15 Same

SOC window Based on JCI data for NiMH, see slide 21 Assumes Li-Ion achieves same cycle life as NiMH

Efficiency Equivalent circuit model based on P/E ratio Same

Tray + straps + thermal management = 0.06 kg/kg Same

Harness + bus bars = 0.14 kg/kW (Delucchi)

Pack mass

Module mass NiMH battery design function (Delucchi), see slide 19 Li-Ion battery design function (Delucchi), see slide 19

Pack cost $ = ($/kWh + 13) x kWh + 680 (from EPRI study) Same

Module cost Double EPRI projections, see slide 20 EPRI projections, see slide 20

Chemistry NiMH Li-Ion

Battery Near-Term Scenario Long-Term Scenario

Efficiency 95% peak efficiency curve, see slide 14 Same

Mass DOE 2006 current status, see slide 15 Based on GM Precept motor drive, see slide 15

Motor Near-Term Scenario Long-Term Scenario

Cost From EPRI study, see slide 16 From EPRI study, see slide 16

12

Baseline Vehicle Characteristics

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

Engine power 121 kW

Vehicle attributes

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Top speed 110 mph

Passing acceleration 40-60 mph in 5.3 s

Standing acceleration 0-60 mph in 8.0 s

Performance Parameters

Accessory load 700 W elec. or 823 W mech.

Rolling resistance coefficient 0.009

2.27m2 Frontal area

Drag coefficient 0.30

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Test Mass 1565 kg (136 kg load)

Curb Mass 1429 kg

Glider Mass 905 kg

Platform Parameters

MIDSIZE SEDAN (AUTOMATIC) – MSRP $23,400

39
13

Engine Map

Based on 1.9L, 95kW gasoline engine

Engine Efficiency Map - 4 Cylinder

0%

5%

10%

15%

20%

25%

30%

35%

40%

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Normalised output power (-)

Efficiency (%)

14

Motor/Controller Map

Based on 50kW permanent magnet motor/controller

Motor/Controller Efficiency Map

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Normalised output power (-)

Efficiency (

%)

drive

regen

40
15

Engine / Motor Masses

0

50

100

150

200

250

300

0 20 40 60 80 100 120 140

Power (kW peak)

Mass (kg)

Engine

Motor/Cont. (near-term)

Motor/Cont. (long-term)

16

Engine / Motor Costs

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

0 20 40 60 80 100 120 140

Power (kW peak)

Manufactured Cost ($)

Engine

Motor/Cont. (near-term)

Motor/Cont. (long-term)

41
17

Simulation Validation

Unadjusted Fuel Economy

34.9 mpg

35.2 mpg

Highway

Simulated 22.3 mpg 26.6 mpg

vehicle

Benchmark 22.2 mpg 26.6 mpg

vehicle

Urban Composite

Conventional Automatic Midsize Sedan

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

0 1 2 3 4 5 6 7 8

< 1% error

18

Battery Definition as Key Input to Simulation

kWh/mi

(from simulation)

SOC window

PHEV range

P/E ratio

Performance 

constraints

kWh usable

kWh total

kWmotor

kWengine

DOH

Benefit of 

plugging-in

Benefit of 

hybridization

Total MPG Benefit

mass compounding

Input parameters that define the battery in BLUE

DOH = degree of hybridization

42
19

Battery Design Functions

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 20 40 60 80 100 120 140 160 180 200

Specific Energy (Wh/kg)

Specific Power (W/kg)

NiMH (near-term scenario)

LI-ION (long-term scenario)

Battery Model based on P/E Ratio

P/E = 2

P/E = 30

20

Battery Model based on P/E Ratio

Battery Module Cost Functions

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

Power-to-Energy (P/E) Ratio

Module Cost ($/kWh)

Near Term

Long Term

43
21

Battery SOC Operating Window vs. Specified All-Electric Range

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / All-Electric Range

SOC widow

Daily mileage distribution

Battery Model (cont.) – SOC Window

SOC operating window

22

HEV/PHEV Control Strategy

• Engine power request adjusted w.r.t. battery SOC

— Pengine = Pdriveline – k (SOC – SOCtarget)

• Engine turn-on/off strategy:

— engine turn-on occurs when

» SOC-adjusted engine power request > 0

— idle-off allowed when

» SOC-adjusted engine power request <= 0

» AND vehicle is stationary

» AND engine has been on for at least 5 minutes

• Motor provides supplemental power and does 60% of braking 

regeneratively (net recapture subject to component efficiencies)

• All component power limits are enforced

• Electric accessories

Idle-off for extended periods

allows some PHEVs to avoid

significant fuel-use penalty

during charge-depleting mode.

To enable this, modifications to

emissions control system

would be required e.g. 

vacuum-insulated catalyst...

However, all vehicles 

(conventional and hybrid) can 

benefit from these systems

44
23

Results

24

1

10

100

0% 10% 20% 30% 40% 50% 60%

Degree of Hybridization (%)

Battery Power/Energy Ratio

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

PHEV Design Space

Maximum engine downsizing

Increasing battery energy

Increasing battery power

Near-term cost scenario

NIMH BATTERIES

45
25

Results – Fuel Consumption Reduction

Maximum engine downsizing

Increasing battery energy

Increasing battery power

Near-term cost scenario

NIMH BATTERIES

Reduction in Fuel Consumption vs Degree of Hybridization

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Degree of Hybridization (DOH)

Reduction in Fuel Consumption (%)

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

26

Results – Powertrain Cost Increments

Maximum engine downsizing

Near-term cost scenario

NIMH BATTERIES

Cost Increment relative to Conventional Vehicle (Midsize Sedan)

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Degree of Hybridization (DOH)

Cost increment

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

46
27

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Reduction in Fuel Consumption (%)

Powertrain Cost Increment

Increasing DOH

Maximum engine

downsizing

HEV0

Near-term cost scenario

NIMH BATTERIES

(based on NREL-revised SAE J1711)

NOTE: maximum engine downsizing is a 

result of performance constraints – less 

stringent constraints move the knee of the 

curve to the right and improve the viability 

of higher-DOH designs

28

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Reduction in Fuel Consumption (%)

Powertrain Cost Increment

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

564 gal, $1692 per year @ $3.00/gal.

Near-term cost scenario

NIMH BATTERIES

(based on NREL-revised SAE J1711)

47
29

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Reduction in Fuel Consumption (%)

Powertrain Cost Increment

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

564 gal, $1692 per year @ $3.00/gal.

Long-term cost scenario

LI-ION BATTERIES

(based on NREL-revised SAE J1711)

30

Optimum PHEV Designs – Near & Long Term Scenarios

Near-term: NIMH BATTERIES

Vehicle Curb Mass Engine Motor DOH Battery P/E SOC MPG Wh/mi Retail Cost

(kg) (kW) (kW) (kWh) (1/h) Increment

CV 1429 121.7 --- --- --- --- --- 26.6 0 $ -

HEV0 1453 78.3 41.0 34% 1.5 30.0 37% 35.1 0 $ 5,199

PHEV2 1453 78.3 41.0 34% 1.5 30.0 37% 36.3 11 $ 5,863

PHEV5 1493 79.7 39.3 33% 3.5 12.0 39% 37.8 28 $ 7,326

PHEV10 1566 81.9 45.0 35% 6.9 7.0 41% 40.6 54 $ 10,389

PHEV20 1666 85.0 47.0 36% 12.6 4.0 47% 47.6 101 $ 14,980

PHEV30 1747 88.2 48.5 35% 17.3 3.0 53% 55.6 138 $ 18,754

PHEV40 1820 89.9 54.2 38% 21.1 2.8 59% 64.2 171 $ 22,129

PHEV50 1875 91.6 56.6 38% 24.2 2.5 66% 74.6 197 $ 24,737

PHEV60 1913 92.8 56.2 38% 26.6 2.3 73% 84.1 218 $ 26,617

Long-term: LI-ION BATTERIES

Vehicle Curb Mass Engine Motor DOH Battery P/E SOC MPG Wh/mi Retail Cost

(kg) (kW) (kW) (kWh) (1/h) Increment

CV 1429 121.7 --- --- --- --- --- 26.6 0 $ -

HEV0 1416 77.2 40.4 34% 1.4 30.0 37% 35.5 0 $ 3,261

PHEV2 1416 77.2 40.4 34% 1.4 30.0 37% 36.7 11 $ 3,925

PHEV5 1438 77.8 38.4 33% 3.4 12.0 39% 38.4 27 $ 4,623

PHEV10 1478 79.1 43.3 35% 6.6 7.0 41% 41.7 52 $ 6,147

PHEV20 1528 80.7 44.5 36% 11.9 4.0 47% 49.6 95 $ 8,314

PHEV30 1563 81.8 45.0 36% 16.1 3.0 53% 58.9 129 $ 10,002

PHEV40 1588 82.6 44.8 35% 19.2 2.5 59% 68.6 157 $ 11,236

PHEV50 1610 83.3 45.8 35% 21.8 2.3 66% 80.4 180 $ 12,311

PHEV60 1637 84.1 50.4 37% 23.9 2.3 73% 90.2 198 $ 13,406

48
31

Vehicle Mileage Schedule from NHTSA (2006)

0

2000

4000

6000

8000

10000

12000

14000

16000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Year after purchase

Annual VMT

0

20

40

60

80

100

120

140

160

180

Thousands

Cumulative VMT

Annual VMT

Cumulative VMT

Annual Vehicle Mileage Schedule

used in following payback scenarios

150,000 miles after 15 years

32

Cumulative Per-Vehicle Fuel Use

0

1000

2000

3000

4000

5000

6000

0 2 4 6 8 10 12 14

Time (years)

Gasoline Fuel Used (gallons)

CV

HEV0

PHEV10

PHEV20

PHEV40

PHEV60

Fuel Use Reduction from HEVs and PHEVs

138 bbl

44 bbl

Near-term cost scenario

NIMH BATTERIES

49
33

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 2 4 6 8 10 12 14

Years after purchase

Cumulative Cost

CV

HEV0

PHEV10

PHEV20

PHEV40

PHEV60

Cost Reduction from HEVs and PHEVs

$3.00 / gal. (today)

Near-term cost scenario

NIMH BATTERIES

$0.09¢/kWh (2005 average)

34

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 2 4 6 8 10 12 14

Years after purchase

Cumulative Cost

CV

HEV0

PHEV10

PHEV20

PHEV40

PHEV60

Cost Reduction from HEVs and PHEVs (cont.)

$3.00 / gal. (today)

$0.09¢/kWh (2005 average)

Long-term cost scenario

LI-ION BATTERIES

50
35

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 2 4 6 8 10 12 14

Years after purchase

Cumulative Cost

CV

HEV0

PHEV10

PHEV20

PHEV40

PHEV60

Cost Reduction from HEVs and PHEVs (cont.)

$4.00 / gal. (tomorrow?)

Long-term cost scenario

LI-ION BATTERIES

$0.09¢/kWh (2005 average)

36

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 2 4 6 8 10 12 14

Years after purchase

Cumulative Cost

CV

HEV0

PHEV10

PHEV20

PHEV40

PHEV60

Cost Reduction from HEVs and PHEVs (cont.)

$5.00 / gal. (day after tomorrow??)

Long-term cost scenario

LI-ION BATTERIES

$0.09¢/kWh (2005 average)

51
37

Cumulative Incremental Vehicle plus Energy (Fuel/Elec.) Costs

$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

0 2 4 6 8 10 12 14

Years after purchase

Cumulative Incremental Cost

HEV0

PHEV40

HEV/PHEV Payback is Sensitive to Annual Mileage

Long-term cost scenario

LI-ION BATTERIES

$4.00 / gal. $0.09¢/kWh

15kmi per year NHTSA mileage schedule

~10kmi per year average

5000mi per year difference varies 

PHEV40 payback by 4 years

relative to conventional vehicle

38

Vehicle Costs cont.

Why might PHEV buyers pay more?

1. Tax incentives

2. Reduced petroleum use, air pollution and CO2

3. National energy security

4. Less maintenance

5. Reduced fill-ups

6. Convenience of home recharging (off-peak)

7. Improved acceleration (high torque of electric motors)

8. Green image, “feel-good factor”

9. Vehicle-to-grid (V2G)

52
39

Conclusions

1. There is a very broad spectrum of HEV-PHEV designs.

2. Key factors in the HEV/PHEV cost-benefit equation include:

• Battery costs

• Fuel costs

• Control strategy (battery SOC window and emissions considerations)

• Driving habits (annual VMT and trip-length distribution)

3. Based on the midsize vehicle platform, performance constraints, 

component technologies and control strategies used in this study:

• HEVs can reduce per-vehicle fuel use by approx. 25%. Note this study did not

consider benefits from platform engineering (i.e. mass/drag reduction).

• PHEVs can reduce per-vehicle fuel use by up to 45% for PHEV20s, up to 60% 

for PHEV40s and up to 70% for PHEV60s.

• In the long term, powertrain cost increments are predicted to be $2-5k for 

HEVs, $7-11k for PHEV20s, $10-14k for PHEV40s and $13-17k for PHEV60s

assuming that projected component (battery) costs can be achieved.

40

Conclusions (cont.)

4. Based on combined powertrain and energy costs:

• At today’s fuel and powertrain component costs, conventional 

vehicles are the most cost-competitive.

• HEVs become the most cost-competitive EITHER if fuel prices

increase OR projected battery costs are achieved.

• PHEVs become cost-competitive ONLY if projected battery

costs are achieved AND fuel prices increase.

• Since high-range PHEVs reduce petroleum consumption the 

most, they might be good candidates for subsidies or taxincentives to improve their cost-competitiveness and help 

realize national security goals.

• Alternative business models (e.g. battery lease) could 

significantly improve the market for PHEVs

53
41

Next Steps

• Expand the HEV-PHEV analysis space to include:

— Platform engineering (mass/drag reduction)

— Different performance constraints / component sizes

• Detailed simulation of promising PHEV designs:

— Real world driving patterns (e.g. St Louis data)

— Control strategy optimization

• Optimization of PHEV competitiveness using Technical 

Targets Tool

— Various gas price and battery cost scenarios

54
Section 2.2 

Title: “Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology”

Type: Paper

Authors: Andrew Simpson 

Date: October 2006 

Conference or Meeting: Published at the 22nd International Battery, Hybrid, and Fuel Cell Electric 

Vehicle Symposium and Exposition 

Abstract: Explores the spectrum of PHEV design space with respect to battery options and quantifies the 

most cost-effective scenarios 

 55
COST-BENEFIT ANALYSIS OF PLUG-IN HYBRID 

ELECTRIC VEHICLE TECHNOLOGY1

ANDREW SIMPSON 

National Renewable Energy Laboratory

Abstract 

Plug-in hybrid-electric vehicles (PHEVs) have emerged as a promising technology that uses electricity 

to displace petroleum consumption in the vehicle fleet. However, there is a very broad spectrum of 

PHEV designs with greatly-varying costs and benefits. In particular, battery costs, fuel costs, vehicle 

performance attributes and driving habits greatly-influence the relative value of PHEVs. This paper 

presents a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced 

petroleum consumption) of PHEVs relative to hybrid-electric and conventional vehicles. A detailed 

simulation model is used to predict petroleum reductions and costs of PHEV designs compared to a 

baseline midsize sedan. Two powertrain technology scenarios are considered to explore the near-term 

and long-term prospects of PHEVs. The analysis finds that petroleum reductions exceeding 45% pervehicle can be achieved by PHEVs equipped with 20 mi (32 km) or more of energy storage. However, 

the long-term incremental costs of these vehicles are projected to exceed US$8,000, with near-term 

costs being significantly higher. A simple economic analysis is used to show that high petroleum 

prices and low battery costs are needed to make a compelling business case for PHEVs in the absence 

of other incentives. However, the large petroleum reduction potential of PHEVs provides strong 

justification for governmental support to accelerate the deployment of PHEV technology. 

Keywords: Plug-in Hybrid; Hybrid-Electric Vehicles; Battery, Secondary Battery; Modeling, 

Simulation; Energy Security. 

1 Introduction to Plug-In Hybrid-Electric Vehicles 

Plug-in hybrid-electric vehicles have recently emerged as a promising alternative that uses electricity 

to displace a significant fraction of fleet petroleum consumption [1]. A plug-in hybrid-electric vehicle 

(PHEV) is a hybrid-electric vehicle (HEV) with the ability to recharge its electrochemical energy 

storage with electricity from an off-board source (such as the electric utility grid). The vehicle can 

then drive in a charge-depleting (CD) mode that reduces the system’s state-of-charge (SOC), thereby 

using electricity to displace liquid fuel that would otherwise have been consumed. This liquid fuel is 

typically petroleum (gasoline or diesel), although PHEVs can also use alternatives such as biofuels or 

hydrogen. PHEV batteries typically have larger capacity than those in HEVs so as to increase the 

potential for petroleum displacement. 

1.1 Plug-In Hybrid-Electric Vehicle Terminology 

Plug-in hybrid-electric vehicles are characterized by a “PHEVx” notation, where “x” typically denotes 

the vehicle’s all-electric range (AER) – defined as the distance in miles that a fully charged PHEV can 

drive before needing to operate its engine. The California Air Resources Board (CARB) uses the 

standard Urban Dynamometer Driving Schedule (UDDS) to measure the AER of PHEVs and provide 

a fair comparison between vehicles [2]. By this definition, a PHEV20 can drive 20 mi (32 km) allelectrically on the test cycle before the first engine turn-on. However, this all-electric definition fails 

1

 This work has been authored by an employee or employees of the Midwest Research Institute under Contract 

No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government retains and the 

publisher, by accepting the article for publication, acknowledges that the United States Government retains a 

non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, 

or allow others to do so, for United States Government purposes. 

56
to account for PHEVs that might continue to operate in CD-mode after the first engine turn-on. 

Therefore, the author uses a definition of PHEVx that is more appropriately related to petroleum 

displacement. By this definition, a PHEV20 contains enough useable energy storage in its battery to 

displace 20 mi (32 km) of petroleum consumption on the standard test cycle. Note that this definition 

does not imply all-electric capability since the vehicle operation will ultimately be determined by 

component power ratings and their control strategy, as well as the actual in-use driving cycle. 

1.2 The Potential of Plug-In Hybrid-Electric Vehicles 

The potential for PHEVs to displace fleet petroleum consumption derives from several factors. First, 

PHEVs are potentially well-matched to motorists’ driving habits – in particular, the distribution of 

distances traveled each day. Based on prototypes from the last decade, PHEVs typically fall in the 

PHEV10-60 range [3]. Figure 1 shows the US vehicle daily mileage distribution based on data 

collected in the 1995 National Personal Transportation Survey (NPTS) [4]. Clearly, the majority of 

daily mileages are relatively short, with 50% of days being less than 30 mi (48 km). Figure 1 also 

shows the Utility Factor (UF) 

curve for the 1995 NPTS data. Daily Mileage Distribution and Utility Factor Curve 

For a certain distance D, the 100 

Utility Factor is the fraction of 90 

total vehicle-miles-traveled 

(VMT) that occurs within the first 80 

D miles of daily travel. For a 70 

equivalent to 40% of VMT, 30 

(assuming the vehicle is fully 20 

recharged each day). Similarly, 

an all-electric PHEV60 can 10 

displace about 60%. This low- 0 

Daily mileage distribution 

Utility Factor curve 

0 20 40 60 80 100 daily-mileage characteristic is Daily Mileage (mi) 

why PHEVs have potential to Figure 1: Daily mileage distribution for US motorists based on displace a large fraction of per- the 1995 National Personal Transportation Survey vehicle petroleum consumption. 

However, for PHEVs to displace fleet petroleum consumption, they must penetrate the market and 

extrapolate these savings to the fleet level. A second factor that is encouraging for PHEVs is the 

success of HEVs in the market. Global hybrid vehicle production is currently several hundred 

thousand units per annum [5]. Because of this, electric machines and high-power storage batteries are 

rapidly approaching maturity with major improvements in performance and cost having been achieved. 

Although HEV components are not optimized for PHEV applications, they do provide a platform from 

which HEV component suppliers can develop a range of PHEV components. 

Finally, PHEVs are very marketable in that they combine the beneficial attributes of HEVs and battery 

electric vehicles (BEVs) while mitigating their disadvantages. Production HEVs achieve high fuel 

economy, but they are still designed for petroleum fuels and do not enable fuel substitution/flexibility. 

PHEVs, however, are true fuel-flexible vehicles that can run on petroleum or electrical energy. BEVs 

do not require any petroleum, but are constrained by battery technologies resulting in limited driving 

ranges, significant battery costs and lengthy recharging times. PHEVs have a smaller battery which 

mitigates battery cost and recharging time while the onboard petroleum fuel tank provides driving 

range equivalent to conventional and hybrid vehicles. This combination of attributes is building a 

strong demand for PHEVs, as evidenced by the recently launched Plug-In Partners Campaign [6]. 

distance of 30 mi (48 km), the 

utility factor is approximately 

Probability (%) 

60 

40%. This means that an all- 50 

electric PHEV30 can displace 

petroleum consumption 

40 

57
2 

PHEVs have the potential to come to market, penetrate the fleet, and achieve meaningful petroleum 

displacement relatively quickly. Few competing technologies offer this potential combined rate and 

timing of reduction in fleet petroleum consumption [7]. However, PHEV technology is not without its 

challenges. Energy storage system cost, volume, and life are major obstacles that must be overcome 

for these vehicles to succeed. Increasing the battery storage beyond that of HEVs increases vehicle 

cost and presents significant packaging challenges. Furthermore, the combined deep/shallow cycling 

in PHEV batteries is uniquely more demanding than that experienced by HEVs or BEVs. PHEV 

batteries may need to be oversized to last the life of the vehicle, further increasing cost. Given that 

HEVs are succeeding in the market, the question relevant to PHEVs is, “What incremental petroleum 

reductions can be achieved at what incremental costs?” These factors will critically affect the 

marketability of PHEVs through their purchase price and cost-of-ownership. This paper presents the 

results of a study designed to evaluate this cost-benefit tradeoff. 

Modeling PHEV Petroleum Consumption and Cost 

The reduction of per-vehicle petroleum consumption in a PHEV results from two factors: 

1. Petroleum displacement during CD-mode, which as previously discussed relates to the PHEVx 

designation based on the added battery energy capacity of the vehicle. 

2. Fuel-efficiency improvement in charge-sustaining (CS) mode due to hybridization, which relates 

to the degree-of-hybridization (DOH) or added battery power capability of the vehicle. HEVs, 

which do not have a CD-mode, are only able to realize savings via this second factor. 

For a PHEVx, these two factors can be combined mathematically as follows: 

FC PHEVx CS = [1− ( ) FC UF x ] (1) FCCV FCCV

where FCPHEVx is the UF-weighted fuel consumption of the PHEVx, FCCV is the fuel consumption of 

the reference conventional (non-hybrid) vehicle and FCCS is the PHEVx’s CS-mode fuel consumption. 

Note that this expression becomes approximate for PHEVs without all-electric capability because use 

of the utility factor in this way assumes that no petroleum is consumed in the first x miles of travel. 

Figure 2 uses Equation 1 to compare the petroleum reduction of various PHEV designs. We see there 

are a variety of ways to achieve a target level of petroleum reduction. For example, a 50% reduction is 

achieved by an HEV with 50% reduced fuel consumption, a PHEV20 with 30% CS-mode reduction 

and by a PHEV40 with 0% CS-mode reduction (this last example is unlikely since PHEVs will show 

CS-mode improvement due to hybridization, notwithstanding the increase in vehicle mass from the 

larger battery). To demonstrate the 

feasible range of CS-mode reduction, Potential Reduction of Petroleum Consumption in PHEVs 

Figure 2 compares several 

contemporary HEVs to their 100% 

conventional counterparts (in the 90% 

case of the Toyota Prius, a 80% 

comparison is made to the Toyota 70% 

Corolla which has similar size and 60% 

performance). At the low end of the 50% 

spectrum, the “mild” HEV Saturn 40% 

Vue achieves a modest reduction of 30% 

less than 20%. The “full” HEV 20% 

Toyota Prius achieves the highest 10% 

percentage reduction (40%) of all 0% 

HEVs currently on the market 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

although, in addition to the platform Reduction in CS-mode Petroleum Consumption (%) 

enhancements employed in Figure 2: Potential per-vehicle reduction of petroleum 

production hybrids, it also uses an consumption in PHEVs 

Total Reduction in Petroleum Consumption (%)

Prius (Corolla) 

Civic 

Accord Highlander 

Escape 

Vue 

PHEV60 

PHEV40 

PHEV20 

HEV 

Challenging region 

for HEV technology 

58
advanced (Atkinson-cycle) engine technology. Note that none of the production HEVs achieve the 

50% reduction discussed in the above example, suggesting that there is an upper limit on the benefit of 

hybridization alone. Reductions exceeding 50% are available through CD-mode operation in a PHEV, 

although increasing PHEVx ranges can be seen to provide diminishing returns due to the nature of the 

Utility Factor curve (Figure 1). 

The PHEV design space in Figure 2 characterized by CS/CD-mode fuel consumption has a matching 

space characterized by battery power/energy. Improving CS-mode fuel consumption implies an 

increase in DOH and battery power, while increasing CD-mode benefit implies an increase in PHEVx 

and useable battery energy. Moving in either direction incurs additional vehicle costs. However, the 

link between battery specifications, CS/CD-mode reductions, and vehicle costs is not obvious and 

must be explored through detailed vehicle fuel consumption and cost modeling. Therefore, a model 

was developed to predict the petroleum reductions and costs of contrasting PHEV designs compared to 

a reference conventional vehicle. The details of this model are presented in the following sections. 

2.1 Modeling Approach and Scope of the Study 

The PHEV cost-benefit model includes several sub-models. First, a performance model calculates 

component sizes necessary to satisfy the performance constraints listed in Table 1. Second, a mass 

balance calculates the vehicle mass based on component sizes determined by the performance model. 

Third, an energy-use model simulates the vehicle’s gasoline and electricity consumption over various 

driving cycles. The vehicle performance and energy-use models are coupled to vehicle mass, so the 

model is able to capture mass compounding in the sizing of components. Fourth, a cost model 

estimates the vehicle retail price based on the component sizes. All costs are reported in 2006 US 

dollars. Finally, the results post-processing performs calculations to report the vehicle energy 

consumption and operating costs in meaningful ways. The model is implemented in an iterative 

Microsoft Excel spreadsheet. 

The energy-use model is a detailed, second-by-second, dynamic vehicle model that uses a reversecalculation approach [8]. It is also characterized as a power-flow model since it models component 

losses/efficiencies as functions of device power, rather than as functions of torque/speed or 

current/voltage as in more detailed models. This reverse-calculation, power-flow method provides 

rapid estimation of vehicle energy usage and enables the coupled, iterative spreadsheet described 

above. A solution is obtained in only a few seconds, meaning that the design space can be explored 

very quickly and thoroughly. Several hundred PHEV designs were therefore included in the study. 

The model performs simulations of both conventional vehicles (CVs) and HEVs (including PHEVs) so 

that side-by-side comparisons can be made. The performance and energy-use models were validated 

for a Toyota Camry sedan and Honda Civic Hybrid. In both cases, errors of less than 5% were 

observed in the estimates of vehicle performance and energy use. 

Two powertrain technology scenarios (Table 2) were included in the study. The near-term scenario 

(2005-2010) represents vehicles produced using current-status powertrain technologies, whereas the 

long-term scenario (2015-2020) allows for advanced technologies expected to result from ongoing 

R&D efforts and high-volume production levels. The long-term scenario does not, however, include 

advanced engine technologies since the author wanted to isolate the impact of improved electric drive 

and energy storage technologies on the relative cost-benefit of PHEVs. 

2.2 Vehicle Platform, Performance and Cost Assumptions 

All vehicles included in the study satisfied the same performance constraints and used a vehicle 

platform identical to the baseline CV. The baseline CV was a midsize sedan (similar to a Toyota 

Camry or Chevrolet Malibu) and relevant parameters are presented in Table 1. Most parameters were 

calculated from sales-weighted average data for the top selling US midsize sedans in 2003 [9]. Some 

parameters, such as rolling resistance, accessory loads, passing acceleration, and gradeability, were 

engineering estimates. The baseline manufacturer’s suggested retail price (MSRP) of US$23,392 was 

59
used in combination with the powertrain cost model to estimate the baseline “glider” cost (i.e. vehicle 

with no powertrain). The cost of a 121 kW CV powertrain was estimated at US$6,002, leading to an 

estimated baseline glider cost of US$17,390. 

Table 1: Vehicle Platform and Performance Assumptions for Midsize Sedan 

Platform Parameters 

Glider Mass 905 kg 

Curb Mass 1429 kg 

Test Mass 1565 kg (136 kg load) 

Gross Vehicle Mass (GVM) 1899 (470 kg load) 

Drag coefficient 0.3 

Frontal area 2.27m2 

Rolling resistance coefficient 0.009 

Baseline accessory load 800 W elec. (4000 W peak) 

Performance Parameters 

Standing acceleration 0-97 kph (0-60 mph) in 8.0 s 

Passing acceleration 64-97 kph (40-60 mph) in 5.3 s 

Top speed 177 kph (110 mph) 

Gradeability 6.5% at 88 kph (55 mph) at GVM 

with 2/3 fuel converter power 

Vehicle attributes 

Engine power 121 kW 

Fuel consumption 10.6 / 6.7 / 8.8 L per 100km 

(urban / highway / composite) 

MSRP $23,392 

Table 2: Powertrain Technology Scenarios for the Cost-Benefit Analysis 

Near-Term Scenario Long-Term Scenario 

Battery 

Chemistry NiMH Li-Ion 

Module cost Twice that of long-term scenario $/kWh = 11.1 x P/E + 211.1 [14] 

Pack cost $ = ($/kWh + 13) x kWh + 680 [14] Same 

Module mass NiMH battery design function [15], see Figure 6 Li-Ion battery design function [15], 

see Figure 6 

Pack mass Tray/straps + thermal mgmt = 0.06 kg/kg [15] 

Harness + bus bars = 0.14 kg/kW [15] Same 

Efficiency 

Equivalent circuit model based on P/E ratio, 

see Figure 5 Same 

SOC window SOC design window curve, see Figure 4 Same (assumes Li-Ion cycle life = NiMH) 

Motor 

Mass kg = 21.6 + 0.833 x kW [13] kg = 21.6 + 0.532 x kW [14] 

Cost $ = 21.7 x kW + 425 [14] $ = 16 x kW + 385 [14] 

Efficiency 95% peak efficiency curve, see Figure 5 Same 

Engine 

Mass kg =1.62 x kW + 41.8 [9] Same 

Cost $ = 14.5 x kW + 531 [14] Same 

Efficiency 34% peak efficiency curve, see Figure 5 Same 

2.3 Powertrain Architecture 

The two things that differentiate a PHEV 

from an HEV are the inclusion of a CD 

operating mode and a recharging plug. 

Therefore, a PHEV can be implemented 

using any of the typical HEV 

architectures (parallel, series, or powersplit). For this study, a parallel 

architecture was assumed with the ability 

to declutch the engine from the powertrain (Figure 3). This parallel layout provides greater flexibility 

in engine on/off control compared to Honda’s integrated motor assist (IMA) parallel system [10] 

ENGINE TRANS. 

BATTERY MOTOR 

Figure 3: Parallel HEV powertrain architecture 

60
0 10 20 30 40 50 6 

Daily Mileage / PHEVx 

Design SOC window 

based on PHEVx 

Average daily SOC 

swing based on daily 

mileage distribution 

Daily mileage probability distribution 

where the engine and motor are always connected. To create more flexibility in engine on/off control, 

it was also assumed that all accessories (including air conditioning) would be powered electrically 

from the battery. 

2.4 Component Sizing 

Battery 

The battery is the first component sized by the model and the two key inputs are the PHEVx 

designation and the battery power-to-energy (P/E) ratio. The useable battery energy is calculated 

using an estimate of the vehicle’s equivalent electrical energy consumption per unit distance 

multiplied by the target PHEVx distance. The electrical energy consumption is estimated using the 

PAMVEC model [11]. The total battery energy is then calculated based on the SOC design window. 

Finally, the rated battery power is calculated by multiplying the total battery energy by the input P/E 

ratio and then de-rating by 20% to account for battery power degradation at end-of-life. 

To achieve similar battery cycle life, different PHEVx ranges require different SOC design windows. 

The daily mileage distribution (Figure 1) means that a PHEV10 is far more likely to experience a deep 

cycle than a PHEV60. Therefore, 

the SOC design window must be 100% 

chosen such that the average daily 90% 

SOC swing is consistent across the 80% 

range of PHEVs. Figure 4 shows 70% 

the SOC design windows assumed 60% 

in the PHEV cost-benefit model, 50% 

based on cycle-life data presented 

by Rosenkrantz [12] and a target 

battery life of 15 years (assuming 30% 

40% 

one full recharge each day). Figure 20% 

4 also shows the resulting average 10% 

daily SOC swing which is 0% 

0 

consistent across the range. 

Electric Motor Figure 4: SOC design window for PHEVs 

The motor power is matched to the battery power, but with the resulting motor power being slightly 

smaller after accounting for electric accessory loads and motor/controller efficiency. 

Engine 

Several steps are required to size the engine. First, the required peak power of the engine plus motor is 

calculated using the PAMVEC model [11]. This power is typically dictated by the standing 

acceleration performance and for the baseline midsize platform is approximately 120kW. The motor 

power is then subtracted from the total to provide a requirement for the engine power. This produces 

some “engine downsizing,” but there are downsizing limits imposed by other performance constraints. 

Continuous performance events (gradeability and top speed) determine the minimum permissible 

engine size. Gradeability performance is limited to 2/3 of peak engine power due to engine thermal 

management and noise, vibration, and harshness (NVH) considerations. For the baseline midsize 

platform, the minimum engine size is approximately 80kW. 

2.5 Component Efficiencies, Masses, and Costs 

Engine and Electric Motor 

As discussed in section 2.1, the PHEV energy-use model is a reverse-calculation, power-flow model 

that simulates component losses/efficiencies as a function of output power. Both the engine and 

electric motor efficiencies are modeled using polynomial expressions for component input power as a 

function of output power. The engine curve is based on a 4-cylinder, 1.9L, 95kW gasoline engine. A 

3rd-order polynomial was fitted to data from an ADVISOR simulation [8] using this engine. The 

61
1800 

2000 

motor curve is based on a 50kW permanent magnet machine and a 9th-order polynomial was fitted to 

data from an ADVISOR simulation using this motor. Both efficiency curves are shown in Figure 5. 

The engine and motor masses and costs are modeled as linear functions of rated output power. The 

engine mass function is derived from a database of 2003 model-year vehicles [9]. The near-term 

motor-controller mass function 

is based on the 2006 current Powertrain Components - Normalised Efficiency Curves 

status listed in the FreedomCAR 100% 

and Vehicle Technologies 

Program Plan [13]. The long- 90% 

term motor-controller mass is 80% 

Engine 

Motor-drive 

Motor-regen 

Battery 

based on technology 70% 

demonstrated in the GM Precept 

concept vehicle [14]. The 

engine cost function is based on 

Efficiency (%)

60% 

50% 

40% 

manufacturers’ data provided to 30% 

the EPRI Hybrid-Electric 

Vehicle Working Group 20% 

(HEVWG) [14]. The near-term 10% 

and long-term motor cost 0% 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

functions are also based on data Normalised Power (P/Pmax) 

reported by EPRI [14]. 

Figure 5: Efficiency curves used in the PHEV cost-benefit model 

Battery 

Battery efficiency is modeled using a normalized function for efficiency vs. input power (Figure 5). 

This relationship was derived from an equivalent circuit model using realistic values for nominal opencircuit voltage and internal impedance. Battery-module mass for both NiMH and Li-Ion technology is 

modeled using battery design functions developed by Delucchi [15] and shown in Figure 6. The added 

mass of battery packaging and thermal management was also based on [15]. 

Battery-module-specific costs ($/kWh) vary as a function of power-to-energy ratio (Figure 6). The 

long-term Li-Ion cost curve is based on estimates from EPRI [14]. After speaking with battery 

suppliers and other experts, it was estimated that the near-term specific cost of NiMH modules was 

approximately double that of EPRI’s long-term prediction. The costs of battery packaging and thermal 

management are also based on those listed in [14]. 

Recharging Plug and Charger 

PHEVs are assumed to be equipped with an inverter-integrated plug/charger with 90% efficiency and 

an incremental manufactured cost of US$380 over the baseline inverter cost [14]. 

Battery Design Functions Battery Cost Functions 

5 20 10 1200

200

0

0 50 100 150 200 

Specific Energy (Wh/kg) 

NiMH (near-term scenario) 

LI-ION (long-term scenario) 

2 

5 

0 

0 5 10 15 20 25 30 

Power-to-Energy Ratio (1/h) 

Figure 6: Battery design functions and module cost curves assumed for NiMH and Li-Ion technology 

NiMH (near-term) 1000 

Module Specific Cost ($/kWh) 

Li-Ion (long-term) 

Specific Power (W/kg) 1400 

1200 

1000 

800 

600 

600 

400 

400 

200 

62

800 

1600 
Retail Markup Factors 

The component cost functions in Table 2 model the manufactured cost of components. To convert 

these to retail costs in a vehicle, various markup factors are applied. A manufacturer’s markup of 50% 

and dealer’s markup of 16.3% are assumed based on estimates by EPRI [14] 

2.6 Powertrain Control Strategy 

A generic control strategy was developed for the spectrum of PHEV designs. This control strategy 

consists of four basic elements. The basis of the strategy is an SOC-adjusted engine power request: 

Pengine−request = Pdriveline − k(SOC − SOCt arg et ) (2) 

When the SOC is higher than the target, the engine power request is reduced to promote CD operation. 

Alternatively, when the SOC is lower than the target, the engine power request is increased to recharge 

the battery. The adjustment is governed by the factor k which is set proportional to total battery 

capacity. An electric-launch speed of 10 mph (16 kph) is also specified, below which the strategy tries 

to operate the vehicle all-electrically by setting the engine power request to zero. However, both the 

SOC adjustment and electric launch can cause the power ratings of the motor to be exceeded. 

Therefore, a third element of the strategy is to constrain the engine power request to within acceptable 

limits such that no components are overloaded. Finally, there is engine on/off control logic. The 

engine is triggered on whenever the adjusted engine power request becomes positive. Once on, 

however, the engine can only turn off after it has been on for at least 5 minutes. This final constraint is 

designed to ensure the engine warms up thoroughly so that repeated cold starts are avoided. 

The aim of this control strategy is to prioritize discharging of the battery pack. Given the nature of the 

daily mileage distribution, this approach ensures that the maximum petroleum will be displaced. 

However, the strategy does not explicitly command all-electric operation. Rather, it discharges battery 

energy at the limits of the battery/motor power capabilities and uses the engine as needed to 

supplement the road load power demand. Therefore, the vehicle behavior that results is totally 

dependent on the power ratings of components. Vehicles with higher electric power ratings will have 

all-electric capability in more aggressive driving, whereas vehicles with lower electric power ratings 

will tend to operate in a “blended” CD-mode that utilizes both motor and engine. For more discussion 

of all-electric vs “blended” operation, the reader is directed to [16]. 

2.7 Driving Cycles 

The cost-benefit model simulates CVs, HEVs, and PHEVs over two cycles – the Urban Dynamometer 

Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET) – used by the US 

Environmental Protection Agency (EPA) for fuel economy and emissions testing and labeling [17]. 

2.8 Fuel Economy Measurement and Reporting 

The PHEV fuel economies and operating costs are measured and reported using a procedure based on 

a modification of the Society of Automotive Engineers' (SAE) J1711 Recommended Practice for 

Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles [18]. This 

procedure measures the fuel and electricity use in both CD and CS-modes and weights them according 

to the Utility Factor (UF), assuming the PHEVs are fully-recharged each day. Further discussion of 

this procedure for fuel economy measurement and reporting is provided in [17]. 

3 Results 

PHEV2, 5, 10, 20, 30, 40, 50, and 60 vehicles were considered in the study. Also, an HEV0 was 

modeled as a PHEV2 with its charger/plug removed. P/E ratios were chosen to vary DOH (defined as 

the ratio of motor power to total motor plus engine power) across a range of approximately 10%–55%. 

Note that the engine downsizing limit corresponds to a DOH of approximately 32%, and that DOH 

higher than this results in excess electric power capability onboard the vehicle. 

63
Battery Power vs Energy for PHEVs 

Figure 7 shows the battery 20 10 6 4 

specifications for the spectrum of 120 

PHEVs in the long-term scenario. 100

The total battery energy varies 

0 5 10 15 20 25 30 

Total Battery Energy (kWh) 

1 

2 

UDDS 

all-electric 

UDDS 

blended 

from approximately 1.5 kWh for 80 

Figure 7 includes dashed lines of 20 

constant P/E ratio, which varied 

from approximately 1–50. 0 

PHEV2 

PHEV5 

PHEV10 

PHEV20 

PHEV30 

PHEV40 

PHEV50 

PHEV60 

Figure 7 also indicates the 

minimum battery power 

requirement (approximately Figure 7: Battery specifications for the spectrum of PHEV 

45kW) for the PHEVs to have 

designs (long-term scenario) 

all-electric capability on the UDDS test cycle. The battery specifications for the near-term scenario 

are similar to Figure 7 but have increased power and energy requirements due to mass-compounding 

from the lower specific energy of NiMH batteries. 

Figure 8 presents the reductions Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans 

in annual petroleum consumption $20,000 

and incremental costs for the $18,000 

spectrum of PHEVs in the long- $16,000 

term scenario. Taking a 

$14,000 

Battery Power (kW) 

the HEV0/PHEV2 to 

approximately 25kWh for the 60 

PHEV60. The battery power 

varies from approximately 10– 40 

100kW across the range of DOH. 

HEV0 

PHEV2 

PHEV5 

PHEV10 

PHEV20 

PHEV30 

PHEV40 

PHEV50 

PHEV60 

UDDS AER 

vehicles 

macroscopic view, we see that 

Retail Cost Increment 

$12,000 

$10,000 

$8,000 

$6,000 

increasing PHEVx provides 

increasing reduction in 

petroleum consumption. 

Relative to the baseline CV, 

which consumes 659 gal (2494 $4,000 

L) of petroleum based on 15,000 

mi (24,100 km) each year, the 

$2,000 

$- HEVs reduce petroleum 0 100 200 300 400 500 600 

consumption by 20%–28%. The Reduction in Annual Petroleum Consumption (gals.) 

PHEVs reduce petroleum Figure 8: Incremental costs and annual petroleum consumption 

consumption further, ranging for the spectrum of PHEV designs (long-term scenario)

from 21%–31% for the PHEV2s

up to 53%–64% for the PHEV60s. However, these increasing reductions come at increasing costs.

The HEV0s are projected to cost US$2,000–$6,000 more than the baseline CV, whereas the PHEV60s

are projected to cost US$12,000–$18,000 more. The near-term trend is quite similar to Figure 8,

except that petroleum reductions are slightly reduced and vehicle cost increments are much larger due

to the greater mass and significantly higher cost of near-term NiMH batteries. 

Looking closely at Figure 8, we see a repeated trend in the relative cost-benefit of PHEVs with varying

DOH, and there is an optimum DOH for each PHEVx. For the HEV0s, the optimum DOH (32%) 

coincides with the limit of engine downsizing. For the PHEVs, the optimum DOH is higher (35%) to

coincide with the minimum battery power required for all-electric capability on the UDDS cycle (the

maximum power requirement on the HWFET cycle is lower). This all-electric capability allows

vehicles to avoid engine idling losses that would otherwise be incurred due to engine turn-on events 

subject to the 5-minute minimum engine on time constraint. The optimum HEVs and PHEVs for the

near-term and long-term scenarios are summarized in Tables 3 and 4. 

64
It must be emphasized that these optimum DOH are highly-dependent on the vehicle 

platform/performance attributes and the nature of the driving pattern. The analysis should be repeated 

for other baseline vehicles (e.g. sport-utility vehicles) to see how the PHEV designs will vary. 

Furthermore, PHEVs should be simulated over real-world driving cycles to identify differences in the 

petroleum displacement and all-electric operation compared to standard test cycles. Such further 

analyses should provide the understanding needed to optimize PHEVs for the market. 

Table 3: Near-Term Scenario PHEV Specifications – Optimum DOH Vehicles 

Vehicle Curb 

Mass 

(kg) 

Engine 

Power 

(kW) 

Motor 

Power 

(kW) 

DOH Battery 

Energy 

(kWh) 

P/E 

Ratio 

(1/h) 

SOC 

Window 

Fuel 

Cons. 

(L/100km) 

Elec. 

Cons. 

(Wh/km) 

Retail 

Cost 

(US$) 

CV 1429 122 --- --- --- --- --- 10.3 --- 23,392 

HEV0 1451 78 38 33% 1.5 33.4 37% 7.5 --- 28,773 

PHEV2 1451 78 38 33% 1.5 33.4 37% 7.3 7 29,435 

PHEV5 1505 80 42 35% 3.6 15.9 39% 7.1 17 31,447 

PHEV10 1571 82 44 35% 6.9 8.6 41% 6.7 33 34,180 

PHEV20 1678 85 47 35% 12.7 4.9 47% 6.0 60 38,935 

PHEV30 1759 89 49 36% 17.2 3.8 53% 5.4 84 42,618 

PHEV40 1824 91 51 36% 20.8 3.3 59% 4.8 104 45,655 

PHEV50 1880 94 52 36% 23.9 2.9 66% 4.5 118 48,162 

PHEV60 1923 96 53 36% 26.4 2.7 73% 4.1 133 50,184 

Table 4: Long-Term Scenario PHEV Specifications – Optimum DOH Vehicles 

Vehicle Curb 

Mass 

(kg) 

Engine 

Power 

(kW) 

Motor 

Power 

(kW) 

DOH Battery 

Energy 

(kWh) 

P/E 

Ratio 

(1/h) 

SOC 

Window 

Fuel 

Cons. 

(L/100km) 

Elec. 

Cons. 

(Wh/km) 

Retail 

Cost 

(US$) 

CV 1429 122 --- --- --- --- --- 10.3 --- 23,392 

HEV0 1412 77 36 32% 1.5 32.8 37% 7.4 --- 26,658 

PHEV2 1412 77 36 32% 1.5 32.8 37% 7.2 7 27,322 

PHEV5 1445 78 41 34% 3.5 15.7 39% 7.0 17 28,365 

PHEV10 1481 79 42 35% 6.6 8.5 41% 6.5 32 29,697 

PHEV20 1531 81 43 35% 11.8 4.9 47% 5.7 58 31,828 

PHEV30 1569 82 44 35% 15.9 3.7 53% 5.0 78 33,533 

PHEV40 1598 83 45 35% 19.0 3.2 59% 4.5 96 34,839 

PHEV50 1618 84 45 35% 21.6 2.8 66% 4.1 108 35,857 

PHEV60 1636 84 46 35% 23.6 2.6 73% 3.7 120 36,681 

3.1 Economics of PHEVs 

The PHEV cost-benefit analysis also includes a simple comparison of cost-of-ownership over the 

vehicle lifetime. The comparison includes the retail cost of the vehicle and the cost of its annual 

energy (fuel and electricity) consumption, but does not account for possible differences in maintenance 

costs (for a more thorough analysis of total PHEV lifecycle costs, the reader is directed to [14]). 

Figure 9 presents economic comparisons for the near-term and long-term scenarios. In calculating 

annual petroleum and electricity consumption, all vehicles are assumed to travel 15,000 mi (24,100 

km) per year to be consistent with the assumptions of the US EPA. The near-term cost of retail 

gasoline is assumed to be US$3 per gallon (US$0.79 per L), whereas a higher gasoline cost of US$5 

per gallon (US$1.32 per L) is assumed for the projected scenario. The cost of retail electricity is held 

constant at US$0.09 per kWh based on the 2005 US average retail price and historical trends [19]. No 

discount rate was applied to future cash flows. 

In the near-term scenario, the HEV achieves a lower cost-of-ownership than the CV after 

approximately 10 years. However, the PHEVs never achieve a lower cost-of-ownership than the CV 

nor the HEV over the 15-year vehicle lifetime. The long-term scenario provides a significant contrast, 

with the HEV providing lower cost than the CV after approximately 4 years and the PHEVs providing 

lower cost than the HEV after approximately 12 years. 

65
Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Cumulative Vehicle plus Energy (Fuel/Elec.) Costs 

$60,000 

0 5 10 15 

Cumulative Cost 

PHEV40 

PHEV20 

PHEV10 

HEV0 

N CV ear-term 

$60,000 

$50,000 $50,000 

0 5 10 

Years after purchase 

PHEV40 

PHEV20 

PHEV10 

HEV0 

CV Long-term 

$40,000 $40,000 

$30,000 $30,000 

Cumulative Cost $20,000 $20,000 

$-

$10,000 

Years after purchase 

$-

$10,000 

15 

Figure 9: Economic comparison of PHEVs in the near-term and long-term scenarios 

Several observations can be made from these comparisons. It is clear that these “payback” analyses 

are sensitive to the cost of gasoline and also the vehicle retail costs, which are strongly affected by the 

battery cost assumptions in each scenario. It is also clear that the economics of PHEVs are not 

promising if gasoline prices remain at current levels and battery costs cannot be improved. However, 

it does seem that a compelling business case for plug-in hybrids can be made under a scenario of both 

higher gasoline prices and projected (lower) battery costs, at least from the perspective of the simple 

consumer economic comparison presented here. 

Despite the uncertainty of PHEV economics, there are other factors that may justify the incremental 

PHEV cost. Examples include tax incentives; reductions in petroleum use, air pollution, and 

greenhouse emissions; national energy security; reduced maintenance; fewer fill-ups at the gas station; 

convenience of home recharging; improved acceleration from high-torque electric motors; a green 

image; opportunities to provide emergency backup power in the home; and the potential for vehicle-togrid applications. Alternative business models—such as battery leasing—also deserve further 

consideration since they might help to mitigate the daunting incremental vehicle cost and encourage 

PHEV buyers to focus on the potential for long-term cost savings. 

4 Conclusion 

This paper has presented a comparison of the costs (vehicle purchase costs and energy costs) and 

benefits (reduced petroleum consumption) of PHEVs relative to HEVs and CVs. Based on the study 

results, there is a very broad spectrum of HEV-PHEV designs with greatly varying costs and benefits. 

Furthermore, the PHEV cost-benefit equation is quite sensitive to a range of factors. In particular, 

battery costs, fuel costs, vehicle performance, and driving habits have a strong influence on the relative 

value of PHEVs. Given the large variability and uncertainty in these factors, it is difficult to predict 

the future potential for PHEVs to penetrate the market and reduce fleet petroleum consumption. 

However, the potential for PHEVs to reduce per-vehicle petroleum consumption is clearly very high. 

Reductions in excess of 45% are available using designs of PHEV20 or higher. This compares 

favorably with the 30% maximum reduction estimated for HEVs However, it seems likely that the 

added battery capacity of a PHEV will result in significant vehicle cost increments, even in the long 

term. For the projected scenario in this study, a retail cost increment of US$3,000 was estimated for a 

midsize sedan HEV. In contrast, the long-term cost increments for a midsize PHEV20 and PHEV40 

were estimated at US$8,000 and US$11,000 respectively. Without knowing the future costs of 

petroleum, it is impossible to determine the future economics of PHEVs. But it does seem likely, 

based on the results of this study, that it will be quite a challenge to justify the PHEV capital cost 

premium on the basis of reduced lifetime energy costs alone. Other incentives and business models 

may be required to create an attractive value proposition for PHEV motorists. However, the large 

petroleum reduction potential of PHEVs offers significant national benefits and provides strong 

justification for governmental support to accelerate the deployment of PHEV technology. 

66
Acknowledgement 

The authors would like to acknowledge the programmatic support of the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program. 

References 

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Subsequent Model Zero-Emission Vehicles, and 2002 and Subsequent Model Hybrid Electric Vehicles, in the 

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2006. 

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Consumption. NREL 39415. Golden, CO: National Renewable Energy Laboratory, 2006. 

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[9] Rush, D. Market Characterization for Light Duty Vehicle Technical Targets Analysis, National Renewable 

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[10] "Honda IMA System/Power Unit." Honda online, 

world.honda.com/CIVICHYBRID/Technology/NewHondaIMASystem/ PowerUnit/index_1.html, accessed July 

30, 2006. 

[11] "PAMVEC Model." University of Queensland Sustainable Energy Research Group online, 

www.itee.uq.edu.au/~serl/PAMVEC.html, accessed July 30, 2006. 

[12] Rosenkrantz, K. "Deep-Cycle Batteries for Plug-In Hybrid Application." EVS20 Plug-In Hybrid Vehicle 

Workshop, Long Beach, 2003. 

[13] "Multi-Year Program Plan." FreedomCAR and Vehicle Technologies Program online. 

http://www.eere.energy.gov/vehiclesandfuels/resources/fcvt_mypp.html, accessed July 30, 2006. 

[14] Graham, R. et al. "Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options." Electric Power 

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[15] Delucchi, M. "Electric and Gasoline Vehicle Lifecycle Cost and Energy-Use Model." Institute of 

Transportation Studies. University of California, Davis, 2000. 

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Automotive Battery Conference; 2006, Baltimore, Maryland. 

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22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exposition; 2006, Yokohama. 

[18] "SAE J1711 – Recommended Practice for Measuring Fuel Economy of Hybrid-Electric Vehicles." Society 

of Automotive Engineers Surface Vehicle Recommended Practice. Society of Automotive Engineers, 

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[19] U.S. Energy Information Administration online, www.eia.doe.gov, accessed July 30, 2006. 

Author 

Andrew Simpson, Vehicle Systems Engineer, National Renewable Energy Laboratory (NREL), 

1617 Cole Blvd, Golden CO 80401 USA; Tel: 303-275-4430; Fax: 303-275-4415; 

andrew_simpson@nrel.gov. Andrew joined the Advanced Vehicle Systems Group at NREL in 

2005 and his current focus is plug-in hybrid-electric vehicles. He holds a Bachelor of Mechanical 

Engineering (2000) and Ph.D. in Electrical Engineering (2005) from the University of Queensland, 

Brisbane, Australia. Prior to NREL, Andrew worked as a CFD consultant for Maunsell Australia. 

He also co-founded the Sustainable Energy Research Group at The University of Queensland and was a 

coordinating member of the University’s “SunShark” solar car team which raced successfully from 1996-2000. 

67
Section 2.3 

Title: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges”

Type: Presentation 

Authors: Matthew Thornton, Tony Markel, Andrew Simpson, Jeff Gonder, Aaron Brooker 

Date: July 20, 2006 

Conference or Meeting: Presented at the Federal Network for Sustainability Meeting at Idaho National 

Laboratory 

Abstract: Provides an overview of PHEV technology, technical challenges, and systems analysis efforts 

 68
Plug-in Hybrid Electric Vehicles

Current Status, Long-Term Prospects and Key Challenges 

Matthew Thornton 

Tony Markel, Andrew Simpson, Jeff Gonder, Aaron Brooker 

Federal Network for Sustainability Meeting 

July 20, 2006 

Idaho National Laboratory 

U.S. Department of Energy 

FreedomCAR and Vehicle Technologies Program 

Vehicle Systems Subprogram 

Vehicle Systems Analysis Activity 

Lee Slezak, Technology Manager 

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under 

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy 

(the “DOE”). The United States Government (the “Government”) retains 

and the publisher, by accepting the work for publication, acknowledges 

that the Government retains a non-exclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published form of this work 

or allow others to do so, for Government purposes. 

Neither MRI, the DOE, the Government, nor any other agency thereof, 

nor any of their employees, makes any warranty, express or implied, or 

assumes any liability or responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or 

represents that its use would not infringe any privately owned rights. 

Reference herein to any specific commercial product, process, or service 

by trade name, trademark, manufacturer, or otherwise does not constitute 

or imply its endorsement, recommendation, or favoring by the 

Government or any agency thereof. The views and opinions of the 

authors and/or presenters expressed herein do not necessarily state or 

reflect those of MRI, the DOE, the Government, or any agency thereof. 

69

2 
3 

The Perfect Storm 

• Petroleum consumption has steadily 

increased while domestic production has 

continued to decline 

• World oil production predicted to peak 

within the next 5-15 years 

• Recent increase in gasoline price is 

indicator of growing tension between 

supply and demand 

WHAT’S OUR PLAN?

0 

5 

10 

15 

20 

25 

1940 1950 1960 1970 1980 1990 2000 2010 

Petroleum (mmb/day) 

Domestic Production 

Domestic Consumption 

Source: U.S. Department of Energy, Energy Information Administration 

Gasoline price - 85% rise in 5 years! 

Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews 

0 

0.5 

1 

1.5 

2 

2.5 

3 

3.5 

Aug 11, 

1987 

May 07, 

1990 

Jan 31, 

1993 

Oct 28, 

1995 

Jul 24, 

1998 

Apr 19, 

2001 

Jan 14, 

2004 

Oct 10, 

2006 

Jul 06, 

2009 

Weekly National Average Gasoline

Retail Price ($/gal) 

Source: U.S. Department of Energy, Energy Information Administration 

America’s Oil Addiction 

• Transportation sector nearly 

100% petroleum dependent 

• Transportation accounts for 

two-thirds of total petroleum 

consumption 

• 58% of petroleum is imported 

“America is addicted to oil...” 

The President’s Advanced Energy Initiative Goals: 

Fueling Our Vehicles 

1. Develop advanced battery technologies that allow a plug-in hybrid-electric vehicle to 

have a 40-mile range operating solely on battery charge 

2. Foster the breakthrough technologies needed to make cellulosic ethanol costcompetitive with corn-based ethanol by 2012. 

3. Accelerate progress towards the President’s goal of enabling large numbers of 

Americans to choose hydrogen fuel cell vehicles by 2020. 

Source: www.whitehouse.gov 4 

70
5 

A “Full” Hybrid

ELECTRIC ACCESSORIES 

ADVANCED ENGINE 

ENGINE IDLE-OFF 

ENGINE DOWNSIZING 

REGENERATIVE BRAKING 

PETROLEUM ONLY 

76hp gasoline engine, 67hp electric motor, 1.5kWh battery 

Contemporary Hybrids 

6 

Toyota Camry 

Honda Civic 

Saturn Vue 

Toyota Prius Toyota Highlander Honda Insight 

Lexus RX400h Lexus GS450h Honda Accord 

Chevy Silverado 

Ford Escape 

71
ELECTRIC ACC

ADVANCED ENGINE

REG

7 

A Plug-In Hybrid 

ESSORIES 

ENGINE IDLE-OFF 

ENGINE DOWNSIZING 

ENERATIVE BRAKING 

BATTERY RECHARGE 

ELECTRICITY 

PETROLEUM 

AND/OR 

76hp gasoline engine, 67hp electric motor, 9.0kWh battery (30mi) 

Fuel Flexibility 

8 

OEM Plug-In Hybrids 

2003 Renault Kangoo Elect’road 

- up to 50mi electric range

- approximately 500 sold in Europe

Renault Cleanova 

- Max range 329 

miles with 5.3 

gallons of fuel 

DaimlerChrysler Sprinter PHEV 

- 15 prototypes being produced for

testing in various locations in 

Europe and North America

- up to 20mi electric range 

72
9 

Other PHEV Prototypes - Industry 

EnergyCS Plug-In Prius 

HyMotion Escape PHEV 

10 

Light Duty Fleet Oil Use - Impact of HEVs on Consumption 

0 

2 

4 

6 

8 

10 

12 

14 

16 

2000 2005 2010 2015 2020 2025 2030 

Year 

Oil Consumption (MPBD) 

2005 AEO Base Case 

HEV Scenario 

Oil Use Reduction with HEVs 

3 MBPD Oil use same as today! 

This highly aggressive scenario assumes 100% HEV sales from 2010 onwards... 

Produced using VISION model, MBPD = million barrels per day 

HEVs unable to reduce consumption below today’s consumption level 

73
74

Oil Consumption (MPBD) 

Oil Use Reduction with PHEVs 

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption 

16 

2005 AEO Base Case

PHEV Scenario

14 

12 

Oil use reduction! 4 MBPD 10 

8 

6 

PHEVs on E85 ?? 4 

This highly aggressive scenario assumes 100% HEV sales 

from 2010 and 50% PHEV40 sales from 2020 onwards... 2 

0

2000 2005 2010 2015 2020 2025 2030

Year 

PHEVs reduce oil consumption with a transition to electricity 

11 Produced using VISION model, MBPD = million barrels per day 

Design Options 

All-Electric vs Blended Strategy 

All-Electric 

70 100% • Engine turns on when battery engine 

60 motor 90% reaches low state of charge SOC 50 80% 

• Requires high power battery and 40 70% 

30 60% motor 

20 50% 

10 40% 

0 30% 

SOC (%) 

-10 20% Blended 

-20 10% 70 100% engine 

-30 0% 60 motor 90% 

0 5 10 15 20 25 30 35 40 SOC 

50 80% Distance (mi) 

40 70% 

• Engine turns on when power

Power (kW) 

30 60% 

20 50% 

10 40% 

exceeds battery power capability 0 30% 

-10 20% • Engine only provides load that 

exceeds battery power capability -20 10% 

-30 0% 

0 5 10 15 20 25 30 35 40 

Distance (mi) 

12 

Power (kW) 

SOC (%) 
---

---

13 

Plug-In Hybrid Fuel Economy 

Predicted fuel economy and operating costs for midsize sedan1 

Vehicle Type 

Conventional 

Hybrid-Electric 

Plug-In Hybrid 

20mi range 

Plug-In Hybrid 

40mi range 

Gasoline

Fuel

Economy

27 mpg

36 mpg 

51 mpg 

69 mpg 

Electricity

Use

0.09 kWh/mi 

0.16 kWh/mi 

Annual

Energy Use

564 gal. 

416 gal. 

297 gal. and 

1394 kWh2 

218 gal. and 

2342 kWh2 

Annual 

Energy Cost 

Recharge 

Time3 

$1360 ---

$1000 ---

$716 + $125 < 4 hrs 

$525 + $211 < 8 hrs 

1) Assumes 15,000 miles annually, gasoline price of $2.41 per gallon, electricity price of 9c/kWh 

2) Note that average US household consumes 10,700 kWh of electricity each year 

3) Using 110V, 20A household outlet 

14 

Household Travel Survey Data Can be Used to Predict 

Real-World Benefits of Advanced Technologies 

• Provides valuable insight into travel behavior 

• GPS augmented surveys supply details needed 

for vehicle simulation 

75
15 

0 5 10 15 20 25 30 0 

5 

10 

15 

20 

25 

30 

35 

40 

Time of Day (hr) 

Percentage of Vehicle Fleet In Use (%) 

0 5 10 15 20 25 300 

50 

100 

150 

200 

250 

300 

350 

400 

Cu

mulative Fuel Consumed (gallons) 

Conventional 

Hybrid 

PHEV20 

PHEV40 

PHEVs Reduce Fuel Consumption By >50% 

On Real- World Driving Cycles 

• 8647 total miles driven 

• 100% replacement of 

sample fleet 

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

26 mpg 

58 mpg & 

140 Wh/mi 

PHEVs: 

>40% reduction in 

energy costs 

>$500 annual savings 

37 mpg 

76 mpg & 

211 Wh/mi 

Assumes $2.41/gal and 9¢/kWh 

$1.21 

$1.58 

$2.48 

$3.45 

Gas. 

Average Daily Costs 

$0.72 

$0.48 

---

---

Elec. 

5.1 

5.4 

6.5 

9.1 

¢/mi 

PHEV40 

PHEV20 

HEV 

CV 

16 

HEVs and PHEVs Likely to Reduce 

Greenhouse Emissions 

Source: Hybrid Electric Vehicle Working Group, www.epri.com 

76
17 

Electrified Miles May Lead to Cleaner Operation 

Source: Hybrid Electric Vehicle Working Group, www.epri.com 

18 

Technical Challenges 

Battery Life 

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles 

• Also need to consider combination of high and low frequency cycling 

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20 

4000 

50% 

70% 

77
19 

Technical Challenges 

Battery Packaging 

Technical Challenges 

Vehicle Costs 

Incremental Cost (%)

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

HOW CAN WE SAVE THE MOST GALLONS AT THE LEAST COST? 

Prius (Corolla) 

Civic 

Accord 

Escape 

Highlander 

Vue 

HEVs 

PHEVs? 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Gasoline Savings (%) 

78

20 
21 

Vehicle Costs

Projected Retail Powertrain Costs - Midsize Sedans 

1) including manufacturer and dealer markups 

2) all component costs assume projected status 

$4,005 $2,865 $2,919 $3,002 

$1,998 

$1,992 $2,004 $2,020 

$2,079 $2,407 $2,454 

$2,433 

$5,117 

$8,433 

$663 

$663 

$-

$2,000 

$4,000 

$6,000 

$8,000 

$10,000 

$12,000 

$14,000 

$16,000 

$18,000 

charger/plug 

battery 

motor/inverter 

transmission 

engine 

CV HEV PHEV20 PHEV40 

22 

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs 

$-

$10,000 

$20,000 

$30,000 

$40,000 

$50,000 

$60,000 

0 5 10 15 

Time after purchase (years) 

Cumulative cost (2006 $) 

CV 

HEV 

PHEV20 

PHEV40 

$2.41/gal. (2005 avg)

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 5 10 15

Time after purchase (years)

Cumulative cost (2006 $)

CV

HEV

PHEV20

PHEV40

$3.00/gal. (today) 

PH

umulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 5 10 15

Time after purchase (years)

Cumulative cost (2006 $)

CV

HEV

PHEV20

EV40

$4.00/gal.

Vehicle Costs cont. 

79
23 

Vehicle Costs cont. 

Why would PHEV buyers pay more? 

• Tax incentives 

• Reduced petroleum use, air pollution and CO2 

• National energy security 

• Less maintenance 

• Reduced fill-ups 

• Convenience of home recharging (off-peak) 

• Improved acceleration (high torque of electric 

motors) 

• Green image, “feel-good factor” 

Our Contributions 

• Developing Route-Based control strategies that 

will help extend the life of vehicle batteries 

• Evaluating alternative battery usage and 

ownership scenarios to reduce costs

— Does it really have to last the life of the vehicle 

— Battery lease or combine with home mortgage 

• Using travel data to develop real-world usage 

pattern to aid in effective system design

• Vehicle thermal management for PHEVs 

— Energy storage – protection for life 

— Cabin – passenger comfort 

— Engine – emissions control 

80

24 
25 

Conclusions 

• Plug-in hybrid technology uses electricity from the utility 

grid to reduce petroleum consumption beyond that of HEV 

technology 

— Predicted 50% reduction in in-use consumption based on 

simulations using travel survey data 

• Industry interest is growing and some prototypes have 

been built

— Collaboration between labs and industry will likely lead to 

innovative systems solutions 

• The U.S. Department of Energy is expanding its research 

portfolio to include PHEVs 

— Research will address key remaining barriers to commercial 

PHEVs including battery life, packaging, and cost 

81
Section 2.4 

Title: “Plug-In Hybrid Electric Vehicle Energy Storage Analysis”

Type: Presentation 

Authors: Andrew Simpson and Tony Markel 

Date: July 25, 2006 

Conference or Meeting: Presented to the FreedomCAR Electrochemical Energy Storage Technical 

Team PHEV Working Group Meeting

Abstract: Provides a summary of cost and consumption benefit analysis as it relates to setting

requirements for energy storage systems for PHEVs 

 82
Plug-In Hybrid Electric Vehicle

Energy Storage Analysis

spacer

(with implications for vehicle fuel economy and cost) By

Andrew Simpson and Tony Markel

Vehicle Systems Analysis Team

National Renewable Energy Laboratory

Plug-In Hybrid Battery Workgroup Meeting

Electrochemical Energy Storage Tech Team

USCAR, Southfield, MI

July 25, 2006

With support from

Lee Slezak, Vehicle Systems Program

FreedomCAR and Vehicle Technologies Program

Office of Energy Efficiency and Renewable Energy

U.S. Department of Energy

2

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy

(the “DOE”). The United States Government (the “Government”) retains

and the publisher, by accepting the work for publication, acknowledges

that the Government retains a non-exclusive, paid-up, irrevocable,

worldwide license to publish or reproduce the published form of this work

or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof,

nor any of their employees, makes any warranty, express or implied, or

assumes any liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights.

Reference herein to any specific commercial product, process, or service

by trade name, trademark, manufacturer, or otherwise does not constitute

or imply its endorsement, recommendation, or favoring by the

Government or any agency thereof. The views and opinions of the

authors and/or presenters expressed herein do not necessarily state or

reflect those of MRI, the DOE, the Government, or any agency thereof.

83
3

NREL’s Plug-in HEV R&D Activities 

• Battery Level — R&D support to developers

— Testing and evaluation 

— EPRI/Sprinter PHEV testing 

— Thermal characterization and design

• Vehicle Level — Real-world PHEV simulations - fuel economy and recharging

— Development of test procedures for PHEVs and 

MPG reporting

— Evaluation of alternative PHEV design strategies

— PHEV design cost-benefit analysis

• Utility Level

— Assessment of PHEV impacts on utilities

— Exploring synergies between PHEVs and wind 

power

— V2G opportunities for PHEVs in regulation services

• National Level — Benefits assessment - oil use and emissions

— Renewable community – linking PHEV to renewable 

• Analysis support to DOE, OEMs, and others — Working to identify and overcome barriers to PHEV

adoption

Secretary of Energy visiting NREL on 

7/7/06 for ribbon cutting of the new S&T

Facility and then discussing plug-in 

hybrids with EnergyCS & Hymotion

4

Key Messages

In setting energy storage requirements for PHEVs, we need

to consider:

1. There is a VERY large spectrum of PHEV designs

2. There is a link between SOC window, driving habits and

cycle life that impacts energy storage size and cost

3. A wide range of P/E ratios may be needed

4. Tradeoffs between fuel economy / oil displacement and 

powertrain mass, volume and cost

Fundamental Question:

What kind of PHEV are we setting requirements for?

84
5

Some PHEV Definitions

Charge-Depleting (CD) Mode: An operating mode in which the energy storage SOC may fluctuate 

but on-average decreases while driving

Charge-Sustaining (CS) Mode: An operating mode in which the energy storage SOC may fluctuate but on-average is maintained at a certain level while driving

All-Electric Range (AER): After a full recharge, the total miles driven electrically (engine-off) before the engine turns on for the first time. 

Zero Emission Vehicle (ZEV) Range: The same as all-electric range (AER), no tail-pipe emissions during EV mode. To qualify for CARB ZEV, the minimum ZEV or AER should be 10 miles during 

the UDDS drive cycle.

EV, HEV and Blended Modes and Range:

Electric Vehicle Miles (EVM): After a full recharge, the total miles driven electrically (engine-off) 

before the vehicle reaches charge-sustaining mode

Charge-Depleting Range (CDR): After a full recharge, the total miles driven before the vehicle reaches charge-sustaining mode.

PHEV20: A PHEV with useable energy storage equivalent to 20 miles of driving energy on a reference driving cycle. The PHEV20 can displace petroleum energy equivalent to 20 miles of driving on the 

reference cycle with off-board electricity.

NOTE: PHEV20 does not imply that the vehicle will achieve 20 miles of AER, EVM or CDR on the reference cycle nor any other driving cycle. Operating characteristics also depend on the power ratings of components, the powertrain control strategy and the nature of the driving cycle

6

PHEV Architectures

A PHEV can be implemented using any of the HEV architectures

(parallel, series or power-split).

The two things that differentiate PHEVs from HEVs are a CD operating 

mode and a recharging plug.

ENGINE TRANS.

BATTERY MOTOR

The results shown in this presentation are for a parallel PHEV.

85
7

PHEV Design Strategies: All-Electric vs Blended

Î Uses lower-power battery and 

motor

Î Engine turns on when power

request exceeds battery power limit

Í Requires higher-power

battery and motor

Í Engine turns on when battery

reaches CS state of charge

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

All-Electric

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

Blended

8

PHEV Design Strategies (cont.)

Benefits of using a blended strategy:

Lower power to energy ratio leads to lighter, 

smaller, and cheaper energy storage system.

Battery Design Functions

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 50 100 150 200

Specific Energy (Wh/kg)

Sp ec ific Pow e r (W /kg)

NiMH Typ LI-ION ical

Battery Module Cost Functions

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

Power-to-Energy (P/E) Ratio

Module Cost ($/kW h)

Near Term

Long Term

Estimation

86
9

PHEV Design Spectrum

battery energy

battery power

(PHEVx)

(degree of

hybridization)

what’s our target?

cycle life

mass

volume

cost

fuel economy temperature

calendar life

Voltage range

capacity

self discharge

vehicle

attributes

gasoline displaced

10

Exploring the PHEV Design Spectrum

Cost / Benefit Analysis of Hybrid-Electric and

Plug-In Hybrid-Electric Vehicle Technology

A presentation to the FreedomCAR Vehicle Systems Analysis Technical Team by

Andrew Simpson National Renewable Energy Laboratory

Presented Wednesday, 1st March 2006

REVISED May 2006

With support from the

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

FreedomCAR and Vehicle Technologies Program

Key Inputs:

Vehicle platforms and 

performances

SOC design window

Control strategy

Battery performance and cost 

models

Other component performance 

and cost models

Ongoing NREL study using validated CV/HEV/PHEV simulation model

Plug-in Hybrid Electric Vehicle Energy

Storage System Design

Advanced Automotive Battery Conference

Baltimore, Maryland

May 19th, 2006

by

Tony Markel and Andrew Simpson National Renewable Energy Laboratory

With support from the

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

FreedomCAR and Vehicle Technologies Program

87
11

Baseline Vehicle Characteristics – Midsize Sedan

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

Engine power 121 kW

Vehicle attributes

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Top speed 110 mph

Passing acceleration 40-60 mph in 5.3 s

Standing acceleration 0-60 mph in 8.0 s

Performance Parameters

Baseline accessory load 800 W elec. + 2900 W A/C

Rolling resistance coefficient 0.009

2.27m2 Frontal area

Drag coefficient 0.30

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Test Mass 1565 kg (136 kg load)

Curb Mass 1429 kg

Glider Mass 905 kg

Platform Parameters

MIDSIZE SEDAN (AUTOMATIC)

Under review/recommendation by VSATT

12

Baseline Vehicle Characteristics – Midsize SUV

Fuel economy 15.6 / 24.0 / 18.5 mpg (urban / highway / composite, unadjusted)

Engine power 178 kW

Vehicle attributes

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Top speed 110 mph

Passing acceleration 40-60 mph in 5.3 s

Standing acceleration 0-60 mph in 8.4 s

Performance Parameters

Baseline accessory load 1200 W elec. + 4000 W A/C

Rolling resistance coefficient 0.009

2.88m2 Frontal area

Drag coefficient 0.41

Gross Vehicle Mass (GVM) 2667 (516 kg load)

Test Mass 2151 kg (136 kg load)

Curb Mass 2015 kg

Glider Mass 1280 kg

Platform Parameters

MIDSIZE SUV (AUTOMATIC 4WD)

Under review/recommendation by VSATT

88
13

Battery Definition as Key Input to Simulation

kWh/mi

(from simulation)

SOC window

PHEV range

P/E ratio

Performance 

constraints

kWh usable

kWh total

kWmotor

kWengine

DOH

Benefit of 

plugging-in

Benefit of 

hybridization

Total MPG Benefit

mass compounding

Input parameters that define the battery in BLUE

DOH = degree of hybridization

14

Battery SOC Design Window

Battery cycle life is a strong

function of SOC swing:

In setting requirements, we must have realistic expectations for battery

SOC design window because of the impacts on total battery size and life.

SOC design window is a key

factor in battery sizing: SOC window

kWh kWh useable

total =

Daily Mileage Probability Distribution

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50 60 70 80 90 100

Daily mileage (<= mi)

Probability (%)

Battery SOC swing is a strong 

function of driving habits:

89
15

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Design SOC window 

based on PHEVx

Battery SOC Design Window

Daily mileage probability distribution

In this hypothetical example, all PHEVs use the same SOC design window. However, because of the daily 

mileage distribution, the low range PHEV batteries experience more-severe cycling and are likely to fail sooner.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Average daily SOC

swing based on daily

mileage distribution

16

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Design SOC window 

based on PHEVx

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Average daily SOC

swing based on daily

mileage distribution

Battery SOC Design Window (cont.)

Daily mileage probability distribution

In this hypothetical example, PHEVs use a different SOC design window based on their PHEVx design range. 

This produces consistent cycling across the range of PHEVs resulting in consistent expectations for battery life.

90
17

Battery Module Cost Functions

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

Power-to-Energy (P/E) Ratio

Module Cost ($/kWh)

Near Term

Long Term

Battery Design Functions

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 50 100 150 200

Specific Energy (Wh/kg)

Specific Power (W/kg)

NiMH

LI-ION

Battery Models (Scaleable)

Battery Efficiency Curve

60%

65%

70%

75%

80%

85%

90%

95%

100%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

P / Pmax

Efficiency (%)

18

Results: All-Electric Range

0

10

20

30

40

50

60

70

Miles

Charge Depleting Range

All-Electric Range

Midsize Sedans

PHEV2 PHEV5 PHEV10 PHEV20 PHEV30 PHEV40 PHEV50 PHEV60

These results include blended 

and all-electric PHEVs

91
19

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50 60 70

PHEVx

Battery Energy (kWh useable)

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Results: Useable Battery Energy

Midsize Sedans:

Midsize SUVs: ~515 Wh/mi

~290 Wh/mi

20

1

2

20 10 5

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50

Battery Energy (kWh total)

Battery Power (kW)

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Results: Battery Specifications

Midsize Sedans

92
21

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50

Battery Energy (kWh total)

Battery Power (kW)

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

1

2

20 10 5

Results: Battery Specifications

Midsize SUVs

22

PHEV Battery Options ESS Technology Comparison - P/E Ratios

SAFT VLE 45

SAFT VLM 41

SAFT VLP 7

1

2

5

10

Avestor SE 48S63

SAFT VLM 27

SAFT VLP 20

SAFT VLP 30

Cyclon D

Cyclon E

Varta Li 60Ah

Varta Li 6Ah

Prius PEVE SAFT VLE module

SAFT VL41M module

SAFT VL30P module

Cobasys 1000

Cobasys 4500

Cobasys 9500

1000 500 200 100 50 20

SAPHION U1-12FN40

SAPHION U24-12FN100

SAPHION U27-FN130

Optima D51

Optima D34

Optima D35

PEVE 7.5Ah

Kokam SLPB30205130

Kokam SLPB41205130 Kokam SLPB55205130

Kokam SLPB78216216

Kokam SLPB100216216

Kokam SLPB60460330

Kokam SLPB80460330

SAFT NiMH 12

SAFT VHF 10S SAFT VHF 20S

SAFT VHF 30S

SAFT STM 5-100 MR

SAFT STM 5-140 MR

Sprinter Varta

Varta Ni HP

Varta Ni UHP

Prius+ SAPHION

Sprinter SAFT

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 30 60 90 120 150

Available specific energy (Wh/kg)

Specific power (W/kg)

93
23

1

10

100

0 10 20 30 40 50 60 70

PHEVx

P/E Ratio (total)

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Results: Power-to-Energy Ratio

Midsize Sedans

1

10

100

0 10 20 30 40 50 60 70

PHEVx

P/E Ratio (total)

and Midsize SUVs

24

Results: Midsize Sedans

Curb Mass Vehicle Cost Engine Motor DOH P/E Battery SOC Fuel Use Elec. Use

(kg) (kW) (kW) (%) (1/h) (kWh) (%) (mpg) (Wh/mi)

CV --- 1429 $ 23,392 121.7 --- --- --- --- --- 26.6 ---

HEV downsized 1416 $ 26,790 75.0 38.7 34% 39.0 1.4 37% 36.0 0.0

PHEV10 blended 1494 $ 29,071 100.1 19.1 16% 4.6 6.6 41% 38.3 52.0

PHEV10 UDDS AER 1483 $ 29,698 77.0 41.8 35% 9.1 6.6 41% 42.3 51.9

PHEV10 US06 AER 1584 $ 32,862 80.2 93.8 54% 17.5 7.3 41% 38.7 57.4

PHEV20 blended 1542 $ 31,238 102.8 19.7 16% 2.6 11.9 47% 44.0 95.9

PHEV20 UDDS AER 1536 $ 31,885 78.9 43.1 35% 5.2 11.9 47% 50.1 95.1

PHEV20 US06 AER 1657 $ 35,458 82.5 97.8 54% 10.1 13.3 47% 45.7 104.4

PHEV40 blended 1605 $ 34,237 105.9 21.0 17% 1.7 19.1 59% 55.3 160.5

PHEV40 UDDS AER 1604 $ 34,970 81.0 45.9 36% 3.4 19.2 59% 69.0 157.3

PHEV40 US06 AER 1750 $ 39,095 85.4 102.9 55% 6.5 21.6 59% 63.2 173.4

Vehicle

projected battery costs

94
25

Results: Midsize SUVs

Curb Mass Vehicle Cost Engine Motor DOH P/E Battery SOC Fuel Use Elec. Use

(kg) (kW) (kW) (%) (1/h) (kWh) (%) (mpg) (Wh/mi)

CV --- 2015 $ 29,400 184.0 --- --- --- --- --- 18.5 ---

HEV downsized 2002 $ 33,175 129.2 41.7 24% 25.7 2.4 37% 23.4 0.0

PHEV10 blended 2111 $ 37,198 148.9 30.1 17% 4.2 11.3 41% 25.8 89.3

PHEV10 UDDS AER 2138 $ 38,743 129.9 66.7 34% 8.3 11.4 41% 27.1 90.3

PHEV10 US06 AER 2296 $ 43,905 130.8 152.3 54% 16.2 12.8 41% 25.4 100.4

PHEV20 blended 2194 $ 40,944 153.7 31.6 17% 2.4 20.3 47% 29.3 165.2

PHEV20 UDDS AER 2224 $ 42,528 130.4 69.8 35% 4.8 20.6 47% 32.3 165.9

PHEV20 US06 AER 2415 $ 48,378 132.3 158.7 55% 9.3 23.3 47% 30.3 183.1

PHEV40 blended 2303 $ 46,170 160.0 33.4 17% 1.6 33.0 59% 36.9 279.5

PHEV40 UDDS AER 2334 $ 47,787 131.0 73.5 36% 3.1 33.5 59% 44.5 276.6

PHEV40 US06 AER 2586 $ 55,058 138.4 171.2 55% 6.1 38.2 59% 41.1 305.9

Vehicle

projected battery costs

26

Results: Curb Mass

Reduction in Fuel Consumption vs Curb Mass

1400

1600

1800

2000

2200

2400

2600

2800

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Curb Mass (kg)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize Sedans

Reduction in Fuel Consumption vs Curb Mass

1400

1600

1800

2000

2200

2400

2600

2800

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Curb Mass (kg)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize SUVs

95
27

Results: Powertrain Volume

Reduction in Fuel Consumption vs Powertrain Volume

200

300

400

500

600

700

800

900

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Powertrain Volume (L)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize Sedans

Reduction in Fuel Consumption vs Powertrain Volume

200

300

400

500

600

700

800

900

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Powertrain Volume (L)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize SUVs

28

Reduction in Fuel Consumption vs Battery Volume

0

50

100

150

200

250

300

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Battery Volume (L)

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize Sedans

Results: Battery Volume

Reduction in Fuel Consumption vs Battery Volume

0

50

100

150

200

250

300

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Battery Volume (L)

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize SUVs

96
29

Reduction in Fuel Consumption vs Vehicle Cost

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Vehicle Retail Cost ($)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Reduction in Fuel Consumption vs Vehicle Cost

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Vehicle Retail Cost ($)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize SUVs

projected battery costs

Results: Vehicle Cost

Midsize Sedans

projected battery costs

30

Reduction in Fuel Consumption vs Vehicle Cost

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Vehicle Retail Cost ($)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize Sedans

projected battery costs

Reduction in Fuel Consumption vs Vehicle Cost

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

0 100 200 300 400 500 600

Reduction in Annual Fuel Consumption (Gals.)

Vehicle Retail Cost ($)

CV

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

Midsize Sedans

current battery costs

Results: Vehicle Cost

97
31

0 5 10 15 20 25 30 0

5

10

15

20

25

30

35

40

Time of Day (hr)

Percentage of Vehicle Fleet In Use (%)

0 5 10 15 20 25 300

50

100

150

200

250

300

350

400

Cu

mulative Fuel Consumed (gallons)

Conventional

Hybrid

PHEV20

PHEV40

PHEVs Reduce Fuel Consumption By >50%

On Real- World Driving Cycles

• 8647 total miles driven

• 100% replacement of

sample fleet

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

26 mpg

58 mpg &

140 Wh/mi

PHEVs:

>40% reduction in

energy costs

>$500 annual savings

37 mpg

76 mpg &

211 Wh/mi

Assumes $2.41/gal and 9¢/kWh

$1.21

$1.58

$2.48

$3.45

Gas.

Average Daily Costs

$0.72

$0.48

---

---

Elec.

5.1

5.4

6.5

9.1

¢/mi

PHEV40

PHEV20

HEV

CV

Source: Tony Markel, Jeff Gondor, and Andrew Simpson (NREL), 

Milestone Report , Golden, CO, May 2006

32

Fuel Economy and All Electric Range Comparison 

• Difference between rated (EPA drive cycles) and 

Real median values are significant for the PHEVs

— Consumers likely to observe fuel economy higher

than rated value in typical driving

— Vehicles designed with all electric range likely to 

operate in a blended mode to meet driver demands

** Fuel economy values do not include electrical energy consumption

Rated Median Rated Median

Conventional 26 24.4 n/a n/a

HEV 39.2 35.8 n/a n/a

PHEV20 54 70.2 22.3 5.6

PHEV40 67.4 133.6 35.8 3.8

Fuel Economy (mpg) ** All Electric Range (mi)

Source: Tony Markel, Jeff Gonder, and Andrew Simpson (NREL), 

Milestone Report , Golden, CO, May 2006

98
33

Technical Summary

Midsize Sedan

Electrical energy use on UDDS: ~290 Wh/mi

Battery power for all-electric UDDS capability: ~60kW

PHEV 10-40 useable energy: 2.7 – 11.4 kWh

PHEV 10-40 total energy: 6.6 – 19.2 kWh

P/E ratio (total energy): 9.1 – 3.4

Midsize SUV

Electrical energy use on UDDS: ~510 Wh/mi

Battery power for all-electric UDDS capability: ~100kW

PHEV 10-40 useable energy: 4.7 – 19.9 kWh

PHEV 10-40 total energy: 11.4 – 33.5 kWh

P/E ratio (total energy): 8.3 – 3.1

34

Conclusions and Key Messages

In setting energy storage requirements for PHEVs, we need to 

consider:

1. There is a VERY large spectrum of PHEV designs

— Battery energy: 2-50kWh, battery power: 20-250kW

2. There is a link between SOC window, driving habits and cycle life 

that impacts energy storage size and cost

— Different PHEVx requires different SOC design window

3. A wide range of P/E ratios may be needed

— P/E ratios = 1-40

4. Tradeoffs between fuel economy / oil displacement and powertrain

mass, volume and cost

— Blended strategy PHEVs can help reduce battery mass, volume and 

cost in the near term.

— In the long term, all-electric PHEVs achieve a target level of oil 

displacement at the lowest mass, volume and cost

Fundamental Question:

What kind of PHEV are we setting requirements for?

99
Section 2.5 

Title: “Plug-In Hybrid Modeling and Application: Cost/Benefit Analysis”

Type: Presentation 

Author: Andrew Simpson 

Date: Aug. 24, 2006 

Conference or Meeting: Presented at the Third AVL Summer Conference on Automotive Simulation 

Technology: Modeling of Advanced Powertrain Systems 

Abstract: Includes a brief summary of the cost and consumption benefit analysis 

 100
Plug-in Hybrid Modeling and Application: 

Cost / Benefit Analysis

Presented at the 3rd AVL Summer Conference on Automotive Simulation Technology:

Modeling of Advanced Powertrain Systems

Andrew Simpson

National Renewable Energy Laboratory

Thursday, 24th August 2006

With support from the

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

FreedomCAR and Vehicle Technologies Program

2

Disclaimer and Government License

This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36-

99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the

“Government”) retains and the publisher, by accepting the work for publication, acknowledges that 

the Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or 

reproduce the published form of this work, or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof, nor any of their employees, 

makes any warranty, express or implied, or assumes any liability or responsibility for the accuracy, 

completeness, or usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights. Reference herein to any

specific commercial product, process, or service by trade name, trademark, manufacturer, or 

otherwise does not constitute or imply its endorsement, recommendation, or favoring by the 

Government or any agency thereof. The views and opinions of the authors and/or presenters

expressed herein do not necessarily state or reflect those of MRI, the DOE, the Government, or 

any agency thereof.

101
3

Presentation Outline

• What is a plug-in hybrid-electric vehicle (PHEV)?

• Potential petroleum reduction from PHEVs

• Simulation of PHEV efficiency and cost

— Baseline vehicle assumptions

— Powertrain technology scenarios

— Components models (cost, mass, efficiency)

• Results

— Component sizing

— Fuel Economy

— Incremental cost

— Payback scenarios

• Conclusions & Next Steps

4

A Plug-In Hybrid-Electric Vehicle (PHEV)

ELECTRIC ACCESSORIES

ADVANCED ENGINE

ENGINE IDLE-OFF

ENGINE DOWNSIZING

REGENERATIVE BRAKING

BATTERY RECHARGE

ELECTRICITY

PETROLEUM

AND/OR

Fuel Flexibility

102
5

Some PHEV Definitions

All-Electric Range (AER): After a full recharge, the total miles driven

electrically (engine-off) before the engine turns on for the first time.

Blended Mode: A charge-depleting operating mode in which the 

engine is used to supplement battery/motor power.

PHEV20: A PHEV with useable energy storage equivalent to 20 miles

of driving energy on a reference driving cycle. 

NOTE: PHEV20 does not imply that the vehicle will achieve 20 miles of

AER on the reference cycle nor any other driving cycle. Operating 

characteristics depend on the power ratings of components, the 

powertrain control strategy and the nature of the driving cycle

6

KEY CHALLENGES

• Recharging locations

• Battery life

• Component packaging

• Vehicle cost

KEY BENEFITS

Consumer:

• Lower “fuel” costs

• Fewer fill-ups

• Home recharging convenience

• Fuel flexibility

Nation:

• Less petroleum use

• Less greenhouse and regulated 

emissions

• Energy diversity/security

PHEV Key Benefits and Challenges

?

Cost-Benefit Analysis

103
7

National Driving Statistics:

1995 National Personal Transportation Survey

0 20 40 60 80 100 0

10

20

30

40

50

60

70

80

90

100

Daily Mileage (mi)

Probability (%)

Daily Mileage Distribution and Utility Factor Curve

Daily mileage distribution

Utility Factor curve

8

Potential Petroleum Reduction from PHEVs

WHAT ARE THE

RELATIVE COSTS?

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 20% 40% 60% 80% 100%

Reduction in Charge-Sustaining Mode Petroleum Consumption (%) 

Total Reduction in Petroleu

m Consumption (%)

Challenging for

HEV technology Prius (Corolla)

Civic

Accord

Highlander

Escape

Vue

HEV

PHEV20

PHEV40

PHEV60

Battery power

Battery energy

104
9

Vehicle Configurations

 conventional automatic

 pre-transmission parallel hybrid: 

HEV or PHEV

 2 technology scenarios

– near term and long term

PHEV Efficiency and Cost Model

Approach

 Dynamic, power-flow simulation

 Calculates component sizes and costs

 Iterative mass-compounding

 Measures fuel/electricity consumption using NREL-proposed 

revisions to SAE J1711

 Battery definition is key input to the simulation

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

10

Baseline Vehicle Characteristics – Midsize Sedan

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

Engine power 121 kW

Vehicle attributes

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Top speed 110 mph

Passing acceleration 40-60 mph in 5.3 s

Standing acceleration 0-60 mph in 8.0 s

Performance Parameters

Baseline accessory load 800 W elec. + 2900 W A/C

Rolling resistance coefficient 0.009

2.27m2 Frontal area

Drag coefficient 0.30

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Test Mass 1565 kg (136 kg load)

Curb Mass 1429 kg

Glider Mass 905 kg

Platform Parameters

MIDSIZE SEDAN (AUTOMATIC)

105
11

Powertrain Technology Scenarios

Efficiency 35% peak efficiency curve Same*

Cost EPRI Same*

Engine Near-Term Scenario Long-Term Scenario

Mass Based on MY2003 production engines Same*

Same

(assumes Li-Ion achieves same cycle life as NiMH)

SOC design curve based on JCI data

for NiMH cycle-life, see slide 11

SOC window

Efficiency Scaleable model based on P/E ratio Same

Packaging mass Delucchi Same

Module mass NiMH battery design function (Delucchi), see slide 12 Li-Ion battery design function (Delucchi), see slide 12

Packaging cost EPRI Same

Module cost Double EPRI projections, see slide 12 EPRI projections, see slide 12

Chemistry NiMH Li-Ion

Battery Near-Term Scenario Long-Term Scenario

Efficiency 95% peak efficiency curve Same

Mass DOE 2006 current status Based on GM Precept motor drive

Motor Near-Term Scenario Long-Term Scenario

Cost EPRI (near term) EPRI (long term)

* Engine technologies were not improved so as to isolate the benefits of improved plug-in hybrid technology

12

Battery Definition as Key Input to Simulation

kWh/mi

(from simulation)

SOC window

PHEV range

P/E ratio

Performance 

constraints

kWh usable

kWh total

kWmotor

kWengine

DOH

Benefit of 

plugging-in

Benefit of 

hybridization

Total MPG Benefit

mass compounding

Input parameters that define the battery in BLUE

DOH = degree of hybridization

106
13

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Design SOC window 

based on PHEVx

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Daily Mileage / PHEVx

Average daily SOC

swing based on daily

mileage distribution

Battery SOC Design Window

Daily mileage probability distribution

Battery SOC design curve for 15 year cycle life

14

Battery Models (Scaleable)

Battery Design Functions

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 20 40 60 80 100 120 140 160 180 200

Specific Energy (Wh/kg)

Specific Power (W /kg)

NiMH (near-term scenario)

LI-ION (long-term scenario)

2

50 20 10

5

Battery Cost Functions

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30

Power-to-Energy Ratio (1/h)

M odule Specific Cost ($/kW h)

NiMH (near-term)

Li-Ion (long-term)

107
15

Results: Battery Specifications

Midsize Sedans Long-term scenario Battery Power vs Energy for PHEVs

0

20

40

60

80

100

120

0 5 10 15 20 25 30

Total Battery Energy (kWh)

Battery Power (kW)

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

1

10 6 4

2

20

UDDS

all-electric

UDDS

blended

16

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

0 100 200 300 400 500

Reduction in Annual Fuel Consumption (gals.)

Powertrain Cost Increment

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

UDDS AER

vehicles

Results: Battery Specifications

Long-term scenario

LI-ION BATTERIES

108
17

PHEV Energy Use

UDDS AER PHEVs

27 mpg

PHEV Onboard Energy Use: Near and Long-Term Scenarios

0

100

200

300

400

500

600

Conventional HEV0 PHEV10 PHEV20 PHEV40

Annual Petroleum Consumption (gals)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Annual Electricity Consumption (kWh)

Near-Term: Petroleum

Long-Term: Petroleum

Near-Term: Electricity

Long-Term: Electricity

18

Powertrain Costs Comparison – Near Term

UDDS AER PHEVs

Powertrain Costs (incl. retail markups)

$4,004 $2,902 $2,995 $3,079 $3,203

$1,998

$2,000 $2,018 $2,035 $2,057

$2,166 $2,414 $2,516 $2,677

$3,907

$8,296

$12,889

$19,251

$663

$663

$663

$27,851

$21,181

$16,386

$10,976

$6,002

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

Conventional HEV0 PHEV10 PHEV20 PHEV40

Charging Plug

Battery

Motor/Inverter

Transmission

Engine

109
19

Powertrain Costs Comparison – Long Term

UDDS AER PHEVs

Powertrain Costs (incl. retail markups)

$4,004 $2,876 $2,925 $2,964 $3,013

$1,998

$1,994 $2,005 $2,012 $2,022

$1,680 $1,842 $1,882 $1,924

$2,523

$4,677

$6,740

$663 $9,626

$663

$663

$17,249

$14,261

$12,111

$9,073

$6,002

$-

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

Conventional HEV0 PHEV10 PHEV20 PHEV40

Charging Plug

Battery

Motor/Inverter

Transmission

Engine

20

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 5 10 15

Years after purchase

Cumulative Cost

PHEV40

PHEV20

PHEV10

HEV0

CV $3.00 / gal. (today)

Near-term scenario

NIMH BATTERIES

$0.09¢/kWh (2005 average)

Overall Cost Comparison for HEVs and PHEVs

Maintenance costs not included, no discount rate applied

110
21

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 5 10 15

Years after purchase

Cumulative Cost

PHEV40

PHEV20

PHEV10

HEV0

CV

Long-term scenario

LI-ION BATTERIES

$3.00 / gal. (today)

$0.09¢/kWh (2005 average)

Overall Cost Comparison for HEVs and PHEVs

Maintenance costs not included, no discount rate applied

22

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 5 10 15

Years after purchase

Cumulative Cost

PHEV40

PHEV20

PHEV10

HEV0

CV $5.00 / gal. (day after tomorrow??)

Long-term scenario

LI-ION BATTERIES

$0.09¢/kWh (2005 average)

Overall Cost Comparison for HEVs and PHEVs

Maintenance costs not included, no discount rate applied

111
23

Vehicle Costs cont.

Why might PHEV buyers pay more?

1. Tax incentives

2. Reduced petroleum use, air pollution and CO2

3. National energy security

4. Less maintenance

5. Reduced fill-ups

6. Convenience of home recharging (off-peak)

7. Improved acceleration (high torque of electric motors)

8. Green image, “feel-good factor”

9. Backup power

10. Vehicle-to-grid (V2G)

24

Conclusions

1. There is a very broad spectrum of HEV-PHEV designs.

2. Key factors in the HEV/PHEV cost-benefit equation include:

• Battery costs

• Fuel costs

• Control strategy (particularly battery SOC window)

• Driving habits (annual VMT and trip-length distribution)

3. Based on the assumptions of this study:

• HEVs can reduce per-vehicle fuel use by approx. 30%.

• PHEVs can reduce per-vehicle fuel use by up to 50% for PHEV20s and 65%

for PHEV40s.

• In the long term, powertrain cost increments are predicted to be $2-6k for 

HEVs, $7-11k for PHEV20s and $11-15k for PHEV40s assuming that 

projected component (battery) costs can be achieved.

• Note this study did not consider benefits from platform engineering (i.e.

mass/drag reduction).

112
25

Conclusions (cont.)

4. Based on overall costs (powertrain plus energy):

• At today’s fuel and powertrain component costs, conventional 

vehicles are the most cost-competitive.

• HEVs become the most cost-competitive EITHER if fuel prices

increase OR projected battery costs are achieved.

• PHEVs become cost-competitive ONLY if projected battery

costs are achieved AND fuel prices increase.

• Tax incentives and/or alternative business models (e.g. battery

lease) may be required for successful marketing of PHEVs

26

Next Steps

• Present this work at EVS22

• Expand the HEV-PHEV analysis space to include:

— Platform engineering (mass/drag reduction)

— Different performance constraints / component sizes

SAE 2007 paper

• Detailed simulation of promising PHEV designs:

— Real world driving patterns (e.g. St Louis data)

— Control strategy optimization

TRB 2007 paper

• Optimization of PHEV market competitiveness using

Technical Targets Tool

Ongoing analysis

113
Section 3 

Plug-In Hybrid Electric Vehicle Real-World Performance Expectations

The consumption of electricity and petroleum by a PHEV will be strongly influenced by the daily

distance traveled between recharge events and the aggressiveness of driving. Rather than rely on standard

test profiles for a prediction of PHEV fuel consumption, we have collaborated with municipalities to use 

existing drive cycle databases as inputs to our simulation models. The simulation results provide key

insights into consumer travel behavior and quantify the real-world potential for PHEVs to displace 

petroleum. The first data set was from the St. Louis, Missouri, metropolitan area and includes 227 unique 

driving profiles, with daily travel distances ranging from less than a mile to more than 270 miles. 

Conclusions from the travel survey data are:

• Approximately 50% of the vehicles traveled less than 29 miles a day. A PHEV with 20–30 miles 

of electric range capability provides sufficient energy to displace a large percentage of daily

petroleum consumption. Because many vehicles drive less than 30 miles a day, the battery of a 

PHEV with 30 or more miles of electric range capability would likely be under-utilized on a daily

basis. 

• The travel survey data demonstrated that there is a broad spectrum of driving behavior, varying

from short to long distances and from mild to aggressive driving intensities. The Urban 

Dynamometer Driving Schedule and Highway Fuel Economy Test driving profiles used for fuel 

economy reporting today fall short of capturing the typical driving behavior of today’s consumer. 

• Contrary to experience with hybrid electric vehicles, which typically deliver fuel economies 

significantly less than their rated values, simulations of real-world driving suggest that a large 

percentage of drivers of PHEVs will likely observe fuel economies in excess of the rated fuel

economy values. However, because of high power requirements in real-world cycles, drivers are 

unlikely to experience significant all-electric operation if PHEVs are designed for all-electric 

range on the Urban Dynamometer Driving Schedule. 

• If all vehicles in the travel survey “fleet” were PHEV20 vehicles designed for all-electric range 

on the Urban Dynamometer Driving Schedule, petroleum consumption would be reduced by 56% 

relative to a conventional vehicle fleet. The PHEV40 reduced consumption by an additional 12%

and was equivalent to ≈1 gal/vehicle/day of petroleum savings. Including electricity costs, the 

average annual fuel costs savings for the “fleet” of PHEVs is more than $500/vehicle/year. 

• The time-of-day usage pattern obtained from global positioning system (GPS) travel survey data 

and the recharge requirements from simulation will be extremely valuable for determining the 

impact of PHEV recharging scenarios on the electric utility grid. 

Since the St. Louis analyses were completed, data from five other metropolitan GPS travel surveys have 

been obtained. The driving profile database will expand from 227 to more than 2,000 vehicles. Additional 

analyses will be completed using the full collection of more than 2,000 driving profiles. Real-world travel

simulations will be executed to consider variations in platform, aerodynamics, performance, control, and 

recharge scenarios. In addition, the database will be used to explore the emissions control implications of

potential engine cold-starts and the fuel consumption impacts of location-specific air conditioning use. 

For more extensive discussion of this topic, please refer to sections 3.1, 3.2, and 3.3. 

 114
Section 3.1 

Title: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges”

Type: Presentation 

Author: Tony Markel 

Date: May 8, 2006 

Conference or Meeting: Clean Cities Congress and Exposition in Phoenix, Arizona 

Abstract: Discusses what a plug-in hybrid is, its potential benefits, and the key technical challenges to 

overcome

 115
Plug-in Hybrid Electric Vehicles

Current Status, Long-Term Prospects and Key Challenges

Presented at Clean Cities Congress and Expo

by

Tony Markel

National Renewable Energy Laboratory

May 8th, 2006

With support from the

U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

FreedomCAR and Vehicle Technologies Program

Presented at Clean Cities Congress and Expo held May 7-10, 2006 in Phoenix, Arizona

NREL/PR-540-40239

2

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy

(the “DOE”). The United States Government (the “Government”) retains

and the publisher, by accepting the work for publication, acknowledges

that the Government retains a non-exclusive, paid-up, irrevocable,

worldwide license to publish or reproduce the published form of this work

or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof,

nor any of their employees, makes any warranty, express or implied, or

assumes any liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights.

Reference herein to any specific commercial product, process, or service

by trade name, trademark, manufacturer, or otherwise does not constitute

or imply its endorsement, recommendation, or favoring by the

Government or any agency thereof. The views and opinions of the

authors and/or presenters expressed herein do not necessarily state or

reflect those of MRI, the DOE, the Government, or any agency thereof.

116
3

The Perfect Storm

• Petroleum consumption has steadily

increased while domestic production has

continued to decline

• World oil production predicted to peak

within the next 5-15 years

• Recent increase in gasoline price is

indicator of growing tension between 

supply and demand

WHAT’S OUR PLAN?

0

5

10

15

20

25

1940 1950 1960 1970 1980 1990 2000 2010

Petroleum (mmb/day)

Domestic Production

Domestic Consumption

Source: U.S. Department of Energy, Energy Information Administration

Gasoline price - 85% rise in 5 years!

Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews

0

0.5

1

1.5

2

2.5

3

3.5

Aug 11,

1987

May 07,

1990

Jan 31,

1993

Oct 28,

1995

Jul 24,

1998

Apr 19,

2001

Jan 14,

2004

Oct 10,

2006

Jul 06,

2009

Weekly National Average Gasoline

Retail Price ($/gal)

Source: U.S. Department of Energy, Energy Information Administration

4

A “Full” Hybrid

ELECTRIC ACCESSORIES

ADVANCED ENGINE

ENGINE IDLE-OFF

ENGINE DOWNSIZING

REGENERATIVE BRAKING

PETROLEUM ONLY

76hp gasoline engine, 67hp electric motor, 1.5kWh battery

117
5

A Plug-In Hybrid

ELECTRIC ACCESSORIES

ADVANCED ENGINE

ENGINE IDLE-OFF

ENGINE DOWNSIZING

REGENERATIVE BRAKING

ELECTRICITY

BATTERY RECHARGE

PETROLEUM

AND/OR

76hp gasoline engine, 67hp electric motor, 9.0kWh battery (30mi)

Fuel Flexibility

6

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

0

2

4

6

8

10

12

14

16

2000 2005 2010 2015 2020 2025 2030

Year

Oil Consumption (MPBD)

2005 AEO Base Case

HEV Scenario

Oil Use Reduction with HEVs

3 MBPD Oil use same as today!

This highly aggressive scenario assumes 100% HEV sales from 2010 onwards...

Produced using VISION model, MBPD = million barrels per day

HEVs unable to reduce consumption below today’s consumption level

118
7

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption

0

2

4

6

8

10

12

14

16

2000 2005 2010 2015 2020 2025 2030

Year

Oil Consumption (MPBD)

2005 AEO Base Case

PHEV Scenario

Oil Use Reduction with PHEVs

4 MBPD

This highly aggressive scenario assumes 100% HEV sales 

from 2010 and 50% PHEV40 sales from 2020 onwards...

Oil use reduction!

Produced using VISION model, MBPD = million barrels per day

PHEVs reduce oil consumption with a transition to electricity

PHEVs on E85 ??

8

OEM Plug-In Hybrids

2003 Renault Kangoo Elect’road

- up to 50mi electric range

- approximately 500 sold in Europe

DaimlerChrysler Sprinter PHEV

- 15 prototypes being produced for

testing in various locations in 

Europe and North America

- up to 20mi electric range

119
9

Other PHEV Prototypes - Industry

EnergyCS Plug-In Prius HyMotion Escape PHEV

AC Propulsion Jetta PHEV

AFS Trinity Extreme HybridTM

Esoro AG H301

10

Design Options

All-Electric vs Blended Strategy

• Engine turns on when power

exceeds battery power capability

• Engine only provides load that 

exceeds battery power capability

• Engine turns on when battery

reaches low state of charge

• Requires high power battery and 

motor

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

All-Electric

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

Blended

120
11

Household Travel Survey Data Can be Used to Predict

Real-World Benefits of Advanced Technologies

• Provides valuable insight into travel behavior

• GPS augmented surveys supply details needed 

for vehicle simulation

12

0 5 10 15 20 25 30 0

5

10

15

20

25

30

35

40

Time of Day (hr)

Percentage of Vehicle Fleet In Use (%)

0 5 10 15 20 25 300

50

100

150

200

250

300

350

400

Cu

mulative Fuel Consumed (gallons)

Conventional

Hybrid

PHEV20

PHEV40

PHEVs Reduce Fuel Consumption By 50%

On Real- World Driving Cycles

• 8647 total miles driven

• 100% replacement of

sample fleet

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

26 mpg

58 mpg &

140 Wh/mi

PHEVs:

~40% reduction in

operating costs

~$460 annual savings

37 mpg

76 mpg &

211 Wh/mi

Assumes $2.15/gal and 9¢/kWh

$1.08

$1.41

$2.21

$3.15

Gas.

Average Daily Costs

$0.72

$0.48

---

---

Elec.

4.7

5.0

5.8

8.3

¢/mi

PHEV40

PHEV20

HEV

CV

121
13

HEVs and PHEVs Likely to Reduce 

Greenhouse Emissions

Source: Hybrid Electric Vehicle Working Group, www.epri.com

14

Electrified Miles May Lead to Cleaner Operation

Source: Hybrid Electric Vehicle Working Group, www.epri.com

122
15

In-Use Simulations Show Reasonable Recharge Times 

with Standard Household Outlet

• Typical vehicle is used less than 5% of the time

— Lots of opportunity for recharging

• Both PHEV20 and PHEV40 owners likely to get 

full recharge overnight with standard outlet 

0 1 2 3 4 5 6 7 8 9 0

5

10

15

20

25

30

35

40

45

Recharge Duration @ 110V 15A (hr)

Percentage of Fleet (%)

PHEV40

0 50 100 150 200

5

10

15

20

25

Vehicle ID

Ti

me of Day (hr)

Vehicle In-Use (red)

16

Technical Challenges

Battery Life

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles

• Also need to consider combination of high and low frequency cycling

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20

4000

50%

70%

123
17

Technical Challenges

Battery Packaging

18

Technical Challenges

Vehicle Costs

Prius (Corolla)

Civic

Accord

Escape

Highlander

Vue

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Gasoline Savings (%)

Incremental Cost (%)

HEVs

PHEVs?

HOW CAN WE SAVE THE MOST GALLONS AT THE LEAST COST?

124
19

Conclusions

• Plug-in hybrid technology uses electricity from the utility

grid to reduce petroleum consumption beyond that of HEV

technology

— Predicted 50% reduction in in-use consumption based on 

simulations using travel survey data

• Industry interest is growing and some prototypes have 

been built

— Collaboration between labs and industry will likely lead to 

innovative systems solutions

• The U.S. Department of Energy is expanding its research

portfolio to include PHEVs

— Research will address key remaining barriers to commercial 

PHEVs including battery life, packaging, and cost

125
Section 3.2 

Title: “Plug-In Hybrid Vehicle Real-World Performance Expectations” 

Type: Presentation 

Author: Tony Markel 

Date: June 14, 2006 

Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team

Abstract: Summarizes the simulated performance of optimal PHEVs on a collection of real-world 

driving profiles from the St. Louis, Missouri, metropolitan area 

 126
Plug-in Hybrid Vehicle Real-world 

Performance Expectations

a presentation to FreedomCAR Vehicle Systems Analysis Tech Team

by

Tony Markel

National Renewable Energy Laboratory

June 14, 2006

With support from the

U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

FreedomCAR and Vehicle Technologies Program

2

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy

(the “DOE”). The United States Government (the “Government”) retains

and the publisher, by accepting the work for publication, acknowledges

that the Government retains a non-exclusive, paid-up, irrevocable,

worldwide license to publish or reproduce the published form of this work

or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof,

nor any of their employees, makes any warranty, express or implied, or

assumes any liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights.

Reference herein to any specific commercial product, process, or service

by trade name, trademark, manufacturer, or otherwise does not constitute

or imply its endorsement, recommendation, or favoring by the

Government or any agency thereof. The views and opinions of the

authors and/or presenters expressed herein do not necessarily state or

reflect those of MRI, the DOE, the Government, or any agency thereof.

127
3

Outline

• Objective

• Approach

• Assumptions

• Travel survey data

• Simulated in-use operation

• Conclusions

• Next steps

4

Objective and Justification

• Extend preliminary PHEV analyses to predict 

consumption and performance of vehicles in realworld applications

• Fuel consumption benefits and operation of 

PHEVs are highly dependent on usage pattern

— What are real usage patterns like?

— How do rated versus in-use fuel consumption and all

electric range compare?

128
5

Approach

• Consider PHEV20 and PHEV40 architectures

— Based on Presidential initiatives and recent cost/benefit analysis

— Component sizing and energy management strategy to provide all 

electric operation on urban profile

• Generate a collection of real-world driving profiles from St. Louis, Missouri metropolitan household travel survey data — Sample data from GPS units in support of larger written survey

• Optimize vehicle energy management strategy parameters — Vary electric launch speeds at high and low SOC set points and 

charge torque

— Minimize fuel consumption on UDDS, US06, HWFET, SC03

— Minimize the number of engine start/stop events

• Complete detailed cycle simulation — Use existing PHEV, HEV, and Conventional architecture models

— 24 hour driving profiles

» ~10 minutes per profile * 227 profiles = 38 hours (for one architecture)

— Include 4 architectures (Conventional, HEV, PHEV20, PHEV40)

• Summarize simulation results

6

Baseline Vehicle Characteristics

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

Engine power 121 kW

Vehicle attributes

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Top speed 110 mph

Passing acceleration 40-60 mph in 5.3 s

Standing acceleration 0-60 mph in 8.0 s

Performance Parameters

Accessory load 700 W elec. or 823 W mech.

Rolling resistance coefficient 0.009

2.27m2 Frontal area

Drag coefficient 0.30

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Test Mass 1565 kg (136 kg load)

Curb Mass 1429 kg

Glider Mass 905 kg

Platform Parameters

MIDSIZE SEDAN (AUTOMATIC) – MSRP $23,400

129
7

PHEV20 and PHEV40 Characteristics

• Additional assumptions

— 70% usable SOC window (stretch goal)

— Lithium ion technology

Attribute Units PHEV20 PHEV40 Conventional HEV

Curb Mass kg 1488 1567 1429 1399

Battery Power kW 47.0 51.8 n/a 50

Battery Energy kWh 9.4 18.5 n/a 1.9

Battery Power to

Energy Ratio -- 5 2.8 n/a 26.3

Motor Power kW 43.6 48.0 n/a 39

Engine Power kW 79.4 81.9 121.7 82

DOH % 35.4 37.0 n/a 32.2

8

Household Travel Survey Data

• Provides valuable insight into travel behavior

• GPS augmented surveys supply details needed 

for vehicle simulation

130
9

• St. Louis data set includes 227 vehicles from 147 households

• Complete second by second driving profile for one day

• 8650 miles of travel

• St. Louis data set is a small sample of real data 

• NPTS data is generated from mileage estimates

St. Louis Travel Data Analysis

Daily Driving Distance Similar to 1995 NPTS Data

0

2

4

6

8

10

12

0

0-5

5-10

10-1515-2020-2525-3030-35

35-4040-45

45-50

50-5555-6060-6565-7070-7575-8080-8585-9090-95

95-100

Daily Distance (mi)

Frequency (%)

0

10

20

30

40

50

60

70

80

90

100

Cummulative Frequency (%)

1995 NPTS Data

~33 mi

0

1

2

3

4

5

6

7

2468

101214161820222426283032343638404244464850525456586062646668707274767880828486889092949698

100

Daily Travel Distance (miles)

Frequency (%)

0

10

20

30

40

50

60

70

80

90

100

Cumulative Frequency (%)

Frequency (%)

Cumulative Freq. (%)

~29 mi

St. Louis HHTS Data

10

In-use PHEV Analysis 

• 227 unique driving profiles

• 4 vehicle architectures

— Conventional, HEV, PHEV20, and PHEV40

• Expectations

— PHEV20 should displace ~50% of the conventional

fleet consumption

— PHEVs will typically see fuel economy in excess of

the rated value

— There is a correlation between PHEV benefit and 

daily driving distance

— All electric range will be less in real driving profiles 

than on certification cycles

131
11

List of Results

• Fleet consumption summary

• Distribution of fuel economy

• Distribution of AER

• Distribution of EV miles

• Distribution of charge depleting miles fraction of 

total miles

• Distribution of charge sustaining miles fraction of

total miles

• Consumption comparison as a function of daily

distance

12

0 5 10 15 20 25 30 0

5

10

15

20

25

30

35

40

Time of Day (hr)

Percentage of Vehicle Fleet In Use (%)

0 5 10 15 20 25 300

50

100

150

200

250

300

350

400

Cu

mulative Fuel Consumed (gallons)

Conventional

Hybrid

PHEV20

PHEV40

PHEVs Reduce Fuel Consumption By >50%

On Real- World Driving Cycles

• 8647 total miles driven

• 100% replacement of

sample fleet

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

26 mpg

58 mpg &

140 Wh/mi

PHEVs:

>40% reduction in

energy costs

>$500 annual savings

37 mpg

76 mpg &

211 Wh/mi

Assumes $2.41/gal and 9¢/kWh

$1.21

$1.58

$2.48

$3.45

Gas.

Average Daily Costs

$0.72

$0.48

---

---

Elec.

5.1

5.4

6.5

9.1

¢/mi

PHEV40

PHEV20

HEV

CV

132
13

5 10 15 20 25 30 35 0

2

4

6

8

10

12

14

16

Fuel Economy (mpg)

Frequency (%)

Conventional

65.6% less than mean

15 20 25 30 35 40 45 0

2

4

6

8

10

12

14

16

18

20

Fuel Economy (mpg)

Frequency (%)

HEV

59.5% less than mean

0 50 100 150 200 250 300 0

5

10

15

20

25

30

Fuel Economy (mpg)

Frequency (%)

PHEV40

26.4% less than mean

0 50 100 150 200 250 300 0

5

10

15

20

25

Fuel Economy (mpg)

Frequency (%)

PHEV20

32.2% less than mean

Fuel Economy Distribution - Details

14

All Electric Range Distribution

• Vehicles were designed to operate all electrically

on UDDS

• Power demands of real profiles exceed UDDS peak 

power within the first few miles

1 3 5 7 9 11 13 15 17 19 21 23 25 0

5

10

15

20

25

30

All Electric Range (mi)

Percentage of Fleet (%)

PHEV20

0 5 10 15 20 25 30 35 40 0

5

10

15

20

25

30

35

40

All Electric Range (mi)

Percentage of Fleet (%)

PHEV40

133
15

Fuel Economy and All Electric Range Comparison 

• Difference between rated and median values are 

significant for the PHEVs

— Consumers likely to observe fuel economy higher

than rated value in typical driving

— Vehicles designed with all electric range likely to 

operate in a blended mode to meet driver demands

** Fuel economy values do not include electrical energy consumption

Rated Median Rated Median

Conventional 26 24.4 n/a n/a

HEV 39.2 35.8 n/a n/a

PHEV20 54 70.2 22.3 5.6

PHEV40 67.4 133.6 35.8 3.8

Fuel Economy (mpg) ** All Electric Range (mi)

16

Electric Vehicle Miles Distribution

50% of the PHEV20 

vehicles had 10 miles or

less in EV mode

Vehicles operate in a

blended mode most of

the time

1 3 5 7 9 11 13 15 17 19 21 23 0

2

4

6

8

10

12

14

Electric Range (mi)

Percentage of Fleet (%)

PHEV20

0 5 10 15 20 25 30 35 40 0

2

4

6

8

10

12

14

16

18

Electric Range (mi)

Percentage of Fleet (%)

PHEV40

134
17

EV Mode Usage

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

2

4

6

8

10

12

14

16

18

20

Fraction of Miles in EV Mode (--)

Percentage of Fleet (%)

PHEV40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

2

4

6

8

10

12

14

16

18

20

Fraction of Miles in EV Mode (--)

Percentage of Fleet (%)

PHEV20

18

Charge Depleting vs.Charge Sustaining Mode Operation – PHEV20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

5

10

15

20

25

30

35

40

Fraction of Miles in Charge Depleting Mode (--)

Percentage of Fleet (%)

PHEV20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

5

10

15

20

25

30

35

40

Fraction of Miles in Charge Sustaining Mode (--)

Percentage of Fleet (%)

PHEV20

0 50 100 150 200 250 0

5

10

15

20

25

30

35

40

45

Charge Sustaining Distance (mi)

Percentage of Fleet (%)

PHEV20

1 3 5 7 9 11 13 15 17 19 21 23 25 0

5

10

15

20

25

30

Charge Depleting Distance (mi)

Percentage of Fleet (%)

PHEV20

44.1% of

VMT in

charge

depleting

mode

135
19

Charge Depleting vs.Charge Sustaining Mode Operation – PHEV40

62.4% of

VMT in

charge

depleting

mode

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

10

20

30

40

50

60

70

Fraction of Miles in Charge Depleting Mode (--)

Percentage of Fleet (%)

PHEV40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

10

20

30

40

50

60

70

Fraction of Miles in Charge Sustaining Mode (--)

Percentage of Fleet (%)

PHEV40

0 50 100 150 200 250 0

10

20

30

40

50

60

70

Charge Sustaining Distance (mi)

Percentage of Fleet (%)

PHEV40

0 5 10 15 20 25 30 35 40 45 0

2

4

6

8

10

12

Charge Depleting Distance (mi)

Percentage of Fleet (%)

PHEV40

20

0 50 100 150 200 250 300 10

15

20

25

30

35

40

45

50

Percent Reduction in Consumption (%)

Daily Distance (mi)

HEV to Conventional

0 50 100 150 200 250 300 10

20

30

40

50

60

70

80

90

100

Percent Reduction in Consumption (%)

Daily Distance (mi)

PHEV20 to Conventional

0 50 100 150 200 250 300 0

10

20

30

40

50

60

70

80

Difference in Percent Reduction in Consumption (%)

Daily Distance (mi)

PHEV20 to HEV Relative to Conv.

PHEV benefits over 

HEV and Conv. tied to 

daily distance

Percent Consumption Reduction by Distance

136
21

Conclusions

• Simulations on sample real-world drive cycles 

suggests PHEV technology can dramatically

reduce petroleum consumption

• Benefits of a PHEV over a conventional or HEV 

are tied to travel behavior

• A vehicle designed for all electric range in urban 

driving will likely provide only limited electric

operation in real world applications

— Still provides significant fuel displacement

22

Next Steps

• Repeat analysis for PHEVs designed for blended 

operation on urban cycle

— Is the fuel consumption significantly different?

• Expand application of travel survey data

— Apply statistical expansion factors to represent population

— Access other metropolitan data sets

• Repeat simulations with alternative recharge scenarios

— Opportunity recharge

— Never recharge

• Analyze real world in-use emissions characteristics

— Daily catalyst temperatures and time of day vehicle emissions

137
Section 3.3 

Title: “Using GPS Travel Data to Assess the Real-World Driving Energy Use of Plug-In Hybrid 

Electric Vehicles” 

Type: Paper

Authors: Jeffrey Gonder, Tony Markel, Andrew Simpson, Matthew Thornton 

Date: February 2007 

Conference or Meeting: To be published at the Transportation Research Board 86th Annual Meeting in 

Washington, D.C. 

Abstract: Highlights the use of GPS travel survey data for simulating the potential real-world benefits of

PHEV technology

 138
Using GPS Travel Data to Assess the Real World Driving Energy Use of Plugin Hybrid Electric Vehicles (PHEVs) 

TRB 86th Annual Meeting, Washington, D.C., 2007 

By

Jeffrey Gonder 

National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 

Phone: 303-275-4462; Fax: 303-275-4415; jeff_gonder@nrel.gov

Tony Markel 

National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 

Phone: 303-275-4478; Fax: 303-275-4415; tony_markel@nrel.gov

Andrew Simpson 

National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 

Phone: 303-275-4430; Fax: 303-275-4415; andrew_simpson@nrel.gov

Matthew Thornton 

National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 

Phone: 303-275-4273; Fax: 303-275-4415; matthew_thornton@nrel.gov

Word Count

• 4695 words, plus 5 figures and 1 table 

• 6195 total “words” < 7,500 “word” limit 

Submitted August 1, 2006 

139
Gonder, Markel, Simpson and Thornton 

Using GPS Travel Data to Assess the Real World Driving Energy Use of Plugin Hybrid Electric Vehicles (PHEVs)

1

Jeffrey Gonder, Tony Markel, Andrew Simpson, and Matthew Thornton 

National Renewable Energy Laboratory 

ABSTRACT 

Hybrid electric and plug-in hybrid electric vehicles provide an avenue toward improved vehicle efficiency

and significant reductions in petroleum consumption in the transportation sector. Knowledge of typical 

driving behavior is critical for such advanced vehicle design. Detailed travel survey data collected using

GPS units for 227 unique consumer vehicles in the St. Louis metropolitan area was used to assess the fuel

consumption and operating characteristics of conventional, hybrid electric, and plug-in hybrid electric

vehicle technologies under real-world usage patterns. In comparison to standard cycles used for 

certification procedures, the travel survey duty cycles include significantly more aggressive acceleration 

and deceleration events across the velocity spectrum, which impacts vehicle operation and efficiency. 

Even under these more aggressive operating conditions, a plug-in hybrid electric vehicle using a blended 

charge-depleting energy management strategy would be expected to consume less than 50% of the 

petroleum used by a similar conventional vehicle. This study highlights new opportunities for using

available GPS travel survey data and advance vehicle systems simulation tools to improve vehicle design

and maximize the benefits of energy efficiency technologies. 

INTRODUCTION 

The United States (U.S.) is faced with a transportation energy problem. The transportation sector is 

almost entirely dependent on a single fuel – petroleum. The future of petroleum supply and its use as the 

primary transportation fuel threatens both personal mobility and economic stability. The U.S. currently

imports nearly 60% of the petroleum it consumes and dedicates over 60% of its petroleum consumption 

to transportation (1). As domestic production of petroleum steadily declines while U.S. consumption 

continues to climb, imports will also continue to increase. Internationally, the growing economies of

China and India continue to consume petroleum at rapidly increasing rates. Many experts are now 

predicting that world petroleum production will peak within the next 5-10 years (2). The combination of

these factors will place great strain on the supply and demand balance of petroleum in the near future. 

Hybrid electric vehicle (HEV) technology presents an excellent way to reduce petroleum

consumption through efficiency improvements. HEVs use energy storage systems combined with electric 

motors to improve vehicle efficiency by enabling engine downsizing and by recapturing energy normally

lost during braking events. A typical HEV will reduce gasoline consumption by about 30% over a 

comparable conventional vehicle. This number could approach 45% with additional improvements in 

aerodynamics and engine technology. Since their introduction in the U.S., HEV sales have grown at an 

average rate of more than 80% per year. However, after 5 years of availability, they represent only 0.1% 

of the total U.S. vehicle fleet. There are 237 million vehicles on the road today and more than 16 million 

new vehicles sold each year (3). Each new vehicle (the vast majority of which are non-hybrids) will

likely be in-use for more than 15 years (4). With continued growth in the vehicle fleet and in average 

vehicle miles traveled (VMT) even aggressive introduction rates of efficient HEVs to the market will only

slow the increase in petroleum demand. Reducing U.S. petroleum dependence below present levels

requires vehicle innovations beyond current HEV technology.

1

 This work has been authored by an employee or employees of the Midwest Research Institute under Contract No. 

DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government retains and the 

publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow

others to do so, for United States Government purposes. 

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Gonder, Markel, Simpson and Thornton 

Plug-in hybrid electric vehicle (PHEV) technology is an option with the potential to displace a 

significant portion of transportation petroleum consumption by using electricity for portions of trips. A 

plug-in hybrid vehicle is an HEV with the ability to recharge its energy storage system with electricity

from the utility grid. With a fully-charged energy storage system, the vehicle will bias towards using 

electricity over liquid fuels. A key benefit of plug-in hybrid technology is that the vehicle is no longer 

dependent on a single fuel source. The primary energy carrier would be electricity generated using a 

diverse mix of domestic resources including coal, natural gas, wind, hydro, and solar energy. The 

secondary energy carrier would be a chemical fuel stored on the vehicle (i.e. gasoline, diesel, ethanol, or

even hydrogen). 

PHEV technology is not without its own technical challenges. Energy storage system cost, 

volume, and life are the major obstacles that must be overcome for these vehicles to succeed. 

Nonetheless, this technology provides a relatively near-term possibility for achieving petroleum

displacement. One of the key factors in assessing the potential fuel use reductions of PHEVs is to assess 

their fuel use relative to both conventional vehicles and other advance technology vehicles, such as

HEVs. This would traditionally be accomplished using controlled chassis dynamometer testing over 

standardized certification cycles (i.e. Urban Dynamometer Driving Schedule (UDDS), Highway Fuel 

Economy Test (HWFET) and the US06 Cycle). Several past studies have evaluated the fuel economy

benefits from simulated PHEVs over such certification cycles (5, 6). Although these standard test cycles 

are widely accepted for this type of analysis, the results are somewhat limited since the cycles do not

necessarily represent actual driving behavior. One alternative option is to use real-world vehicle usage to

generate vehicle driving profiles from which to accurately predict the fuel consumption benefits of 

advanced technology vehicles. Consideration of real-world usage is of particular importance for the 

evaluation of PHEVs since their fuel and electricity use are highly sensitive to cycle distance and 

intensity. 

The drawback of real-world performance evaluation is often the availability of good travel data. 

Typical paper-based travel surveys do not provide sufficiently detailed information to perform advanced 

vehicle simulations. However, the second-by-second information on vehicle position, heading and speed 

collected from global positioning system (GPS) technology can be used to create real-world drive cycles. 

GPS technology uses satellite signals to track the location of vehicles, and can enhance traditional travel 

survey data collection methods. The purpose of this paper is to illustrate the use of GPS travel data for 

assessing the fuel use of PHEV technologies in a real would context. This will be accomplished by

comparing the liquid fuel and electric energy use of conventional vehicles, HEVs, and PHEVs, simulated

over certification cycles with that of a simulated fleet over real world driving activity sampled from an 

actual urban fleet using GPS technology.

OBTAINING DRIVE CYCLES FROM GPS TRAVEL SURVEY DATA 

Metropolitan planning organizations (MPOs) regularly collect data in travel surveys in order to update

transportation demand forecasting models and identify transportation needs and areas of traffic congestion

within the survey region. These surveys typically consist of mail out/mail back travel diaries, and may

also include a computer-assisted telephone interview (CATI) component. In recent years, several MPOs 

have begun investigating use of GPS technology in order to improve the accuracy and completeness of

personal travel data collection. The first significant deployment of GPS equipment in a travel survey

occurred in Austin in 1998. However, the usefulness of the data was limited due to the U.S. 

government’s intentional degradation of GPS data signals (known as “Selective Availability”). On May

1, 2000 President Clinton announced the termination of Selective Availability, which led to ten-fold 

improvements in GPS data accuracy down to 5-10 meters, literally overnight. It has only been in the 

relatively recent time period following this change that continually improving GPS accuracy and 

declining equipment costs have made it practical to include a GPS data collection component in

traditional travel surveys. 

The East-West Gateway Coordinating Council (EWGCC) became one of the initial pioneers by

including a GPS component in the 2002 St. Louis Regional Travel and Congestion Survey. The GPS 

141
Gonder, Markel, Simpson and Thornton 

devices in the St. Louis study were used to investigate trip characteristics of a sub-sample of participants

in the larger survey. The purpose of comparing the GPS and CATI survey results was to gain insight into 

the extent of under- or mis-reported trips, which has long been a problem with household travel studies 

due to the self-reporting nature of traditional survey methods. For more details on the full survey and the 

GPS augment than is provided in this section, the interested reader should consult the final reports on the 

St. Louis survey methodology and results (7, 8). 

In the full St. Louis survey, 5,094 households were used to represent 968,533 households and 

2,428,730 persons in the region. Households participating in the GPS augment to the survey were 

provided a GeoStats GeoLogger

TM data collection device for up to three vehicles in the household. To

utilize the data logger the survey participant needed only to plug the power connector into the vehicle’s 

cigarette lighter socket, and use a magnetic mount to attach the combination GPS receiver/antenna to the 

roof of the vehicle. To filter out non-movement events, each GeoLogger was set to record data points for 

vehicle speeds greater than 1 mph at one-second logging frequencies. The data recorded included date, 

time, latitude, longitude, speed, heading, altitude, number of satellites, and horizontal dilution of precision 

(HDOP, a measure of positional accuracy). A 150 household sub-sample of the 5,094 households 

successfully completing the CATI survey also successfully completed the GPS portion of the survey. 

From these 150 households, 300 vehicles received GPS instrumentation, and 227 of these 300 vehicles

recorded travel on the assigned travel day (a weekday between September 5 and December 12, 2002). 

To satisfy the additional use of the GPS data identified in this paper, the second-by-second speed

vs. time history logged by the 227 vehicles was converted into a set of 24-hour driving profiles to 

describe the behavior of these vehicles over a full day. The authors then used a vehicle simulation model 

to predict the performance and energy consumption of a simulated vehicle operating over the driving

profiles. Simulation or testing time constraints often necessitate constructing a single composite cycle to 

approximately represent such a large set of driving profiles. However, the rapid computational speed of 

the model described in the following section enables individual simulation of vehicle performance over 

each distinct 24-hour drive cycle. The subsequent fleet-level simulation result enhances understanding of 

the distribution and details of vehicle performance that a single composite cycle cannot provide. 

The sample distribution for the GPS portion of the St. Louis survey was designed to mirror the 

population breakdown in the St. Louis area as well as the full study sample distribution. However, 

extracting conclusions about the larger St. Louis population from the simulation results using GPSderived drive cycles requires first applying expansion factors to appropriately weight each category of 

vehicle/household based on its proportion in the larger population (based on household size, location,

income, etc). Because the GPS sub-sample represents such a small portion of the total population, the 

uncertainties associated with applying expansion factors could potentially be large. Nevertheless, 

analyzing vehicle performance over hundreds of real-world drive cycles can certainly provide expanded 

insight over solely simulating a handful of synthetic profiles. By referencing recent travel surveys that

include a GPS component, large numbers of real-world simulation cycles can be extracted from data 

originally obtained for another purpose, thus furthering the usefulness of the survey information.

PREPARING AND RUNNING VEHICLE SIMULATIONS 

The driving profiles extracted from the travel survey data were used to assess the real-world performance 

expectations of PHEVs. The second-by-second speed profiles for each vehicle were provided as input to a 

vehicle systems simulation tool. ADVISORTM was the particular software tool used for the detailed 

vehicle simulations. This program was developed at the National Renewable Energy Laboratory with 

support from the U.S. Department of Energy and has been refined over many years. The tool provides

sufficient detail to understand the impact of component sizing and energy management decisions yet is 

fast enough to analyze 24 hours of driving for a fleet of vehicles in a reasonable amount of time. 

ADVISOR is a vehicle systems simulation tool used to assess the fuel consumption and 

performance of advanced technology vehicles such as hybrid electric, fuel cell, and plug-in hybrid electric 

vehicles (9). It also includes models for conventional and electric vehicles. The driving profile serves as a 

key input along with component attributes. Starting from the acceleration demands of the driving profile, 

142
Gonder, Markel, Simpson and Thornton 

ADVISOR determines the operating point (torque and speed or current and voltage) of each component 

within the powertrain at each instant in time while accounting for component losses and limitations. 

ADVISOR has the ability to model a variety of powertrain configurations including parallel, series, and 

power-split hybrid architectures. For this study all hybrids have been assumed to be in a parallel

architecture so that the engine and electric motor can both provide power in parallel to the drive shaft at 

any time. 

A previous paper (10) analyzed the various design options for plug-in hybrid electric vehicles. 

The two primary PHEV design parameters are the usable energy content of the battery and the rated 

power of the battery. All other parameters depend on the choice of battery power and energy, the vehicle 

attributes, and the performance constraints. For this study, the vehicles were assumed to be representative 

mid-size sedans (similar to a Chevrolet Malibu or a Toyota Camry) with performance that would be 

competitive in today’s market. Table 1 summarizes the attributes of the vehicles considered. 

TABLE 1 Simulated Vehicle Attributes 

Units Conventional Hybrid PHEV20 PHEV40

Engine Power kW 121.7 82 79.4 81.9 

Motor Power kW n/a 39 43.6 48 

ESS Power kW n/a 50 47 51.8 

ESS Energy

(total) kWh n/a 1.9 9.4 18.5 

Curb Mass kg 1429 1399 1488 1567 

Fuel Economy

(urban/highway) mpg 26 39.2 54 67.4 

Electric 

Consumption 

(urban/highway) 

Wh/mi n/a n/a 95 157 

All Electric 

Range – urban miles n/a n/a 22.3 35.8 

The increased mass of the PHEV will increases its energy consumption rate. However, the larger 

energy storage system allows it to use the electric drivetrain more often to provide an overall energy

efficiency improvement and petroleum displacement benefit. The energy storage system and traction 

motor have been sized to provide sufficient power to drive the entire UDDS without the use of the engine. 

The distance a PHEV can drive on a particular cycle before having to turn on its engine is known as the

all-electric range. The PHEV20 in this study has been sized with sufficient energy to drive ~20 miles on

the UDDS without the use of the engine. Likewise, the PHEV40 has sufficient energy to drive ~40 miles 

on the UDDS. On other more aggressive cycles the all-electric operation will be less than as designed for

urban travel since the engine will need to supplement the electric motor power output in order to follow 

the driving profile. 

The energy management strategy for the PHEVs in this study will attempt to run all-electrically

(without the use of a combustion engine) as much as possible as long as the battery has sufficient energy.

However, if the electric drivetrain power is insufficient to satisfy the immediate needs of the driver the 

combustion engine will be used to supplement the electric drivetrain. As the stored energy in the battery

becomes depleted the vehicle will transition into a charge-sustaining mode, in which the engine will

become the primary power source and the stored energy will be used to allow the engine to operate as

efficiently as possible. This is referred to as a blended charge-depleting strategy (11) or as an electric 

vehicle centric strategy (12) and is intended to provide as much petroleum displacement as possible for a 

given set of components. 

143
Gonder, Markel, Simpson and Thornton 

ANALYSIS RESULTS 

The 227 unique driving profiles derived from the St. Louis GPS survey together represent 8650 

miles of travel. Figure 1 shows the distribution of daily distance traveled for this sample data set. 

Approximately 5% of the vehicles traveled more than 100 miles with the one vehicle traveling 270 miles.

PHEV fuel efficiency and petroleum displacement impact is strongly associated with the daily distance 

traveled between recharge events. 

0

2

4

6

8

10

12

0-4

4-8

8-12

12-16

16-20

20-24

24-28

28-32

32-36

36-40

40-44

44-48

48-52

52-56

56-60

60-64

64-68

68-72

72-76

76-80

80-84

84-88

88-92

92-96

96-100

>100

Distance (mi)

Frequency (%)

0

10

20

30

40

50

60

70

80

90

100

C

umulative Frequency (

%)

FIGURE 1 Daily driving distance distribution for 227 vehicles in the St. Louis metropolitan area. 

FIGURE 2 Comparison between acceleration characteristics of real-world and standard driving 

cycles. 

In addition to the daily driving distance, the real data provides valuable insight into driving

behavior. As it relates to vehicle design, the rate of acceleration and the speed at which this acceleration 

occurs is critical in determining the required power capabilities of the hybrid vehicle components. In

Figure 2, acceleration is plotted against speed for the entire set of vehicles in the sample real-world data 

set on the left and for three standard driving profiles on the right. The UDDS and HWFET are used to

represent typical urban and highway driving by the Environmental Protection Agency (EPA) for the 

144
Gonder, Markel, Simpson and Thornton 

purposes of standardized labeling of vehicle fuel economies. The US06 cycle includes more high speed 

and more aggressive accelerations than either the UDDS or HWFET. EPA has proposed using results 

from the US06 to improve vehicle fuel economy labeling to be more representative of what consumers 

might expect to see in-use. This figure clearly shows that even the US06 cycle does not fully encompass 

the range of accelerations seen in this real-world driving sample. 

The four vehicles described in Table 1 were each simulated over all 227 driving profiles. Figure 

3 presents a summary of the simulation results. The vertical bars are associated with the left axis and 

represent the percentage of the 227 vehicles in-use (driving) throughout the day. Morning, mid-day and 

evening peaks in usage are clearly observed. The lines represent the cumulative liquid fuel consumed 

over the course of the day by the entire fleet of 227 vehicles assuming all vehicles are the specified 

architecture. The chart suggests that HEV technology was able to reduce fuel consumption by about 29% 

relative to the conventional case. The PHEV20 technology reduced consumption by almost 55% and the 

PHEV40 reduced consumption by about 66%. The PHEVs are able to displace this level of petroleum

because they attempt to use the stored electrical energy to propel the vehicle as much as possible within

the component limits discussed previously. 

In addition to gasoline consumption, the PHEVs also consume electrical energy. The fleet 

consumed 1212 kWh and 1821 kWh for the PHEV20 and PHEV40 configurations respectively. Since the 

vehicles utilize two different energy sources, it is useful to compare the vehicle configurations on the 

basis of total energy costs. Assuming costs of $2.41/gallon for gasoline and $0.09/kWh for electricity

(national averages for 2005) results in average operating costs of 9.1c/mile for the Conventional fleet, 

6.5c/mile for the HEV fleet, 5.4c/mile for the PHEV20 fleet and 5.1c/mile for the PHEV40 fleet. 

The simulation results indicate that PHEVs would provide substantial petroleum displacement 

benefits and reduce vehicle fuel costs for this “real-world fleet.” The reduced engine use, particularly

during the morning commute in Figure 3 (when emissions provide the greatest contribution to smog

formation) indicates that PHEVs may also provide an emissions benefit. However, further simulations 

will be necessary to quantify the emissions impact of PHEV technology since vehicle emissions are 

highly dependent on transient and on/off engine operation. 

145
Gonder, Markel, Simpson and Thornton 

FIGURE 3 In-use activity pattern for 227 vehicles; cumulative fleet and average vehicle 

consumption results for four vehicle technologies. 

Figure 3 showed the consumption results for the entire fleet of 227 vehicles as a single result. 

Because detailed simulations were completed for each vehicle, it is also possible to examine the specific 

vehicle-level simulation results. Figure 4 shows the distribution of fuel consumption values for all of the 

vehicles in each of the four configuration scenarios. The bars represent the percentage of the fleet of 227 

vehicles that achieved fuel consumption values within the indicated consumption range (each bar 

represents a range of 0.5L/100km). The vertical lines represent the fuel use on standard cycles (red=US06 

and green=City/Highway composite). The city and highway composite value is determined by weighting

the UDDS fuel economy by 55% and the HWFET fuel economy by 45%. In addition, the PHEV

certification cycle values include a utility factor of 0.35 for the PHEV20 and 0.5 for the PHEV40 in order 

to account for the split between charge depleting and charge sustaining operation that a once-daily

charged PHEV experience based on national driving statistics (13). The weighting factors are intended 

translate measured certification cycle results into realistic in-use values. 

The first important insight from Figure 4 is that a large portion of the PHEV20 and PHEV40 

vehicles have real-world fuel consumption values much lower than those predicted by standard 

certification cycles, whereas a large portion of the conventional and HEV results are greater than the 

corresponding standard cycle results. From the perspective of most drivers in this real-world fleet, this 

result suggest that a PHEV is likely to over-deliver on fuel efficiency expectations while conventional and 

HEVs will likely under-deliver. A second important observation is that for both the PHEV20 and the 

PHEV40 nearly all vehicles in the fleet have fuel consumption values less than all of the conventional or 

146
Gonder, Markel, Simpson and Thornton 

US06

33.2 mpg (7.1 L/100km)

US06

51.5 mpg (4.57 L/100km)

US06

39.6 mpg (5.94 L/100km)

US06

24.6 mpg (9.55 L/100km)

HEV vehicles. Whereas Figure 3 indicated PHEVs provide a large average fleet petroleum consumption 

benefit, this result suggests that the reduced petroleum consumption experienced across the range of 

individual vehicle real-world driving profiles. 

0 5 10 15 20 25

0

10

20

30

40

Fuel Consumption (L/100km)

P

erc

e

nta

g

e of Fle

et (%)

Conventional

0 5 10 15 20 25

0

10

20

30

40

Fuel Consumption (L/100km)

P

erc

e

nta

g

e of Fle

et (%)

Hybrid

0 5 10 15 20 25

0

10

20

30

40

PHEV20

Fuel Consumption (L/100km)

Perc

e

ntage of Fl

e

et (

%)

0 5 10 15 20 25

0

10

20

30

40

Fuel Consumption (L/100km)

Perc

e

ntage of Fl

e

et (

%)

PHEV40

Real-world

City/Highway

Composite

39.2 mpg (6.0 L/100km)

FIGURE 4 Comparison of fuel consumption distributions for various vehicle architectures. 

Much recent discussion of plug-in hybrids has focused on the ability of the vehicles to operate 

without the use of the combustion engine (to maximize the previously defined “all-electric range” of the 

vehicle). As stated for this analysis, the PHEV20 and PHEV40 were respectively designed to provide 20 

miles and 40 miles of all-electric range capability on the UDDS cycle. Since the examined real-world

cycles were shown in Figure 2 to be much more aggressive than the UDDS, the actual in-use all-electric

range could be substantially less than the designed 20 and 40 mile distances. Figure 5 confirms that a 

large percentage of the vehicles achieved less than 5 miles of all-electric range over the real-world cycles. 

Even so, it is important to recognize that these vehicles achieve significant petroleum displacement 

without necessarily realizing substantial all-electric range. 

147

City/Highway

Composite

26 mpg (9.05 L/100km)

Real-world

City/Highway

Composite

54 mpg (4.35 L/100km)

City/Highway

Composite

67.4 mpg (3.49 L/100km)

Real-world Real-world
Gonder, Markel, Simpson and Thornton 

FIGURE 5 In-use all-electric range performance of plug-in hybrid vehicles designed for urban 

cycle all-electric range. 

CONCLUSIONS 

Relatively recent advances in GPS technology and reductions in equipment cost have enabled 

metropolitan planning organizations to begin incorporating a GPS component into the travel surveys they

conduct. GPS helps enhance the data collected for the intended transportation planning purpose, but also

presents an opportunity for new uses of this existing data due to the enhanced temporal resolution on

individual vehicle driving profiles. In particular, vehicle simulation tools can utilize the resulting secondby-second drive cycles to make predictions on how different vehicle technologies will perform under the 

real-world driving conditions captured by the GPS survey. The speed and accuracy of modern vehicle 

system simulation tools, such as the ADVISOR software discussed in this paper, enable direct simulation 

of a range of vehicle technologies over each individual 24-hour driving profile collected in the survey. 

Examining performance over a range of real-world cycles provides insights beyond only conducting a 

small number of simulations over “standard” cycles or an aggregate cycle intended to “represent” the 

spectrum of real-world cycles collected. 

For the specific example of PHEV technology, the detailed GPS drive cycles can provide 

information on the time of day and duration of different driving behaviors, as well as where the vehicle 

parks when not in use. This information can be used to predict different charging scenarios that a PHEV

fleet might experience throughout a given day. Further analysis (not directly discussed in this paper)

could examine the implications of fleet recharging on the electrical utility grid in addition to the impacts 

on the vehicles themselves. Real-world fleet driving data can also help quantify the range of vehicle 

operation patterns and acceleration intensities. This information can help vehicle designers make more 

informed design decisions, such as understanding for a PHEV how much all-electric range actual drivers 

are likely to experience for a given motor size. Finally, the detailed fleet driving simulations can be used 

to predict the benefit that advanced vehicle technologies (which can be sensitive to driving type and 

distance) could provide in real-world use. Over the 227 drive profiles taken from the St. Louis GPS 

survey, the simulations in this paper predict that replacing a fleet of conventional midsize vehicles with a 

fleet of comparable PHEVs would provide an approximately 50% reduction in petroleum use. 

The next steps planned for this analysis include segregating the St. Louis drive cycle simulations 

by vehicle platform. Because the survey collected information on the make and model of each 

instrumented vehicle, it is possible to analyze the impact of replacing the conventional midsize vehicles 

with midsize PHEVs, the conventional sport utility vehicles with sport utility PHEVs, etc (rather than

assuming all vehicles in the fleet are identical). The analysis can also go further to examine any

correlation between vehicle or household attributes and driving behavior. Another key supplement to the 

analysis will be application of statistical expansion methods in order to define what conclusions about the 

larger St. Louis area can be drawn from the GPS drive cycle simulation results. Because the 227 vehicle 

sample is a relatively small subset of the vehicles in the area, the uncertainties accompanying any data 

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Gonder, Markel, Simpson and Thornton 

expansion will be fairly large. It is, therefore, also important to continue seeking out more and larger 

GPS data sets on which to perform similar simulations. Decreasing GPS survey costs and formation of 

partnerships between multiple users of the collected data will help increase the scope and quality of the

GPS driving information and subsequent results expansion. Such expanded use of GPS data could help 

more accurately predict how specific advanced technologies will benefit a particular area. 

ACKNOWLEDGEMENT 

The authors would like to acknowledge the programmatic support of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program.

Additionally, we’d like to thank Jean Wolf of GeoStats and Todd Brauer of East-West Gateway Council

of Governments for providing access to the GPS travel survey data. 

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11. Markel, T. and A. Simpson. Energy Storage Considerations for Grid-Charged Hybrid Electric 

Vehicles. IEEE Vehicular Technologies Conference. Chicago, IL. September 7-9, 2005. 

12. O’Keefe, M. and T. Markel. Dynamic Programming Applied to Investigate Energy Management 

Strategies for a Plug-In HEV. To be published at 22nd International Battery, Hybrid and Fuel Cell 

Electric Vehicle Symposium & Exposition (EVS-22). Yokohama, Japan. October 23 - 28, 2006. 

13. Gonder, J. and A. Simpson. Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric 

Vehicles. To be published at 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle 

Symposium & Exposition (EVS-22). Yokohama, Japan. October 23 - 28, 2006. 

149
Section 4 

Plug-In Hybrid Electric Vehicle Energy Management Strategies 

Discussion in many PHEV forums has focused on how the PHEV will function and, more specifically, 

on how the vehicle will use the battery and engine in combination to improve efficiency and 

displace petroleum.

NREL’s vehicle systems analysis team has a long history of applying optimization tools to explore hybrid

electric vehicle energy management strategies. During the past fiscal year, two parallel efforts were 

initiated. The first explored the extensive PHEV design space and identify promising regions (using the 

modeling techniques developed for the cost-benefit study). The second applied dynamic programming

techniques to determine the “near optimal” power distribution among the engine, motor, and battery in a 

PHEV for a known driving profile. NREL’s energy management strategy work is critical for maximizing

the petroleum savings while protecting the batteries of future hybrid vehicles. 

The conclusions from these analyses are: 

• The misconception that a PHEVx must drive using electricity for the first x miles and then use the 

engine for the remaining travel must be clarified. This is one strategy that a manufacturer may

choose to pursue, but it is not the only strategy. As long as the strategy is achieving a net 

discharge of the battery, petroleum will be displaced, regardless of whether the vehicle is 

operated on battery only or on a combination of battery and engine power (known as a “blended” 

control strategy). 

• The selection of strategy and component sizing are not entirely independent. Reducing the rated 

power and size of the electric traction components is one way to reduce the cost of a PHEV. 

Reducing electric components also necessitates the use of a “blended” strategy. The “blended” 

strategy can still utilize electric propulsion to the maximum extent possible to minimize the 

vehicle’s instantaneous fuel use. NREL’s analysis shows that a PHEV with electric traction 

components half the size (based on power) of an all-electric PHEV can provide nearly the same 

petroleum reduction as an all-electric PHEV. 

• Dynamic programming optimization of PHEV energy management strategies indicated that

optimum control based on a priori knowledge of the driving cycle provided marginally better 

petroleum savings than a strategy that used stored electric energy to the greatest extent possible. 

On the other hand, if the real-world driving distance turned out to be less than that predicted for 

dynamic programming, then the “optimally” blended strategy would consume significantly more 

fuel than the electric energy-focused strategy over the length of the shortened driving distance. 

Note, however, that the simulations supporting these results were limited to repetitions of

identical drive cycles. It is possible that drive cycle variation (e.g., an urban followed by a 

highway followed by an urban pattern) may impact this conclusion. 

As PHEV technology evolves, energy management strategy will become increasingly important. It will be 

used to ensure satisfactory battery life, maximize petroleum displacement, gain performance 

improvement, and manage vehicle thermal and emissions transients. NREL’s future work will apply

optimization to more varied driving scenarios and include aspects beyond fuel displacement in the 

objective function. 

For more extensive discussion of this topic, please refer to sections 4.1, 4.2, and 4.3. 

 150
Section 4.1 

Title: “Plug-In Hybrid Electric Vehicle Energy Storage System Design”

Type: Presentation 

Authors: Tony Markel and Andrew Simpson 

Date: May 19, 2006 

Conference or Meeting: Presented at the Advanced Automotive Battery Conference in Baltimore, 

Maryland

Abstract: Discusses the system design trade-offs of battery sizing and control as they relate to cost, fuel

economy, and life 

 151
Plug-in Hybrid Electric Vehicle Energy

Storage System Design

Advanced Automotive Battery Conference 

by

Tony Markel and Andrew Simpson

National Renewable Energy Laboratory

May 19th, 2006

With support from the

U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

FreedomCAR and Vehicle Technologies Program

Presented at the Advanced Automotive Battery Conference held May 17-19, 2006 in Baltimore, Maryland

NREL/PR-540-40237

2

Disclaimer and Government License 

This work has been authored by Midwest Research Institute (MRI) under

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy

(the “DOE”). The United States Government (the “Government”) retains

and the publisher, by accepting the work for publication, acknowledges

that the Government retains a non-exclusive, paid-up, irrevocable,

worldwide license to publish or reproduce the published form of this work

or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof,

nor any of their employees, makes any warranty, express or implied, or

assumes any liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or

represents that its use would not infringe any privately owned rights.

Reference herein to any specific commercial product, process, or service

by trade name, trademark, manufacturer, or otherwise does not constitute

or imply its endorsement, recommendation, or favoring by the

Government or any agency thereof. The views and opinions of the

authors and/or presenters expressed herein do not necessarily state or

reflect those of MRI, the DOE, the Government, or any agency thereof.

152
3

The Perfect Storm

• Petroleum consumption has steadily

increased while domestic production has

continued to decline

• World oil production predicted to peak

within the next 5-15 years

• Recent increase in gasoline price is

indicator of growing tension between 

supply and demand

WHAT’S OUR PLAN?

0

5

10

15

20

25

1940 1950 1960 1970 1980 1990 2000 2010

Petroleum (mmb/day)

Domestic Production

Domestic Consumption

Source: U.S. Department of Energy, Energy Information Administration

Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews

Gasoline price - 85% rise in 5 years!

0

0.5

1

1.5

2

2.5

3

3.5

Aug 11,

1987

May 07,

1990

Jan 31,

1993

Oct 28,

1995

Jul 24,

1998

Apr 19,

2001

Jan 14,

2004

Oct 10,

2006

Jul 06,

2009

Weekly National Average Gasoline

Retail Price ($/gal)

Source: U.S. Department of Energy, Energy Information Administration

4

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

0

2

4

6

8

10

12

14

16

2000 2005 2010 2015 2020 2025 2030

Year

Oil Consumption (MPBD)

2005 AEO Base Case

HEV Scenario

Oil Use Reduction with HEVs

3 MBPD Oil use same as today!

This highly aggressive scenario assumes 100% HEV sales from 2010 onwards...

Produced using VISION model, MBPD = million barrels per day

HEVs unable to reduce consumption below today’s consumption level

153
5

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption

0

2

4

6

8

10

12

14

16

2000 2005 2010 2015 2020 2025 2030

Year

Oil Consumption (MPBD)

2005 AEO Base Case

PHEV Scenario

Oil Use Reduction with PHEVs

4 MBPD

This highly aggressive scenario assumes 100% HEV sales 

from 2010 and 50% PHEV40 sales from 2020 onwards...

Oil use reduction!

Produced using VISION model, MBPD = million barrels per day

PHEVs reduce oil consumption with a transition to electricity

PHEVs on E85 ??

6

Recent PHEV Prototypes

EnergyCS Plug-In Prius HyMotion Escape PHEV

AC Propulsion 

Jetta PHEV

AFS Trinity

Extreme HybridTM Esoro AG H301

DaimlerChrysler

Sprinter PHEV

Renault Kangoo

Elect’road

154
7

PHEV Batteries

Johnson Controls / SAFT Cobasys

Valence Technologies Hymotion

8

Battery Characteristics

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Power Density (W/L)

NiMH

Li-ion

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Specific Cost ($/kWh)

Projected

Current Status

0

200

400

600

800

1000

1200

1400

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Specific Power (W/kg)

NiMH

Li-ion

Lower power to energy ratio

leads to lighter, smaller, and 

less expensive energy

storage system

155
9

All-Electric vs Blended Strategy

• Engine turns on when power

exceeds battery power capability

• Engine only provides load that 

exceeds battery power capability

• Engine turns on when battery

reaches low state of charge

• Requires high power battery and 

motor

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

All-Electric

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

Blended

10

0 10 20 30 40 50 60 0

0.1

0.2

0.3

0.4

Fuel Consumption

PHEV20 on LA92

0 10 20 30 40 50 60 0

20

40

60

80

100

Energy Storage System Rated Power (kW)

CD Mode Petroleu

m

Consumption Reduction (%)

Electricity (kWh/mi)

Gasoline (L/mi)

Blended

All-Electric

Blended vs. AER Consumption Tradeoff

• Reducing ESS power 

should reduce cost,

mass, volume

• 50% reduction in

power still provides 

almost all of the fuel

consumption benefit

* CD = Charge Depleting

Po

wer

Battery Limit

Battery

Engine

156
11

PHEV Battery Sizing Alternatives

0

10

20

30

40

50

60

0 5 10 15 20 25 30

Energy (kWh)

Po

w

er (kW)

PHEV20 - Blended

PHEV20 - AER

PHEV40 - Blended

PHEV40 - AER

70% SOC Window 50% SOC Window

AER

Blended

12

Battery Life

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles

• Also need to consider combination of high and low frequency cycling

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20

4000

50%

70%

157
13

PHEV Battery Cost Requirements for 5 Year Payback 

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

Battery specific cost ($/kWh total)

NiMH

Li-Ion PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC 

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

PHEV40

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

Battery specific cost ($/kWh total)

NiMH

Li-Ion PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC 

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

Battery specific cost ($/kWh total)

NiMH

Li-Ion PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC 

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

PHEV40

$-

$500

$1,000

0 5

PHEV20

PHEV40

$-

$500

$1,000

0 5

PHEV20

$-

$500

$1,000

0 5

PHEV20

PHEV40

What’s value of other benefits

• Less trips to gas station

• Being “Green”

• ...

14

Conclusions

• Plug-in hybrid technology can reduce petroleum 

consumption beyond that of HEV technology

• The study highlighted some of the PHEV design options 

and associated tradeoffs

— Expansion of the energy storage system usable state of charge 

window while maintaining life will be critical for reducing system

cost and volume

— A blended operating strategy as opposed to an all electric

range focused strategy may provide some benefit in reducing 

cost and volume while maintaining consumption benefits

• The key remaining barriers to commercial PHEVs are

battery life, packaging and cost

158
Section 4.2 

Title: “Plug-In Hybrid Electric Vehicle Energy Storage System Design” 

Type: Paper

Authors: Tony Markel and Andrew Simpson 

Date: May 19, 2006 

Conference or Meeting: Presented at the Advanced Automotive Battery Conference in Baltimore, 

Maryland

Abstract: Discusses the system design trade-offs of battery sizing and control as they relate to cost, fuel

economy, and life 

 159
Plug-In Hybrid Electric Vehicle Energy Storage System Design*

Tony Markel and Andrew Simpson 

National Renewable Energy Laboratory, 1617 Cole Blvd. Golden, Colorado 80401 USA 

ABSTRACT

Plug-in hybrid electric vehicle technology holds much promise for reducing the demand for petroleum in

the transportation sector. Its potential impact is highly dependent on the system design and in particular, the

energy storage system. This paper discusses the design options including power, energy, and operating

strategy as they relate to the energy storage system. Expansion of the usable state-of-charge window will

dramatically reduce cost but will likely be limited by battery life requirements. Increasing the power 

capability of the battery provides the ability to run all-electrically more often but increases the incremental

cost. Increasing the energy capacity from 20-40 miles of electric range capability provides an extra 15%

reduction in fuel consumption but also nearly doubles the incremental cost. 

Introduction*

The United States is faced with a transportation 

energy dilemma. The transportation sector is 

almost entirely dependent on a single fuel – 

petroleum. The continued role of petroleum as the

primary transportation fuel should be questioned.

Today, nearly 60% of U.S. total petroleum

consumption is imported and results in billions of

dollars flowing to the economies of foreign

countries. More than 60% of U.S. petroleum

consumption is dedicated to transportation.[1] The

domestic production of petroleum is steadily

declining while our rate of consumption continues

to increase; thus imports are expected to continue

to increase. Meanwhile, petroleum consumption 

rates in the emerging economies of China and

India are rapidly expanding. Furthermore, experts 

believe world petroleum production may peak

within the next 5-10 years.[2] The combination of

these factors will place great strain on the supply

and demand balance of petroleum in the near

future.

Hybrid electric vehicle (HEV) technology is an

excellent way to reduce our petroleum

consumption through efficiency improvements.

HEVs use energy storage technology to improve

vehicle efficiency through engine downsizing and

by recapturing energy normally lost during braking 

events. A typical HEV will reduce gasoline

consumption by about 30% over a comparable

conventional vehicle.

1

 Since introduced, HEV

sales have grown at an average rate of more than

80% per year. However, after 5 years of 

availability, they represent only 0.1% of the total

U.S. vehicle fleet. There are 237,000,000 vehicles

on the road today and more than 16 million new

vehicles sold each year.[3] New vehicles will

likely be in-use for more than 15 years and the

vehicle miles traveled (VMT) continues to 

grow.[4] It will be challenging to overcome the 

inertia of the vehicle fleet. For instance, if every 

new vehicle sold in 2011 and beyond was a

petroleum-fueled hybrid, our petroleum

consumption level 10 years from now would still

be 6% greater than the current light-duty fleet

consumption, and it would never drop below

today’s consumption level. Efficiency

improvements of HEVs will be insufficient to

overcome vehicle fleet and VMT growth

expectations. 

This presents a challenge of how to best displace 

as much petroleum consumption as soon as

possible while incurring reasonable costs. Many

industries, including polymer and pharmaceutical, 

have little choice but to use petroleum. There are

several alternatives to petroleum for a 

transportation fuel source. These include

hydrogen, ethanol, biodiesel, and electricity.

Hydrogen and fuel cell technology has advanced 

rapidly but still faces significant cost,

infrastructure, and technical challenges that could

limit market penetration within the next 15-20

years. Both ethanol and biodiesel are used today * Employees of the Midwest Research Institute under Contract 

No. DE-AC36-99GO10337 with the U.S. Dept. of Energy have

authored this work. The United States Government retains and

the publisher, by accepting the article for publication,

acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or

reproduce the published form of this work, or allow others to 

do so, for United States Government purposes.

1 With additional improvements in aerodynamics and engine

technology, hybrid vehicles today have demonstrated upwards

of a 45% reduction in consumption as compared to a

conventional vehicle.

160
and help displace petroleum. However, at current

production levels and given future expectations on 

cellulosic production potential, biofuels have

limited ability to end our oil addiction alone but

may be more successful when combined with

other displacement technologies.

Plug-in hybrid electric vehicle (PHEV) technology 

is an option with the potential to displace a 

significant portion of our transportation petroleum

consumption. A plug-in hybrid vehicle is an HEV

with the ability to recharge its energy storage

system with electricity from the electric utility

grid. With a fully charged energy storage system,

the vehicle will bias towards using electricity over

liquid fuels. A key benefit of PHEV technology is

that the vehicle is no longer dependent on a single 

fuel source. The primary energy carrier is

electricity generated using a diverse mix of

domestic resources including coal, natural gas,

wind, hydroelectric, and solar energy. The 

secondary energy carrier is a liquid fuel (e.g.,

gasoline, diesel, or ethanol).

PHEV technology is not without its own technical

challenges. Energy storage system cost, volume, 

and life are major obstacles that must be overcome

for these vehicles to be viable. The fuel

displacement potential of a PHEV is directly

related to the characteristics of the energy storage

system. More stored energy means more miles that

can be driven electrically. However, increasing 

energy storage also increases vehicle cost and can 

present significant packaging challenges. Finally,

the energy storage system duty cycle for a PHEV 

is likely to be more severe from a life standpoint

than electric vehicles or HEVs. 

The purpose of this paper is to expand on the

current understanding of the potential benefits, the

design options, and the challenges related to

PHEV technology. 

PHEV and HEV Terminology 

Charge-sustaining mode – An operating mode in

which the state-of-charge of the energy 

storage system over a driving profile may 

increase and decrease but will by the end of 

the cycle return to a state with equivalent

energy as at the beginning of the period. 

Charge-depleting mode – An operating mode in

which the state-of-charge of the energy 

storage system over a driving profile will have

a net decrease in stored energy. 

All-electric range (AER) – The total distance

driven electrically from the beginning of a 

driving profile to the point at which the engine

first turns on. 

Electrified miles – Is the sum of all miles driven

with the engine off including those after the 

engine first turns on.

PHEVxx – A plug-in hybrid vehicle with sufficient

energy to drive xx miles electrically on a

defined driving profile usually assumed to be 

urban driving. The vehicle may or may not

actually drive the initial xx miles electrically

depending on the control strategy and driving 

behavior.

SOC – State-of-charge of the energy storage

system. The fraction of total energy capacity

remaining in the battery. 

Degree of hybridization – The fraction of total

rated power provided by the electric traction 

drive components. 

Utility factor – A measure of the fraction of total

daily miles that are less than or equal to a

specified distance based on typical daily 

driving behavior.

Potential Benefits of PHEVs 

A key reason for exploring PHEV technology is its

ability to achieve significant petroleum

consumption reduction benefits. A PHEV has

essentially two operating modes: a chargesustaining mode and a charge-depleting mode. The 

total consumption benefits of a PHEV are a

combination of the charge-depleting and chargesustaining mode improvements. 

Figure 1 highlights the relative importance of these

two modes in achieving fuel displacement. It

shows the total consumption benefit as a function

of the improvement in charge-sustaining mode

consumption for HEVs and PHEVs with several

electric range capabilities. Several current model

hybrid vehicles are included. Today’s HEVs do 

not have a charge-depleting mode, so their total

consumption benefits are derived solely from

improvements in the charge-sustaining mode. The

large dots on the plot present three scenarios that

achieve 50% reduction in total petroleum

consumption. A PHEV40 that consumed no

petroleum (all-electric operation) in chargedepleting mode with a fuel economy in chargesustaining mode equivalent to a conventional

vehicle would consume 50% less petroleum 

because the first 40 miles of driving would be 

done electrically. Likewise, a PHEV20 that 

161
Prius (Corolla)

Civic

Accord

Highlander

Escape

Vue

HEV 

+100% cs mpg

PHEV20

+40% cs mpg

PHEV40

+0% cs mpg

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 20% 40% 60% 80% 100%

Reduction in Charge-Sustaining Mode Petroleum 

Consumption (%)

Reduction in Tot

al P

etroleum 

Consumption (

%)

Infeasible region for

HEV technology

HEV

PHEV20

PHEV40

PHEV60

Figure 1: HEV and PHEV Fuel Consumption Benefits by Operating Mode 

cs=charge sustaining 

consumed 30% less petroleum in chargesustaining mode would also consume 50% less

total petroleum. An HEV would have to achieve

50% reduction in consumption in its chargesustaining mode to have an equivalent total

benefit. It is unlikely that the consumption

reduction in charge-sustaining mode can be

reduced beyond 50% cost effectively. Quantifying 

the relative costs of adding electric range

capability versus improving charge-sustaining

mode efficiency is important. Moving vertically in

the figure at a given charge-sustaining mode

consumption level results in more miles driven

electrically. Electrification of miles through 

charge-depleting operation in a PHEV is expected 

to be a cost-effective way to continue to reduce 

fuel consumption beyond HEV technology 

capabilities. 

The conclusions drawn from Figure 1 are based on

national driving statistics shown in Figure 2.

Figure 2 is a histogram showing the daily driving 

distance distribution and the resulting utility factor

derived from the 1995 National Personal

Transportation Survey (NPTS) data. The utility

factor represents the fraction of total daily VMT

that are less than or equal to the said distance. The 

utility factor is important for PHEVs because it

can be used to effectively weight the value of the

charge-depleting fuel consumption benefits versus

the charge-sustaining fuel consumption benefits in

a way that allows the results to be extrapolated and

applied to the national fleet. 

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Trip Distance (mi)

Percentage of Trips

Utility Factor

Figure 2: 1995 NPTS Data on Daily Driving 

Distance Distribution and Resulting Utility Factor

PHEVs take advantage of the fact that the typical

daily driving distance is on the order of 30 miles.

If most of these miles could be driven electrically,

a large portion of our petroleum consumption 

would be eliminated. 

Design Options and Implications 

Determination of the energy storage system

characteristics is a critical step in the PHEV design

process. The energy storage system design 

variables include the power, energy, and usable

state-of-charge (SOC) window. These three

162
variables will affect cost, mass, volume, life, fuel

economy, and vehicle operation.

The usable energy capacity of the energy storage

system is defined by the desired electric range

capability. The fuel displacement potential is

directly related to the electric range capability.

From Figure 2, a range capability of 20 miles (i.e., 

a PHEV20) would substitute electrical energy for

petroleum consumption in 30% of total VMT.

Likewise, a PHEV with 40 miles of range

capability could displace 50% of total VMT. A 

typical midsize sedan will require ~300 Wh/mi for 

all electric operation. Thus a PHEV20 would

require ~6 kWh, and a PHEV40 would require ~12

kWh of usable energy. It is possible to reduce the 

usable energy requirement through aerodynamic

and lightweight vehicle designs but not

substantially. 

For design purposes, the usable SOC window

relates the total energy capacity to the required

usable energy capacity. A PHEV is likely to incur

at least one deep discharge cycle per day and as a

result will need to provide 4000+ deep discharge

cycles in its 10-15 year lifetime. Figure 3 is a 

curve-fit to data presented by Rosenkranz showing 

the expected cycle life performance of lithium-ion

(Li-ion) and nickel-metal hydride (NiMH)

technology as a function of the discharge depth.[5] 

It shows that when a battery is discharged more 

deeply, the cycle life decreases. The horizontal, 

shaded box is the typical depth of discharge

cycling that HEV batteries today incur while the

vertical, shaded box is the range of cycles that a

PHEV battery will need to endure for a 10-15 year

vehicle life.

0

10

20

30

40

50

60

70

80

90

100

0 10000 20000 30000 40000

Number of Cycles

Depth of Discharge (%)

NiMH

Li-ion

Typical PHEV Cycles

During Life of Vehicle

Typical HEV

Operating Window

Figure 3: Cycle Life Characteristics of Varta

Energy Storage Technologies [5] 

The data indicate that NiMH can achieve 4000

cycles when discharged to 70% depth of discharge

repeatedly. To achieve the same number of cycles, 

Li-ion technology could only be discharged to

50% depth of discharge on a daily basis. Assuming

a 70% usable SOC window for a PHEV20 then 

requires 8.6 kWh of total energy capacity. This

battery would have 5-10 times more energy 

capacity relative to that found in current hybrid

vehicles. The PHEV40 will need 17.2 kWh. To

minimize total energy storage capacity (and thus

cost and volume), it will be important to maximize 

the usable SOC window for PHEVs while 

satisfying cycle life requirements. 

The energy storage system cost, mass, and volume

are strong functions of the energy storage system

power to energy ratio. Representative specific

power and power densities are provided for both

Li-ion (based on Saft products [6]) and NiMH

(based on Cobasys products [7]) technologies in

Figures 4 and 5. Current and projected specific 

cost relationships are provided in Figure 6. The

cost projections are those suggested by Electric

Power Research Institute.[8] For fixed energy

storage capacity, as power to energy ratio 

decreases so do cost, volume, and mass. The

question is how does reducing the power

capability affect the fuel consumption reduction

potential?

0

200

400

600

800

1000

1200

1400

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Specific Power (

W/kg)

NiMH

Li-ion

Figure 4: Typical Specific Power of Energy

Storage Technologies

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Power Density (

W/L)

NiMH

Li-ion

Figure 5: Typical Power Density of Energy

Storage Technologies

163
0

0.5

1

1.5

2

Energy (k

Wh)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20

Power to Total Energy Ratio (kW/kWh)

Specific Cost ($/k

Wh)

Projected

Current Status

Figure 6: Typical Specific Cost of Energy Storage 

Technologies

To achieve a desired all-electric range (AER) 

capability, the energy storage system and motor 

will need to provide sufficient power to propel the 

vehicle without assistance from the engine. On an 

urban driving profile, the peak power is ~40 kW

and the power is typically less than 15 kW as 

shown in Figure 7 for a typical light-duty vehicle.

0 2 4 6 8

-20

0

20

40

60

Distance (mi)

Power (kW)

Peak Power

Power

Energy

0 2 4 6 8

Figure 7: Power and Energy Requirements for AllElectric Range Capability on Urban Driving

In Figures 8 and 9, the power and energy required

for all-electric range capability on several driving

profiles is provided. The Urban Dynamometer

Driving Schedule (UDDS) and Highway Fuel

Economy Test (HWFET) cycles are used by the

U.S. Environmental Protection Agency to

represent urban and highway driving behaviors in

reporting the fuel economies of today’s vehicles.

The Unified Driving Cycle, also called LA92, and

the US06 (part of the Supplemental Federal Test 

Procedure) are more aggressive urban and 

highway driving cycles respectively that are likely 

to be more representative of current driving 

behaviors. The energy to provide 20 miles of allelectric range on the UDDS and HWFET cycles

only provides 10 and 15 miles of range capability

on the US06 and LA92 cycles, respectively as

shown by the dashed line in Figure 9. A PHEV on 

the UDDS would need 40 kW of battery power 

while it would require more than 60 kW on the

LA92 cycle. Adding battery power beyond the 

peak power requirement is unlikely to provide 

additional value. Acceleration requirements will

place additional constraints on the energy storage 

system power requirements depending on engine

sizing.

0 10 20 30 40 50 60

0

50

100

150

UDDS

US06

HWFET

LA92

Distance (mi)

Peak Po

wer (kW)

Figure 8: Peak Power Requirements for AllElectric Range Capability for Typical Mid-size

Car on Several Duty Cycles

0 10 20 30 40 50 60

0

5

10

15

20

Energy (k

Wh)

Distance (mi)

UDDS

US06

HWFET

LA92

Figure 9: Usable Energy Requirements for AllElectric Range Capability on Typical Driving

Profiles for a Mid-size Car 

To maximize charge-sustaining fuel economy, it is

desirable to minimize the power rating (downsize) 

the engine as much as possible. The engine in a

PHEV will likely be sized to provide continuous

performance capability. If it is assumed that the

vehicle must achieve a continuous top speed of

110 mph and continuous gradeability at 55 mph on 

a 7.2% grade using 2/3 of peak engine power then

the minimum engine would need to be 80-85 kW

for a typical sedan. Now to achieve a 0-60 mph 

acceleration time of 8.0s or less, the energy storage

system would need at least 45-50 kW of power

capability at the low SOC operating point. As a

164
result, with maximum engine downsizing, the

power to energy ratio would be ~5 for a PHEV20

and 2.8 for a PHEV40. 

The sizes described so far are only necessary to

achieve large all-electric range capabilities.

Significant fuel can be displaced without large allelectric range capability. As shown in Figure 7, the

urban drive cycle power requirements are typically

less than 15 kW. Since cost, mass, and volume of 

the energy storage system can be reduced by

reducing the power to energy ratio, it is

worthwhile to explore the fuel displacement

potential of low power energy storage systems for

PHEVs.

The lower bound on the energy storage power will

be a function of the lowest power to energy ratio

modules available. The lowest power to energy 

ratio for typical Li-ion or NiMH technology today

is ~1. Therefore, the minimum power will be on 

the order of 10-15 kW. 

Employing the low-power option limits the allelectric range capability of the vehicle. However,

if, when the engine is on, it only provides

supplemental power beyond the capabilities of the

energy storage system; substantial fuel

displacement can still be achieved via a strategy

where energy storage and engine operate in a

blended manner. The blended approach was 

proposed in an early paper [9] and will be referred 

to as a blended strategy in the remainder of the 

paper. 

Figures 10 and 11 provide a comparison between 

operating characteristics for PHEVs with allelectric-range-focused versus blended operating

strategies. In Figure 10, the battery and motor 

(dashed line) have sufficient power to propel the 

vehicle until about 22 miles at which time the

engine (solid line) is turned on. In Figure 11, the 

engine turns on within the first mile but when on,

it only provides supplemental power, and the

battery still provides most of the power. Thus, the

battery discharges over approximately the same

distance and displaces nearly as much fuel. 

Figure 10: Urban Cycle Operating Characteristics

of an All-Electric Range Focused PHEV20 

Figure 11: Urban Cycle Operating Characteristics

of a PHEV20 with a Blended Strategy

The charts that follow summarize the tradeoffs of

power, energy, SOC window, and operating

strategy on the cost, efficiency, and fuel savings

potential of a PHEV20 and a PHEV40. All

components in each vehicle scenario were sized 

first for an all-electric range scenario and second

for a blended scenario. And for each of these four

scenarios, a 50% SOC window and a 70% usable

SOC window were considered. To define the

blended scenario, a power to energy ratio was

chosen that was half that of the all-electric range

scenario. 

Figure 12 summarizes the energy storage system

power and energy characteristics of the eight

vehicles considered. The SOC window only

slightly impacts the power requirement while the

AER case needs twice as much power as the

blended case as designed. Battery energy is

slightly more than a factor of two due to mass

compounding.

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

-30

-20

-10

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40

Distance (mi)

Power (kW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SOC (%)

engine

motor

SOC

165
0

10

20

30

40

50

60

0 5 10 15 20 25 30

Energy (kWh)

Po

wer (k

W)

PHEV20 - Blended

PHEV20 - AER

PHEV40 - Blended

PHEV40 - AER

70% SOC Window 50% SOC Window

Figure 12: Power and Total Energy Characteristics

of the Energy Storage System

Incremental cost of the vehicle is likely to be a 

significant barrier to PHEV technology 

acceptance. The main reason for trying to use a 

lower power to energy ratio energy storage system

would be to reduce cost while providing the same

amount of energy. Figure 13 shows that reducing

power to energy ratio and moving from an AER to

blended strategy reduced incremental cost. 

However, increasing the usable SOC window

seemed to more strongly impact the incremental

costs.

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

0 1 2 3 4 5 6

Power to Total Energy Ratio (kW/kWh)

Incre

mental Cost ($)

PHEV20 - Blended

PHEV20 - AER

PHEV40 - Blended

PHEV40 - AER

50% SOC Window

70% SOC Window

AER

Blended

Figure 13: Relationship between Incremental Cost

and Power to Energy Ratio

For a given range (e.g., PHEV20) and SOC 

window (e.g., 50%), moving to a lower power to

energy ratio not only reduced the incremental cost

but also reduced the fuel consumption reduction 

potential as expected. The fuel consumption 

benefits of the blended strategy are about 6% less

than the AER strategy for both PHEV20 and 

PHEV40 cases as shown in Figure 14. 

Interestingly, expanding the usable SOC window

has minimal impact on fuel consumption reduction 

potential but substantially reduces incremental cost

and thus should be emphasized. 

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

20% 30% 40% 50% 60% 70%

Percent Reduction in Petroleum Consumption (%)

Incremental Cost ($)

PHEV20 - Blended

PHEV20 - AER

PHEV40 - Blended

PHEV40 - AER

50% SOC Window

70% SOC Window

Figure 14: Cost-to-Benefit Relationship for 

PHEVs

As shown in Figure 15, there are efficiency

tradeoffs between a blended and an all-electric

range focused strategy. In the AER approach, the

engine is as small as possible and when it is on, it

will operate at higher load fractions which

typically correlate to higher efficiencies. The 

energy storage system in the AER scenario is a

higher power to energy ratio with lower internal

resistance and thus less loss. On the other hand,

the motor in the blended approach is smaller, and

thus running at higher load fractions with higher

efficiencies. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Engine Motor Battery

Urban Cycle Avg. Efficiency (%)

PHEV20 - Blended

PHEV20 - AER

Figure 15: Component Efficiencies for AER and

Blended PHEV Strategies

The purpose of these case scenarios is to 

demonstrate that there are many options in the

design of a PHEV. Each of these options has

associated tradeoffs. Ideally, the design should

find a balance between petroleum consumption 

reduction potential and incremental costs if it is to

be successful. Our results demonstrate that a

blended approach combined with an expanded

SOC window effectively reduced cost while

displacing nearly as much fuel in comparison to an 

all-electric range focused PHEV. These cost

reductions are critical for market viability.

166
Analysis Refinements 

This analysis provides only a simplified view of 

the PHEV design space and challenges. There are

many uncertainties associated with these

conclusions. In particular, there is uncertainty

associated with the life cycle data, the battery cost

data, and the vehicle usage patterns. It will be

important to identify how each of these

uncertainties will affect the PHEV design and

operation.

The life cycle data available at this time have been

collected as constant discharge cycles to a 

specified depth of discharge repeatedly. The 

batteries in HEVs today can be expected to 

encounter tens of thousands of small depth of 

discharge cycles at a moderate to high SOC.

PHEVs will on the other hand encounter at least

one deep discharge cycle on a daily basis. In 

addition, a fully utilized energy storage system

will also encounter shallow depth of discharge

cycles both at high and low SOC levels. It has 

been assumed that the daily deep discharge cycle

will be the overriding factor that will determine

life cycle performance. It is unclear how the

shallow cycling behavior may contribute to the

degradation of the energy storage system. 

Today, the cost of hybrid battery technology is

high, and tax incentives are used to make hybrids 

cost competitive with comparable vehicle options.

The energy storage system costs contributing to

the incremental cost analysis presented earlier

assume future high-volume production. Current

costs are estimated to be 4-5 times higher than the

long-term assumptions. Since this is a pivotal

assumption, it is possible to turn the analysis

around and look at what battery costs might need 

to be to provide a cost effective vehicle.

Figure 16, shows the specific costs that the energy 

storage technology would need to achieve for the

fuel cost savings over 5 years to offset the initial

incremental cost. The chart includes both a present

fuel cost ($2.15/gallon gasoline and 9 ¢/kWh 

electricity case) and a future fuel cost scenario 

($4.30/gallon and 9 ¢/kWh). At today’s fuel costs,

to be cost neutral, PHEV20 batteries would need

to be at the projected long-term cost goals (labeled

as Projected Battery Costs in Figure 16). 

However, in the future fuel price scenario, both 

PHEV20 and PHEV40 energy storage systems 

only need to reach the $750 to $500/kWh range to 

be cost neutral respectively.

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

B

attery specific cost (

$/kW

h tot

al)

NiMH

Li-Ion

PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

PHEV40

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

B

attery specific cost (

$/kW

h tot

al)

NiMH

Li-Ion

PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 5 10 15 20 25

Power-to-energy ratio (kW/kWh)

B

attery specific cost (

$/kW

h tot

al)

NiMH

Li-Ion

PHEV3.5

PHEV5

PHEV10

EXPANDED 70% SOC

WINDOW FOR ALL PHEVS

Projected battery costs

Current battery costs?

HEV0

HEV0 PHEV3.5

$4.30 / gal.

$2.15 / gal. PHEV20

(19,000 mi/year, 52 mi/day avg.)

Assumed electricity cost = 9c/kWh

PHEV40

Figure 16: Battery Cost Requirements for Fuel Savings to Offset Incremental Cost within 5 Years

167
Additional research completed at the National 

Renewable Energy Laboratory clearly shows that

there is a significant connection between the

vehicle usage pattern and the consumption 

reduction benefits of a PHEV both over a 

conventional vehicle and an HEV. The analysis

presented assumes utility factor weighted fuel

economies based on the UDDS and HWFET

driving profiles. It is fair to assume that neither of

these driving profiles (developed in the 1970s)

accurately represents typical driving habits of 

today. In addition, the utility factor is used to

weight the relative value of the electric range

capability of a PHEV. However, the utility factor

is based on data from national personal travel

surveys conducted in 1995. More recent data are

available and need to be analyzed. It’s likely that

travel behavior is evolving. In addition, the 

existing survey data typically only represent a

single day of the year and do not account for 

variation daily or seasonally. PHEV benefits are

likely to be significantly influenced by these

variations in driving habits.

Conclusions 

PHEVs have the potential to dramatically reduce

future U.S. transportation petroleum consumption.

To overcome the implementation challenges of 

PHEV technology, a systems perspective should

be employed. This study sheds light on the

systems design tradeoffs as they relate to energy 

storage system technology for PHEVs.

Specifically, it evaluates the impacts of reducing

power to energy ratio and expanding the usable

SOC window on incremental cost and fuel

consumption reduction benefits.

Based on the analyses, we conclude that:

• Plug-in hybrids provide potential for reducing 

petroleum consumption beyond that of HEV

technology.

• There is a spectrum of PHEV design options

that satisfy performance constraints but with

tradeoffs in incremental costs and fuel

consumption reduction potential. 

• Expansion of the usable SOC window while

maintaining energy storage system life will be

critical for reducing incremental costs of 

PHEVs.

• The fuel consumption reduction benefits are

only slightly reduced while the battery size

and cost are significantly reduced when a

blended strategy is chosen relative to an allelectric range focused strategy. 

References 

1) Energy Information Administration 

www.eia.doe.gov. 

2) Hirsch, R., Bezdek, R., and Wendling, R. 

“Peaking of World Oil Production: Impacts,

Risks, and Mitigation.” February, 2005. 

3) “Highway Statistics 2004.” U.S. Department

of Transportation. Federal Highway

Administration.

http://www.fhwa.dot.gov/policy/ohim/hs04/in

dex.htm. 

4) Davis, S., Diegel, S. “Transportation Energy

Databook: Edition 24.” December, 2004. 

5) Rosenkranz, C. “Deep Cycle Batteries for 

Plug-in Hybrid Application” Presented at

EVS-20 Plug-in Hybrid Workshop. Nov. 15,

2003. Monaco. 

6) Saft website. www.saftbatteries.com. 

7) Cobasys website. www.cobasys.com. 

8) Duval, M. “Advanced Batteries for Electric 

Drive Vehicles.” EPRI Technical Report

#1009299. May, 2004. 

9) Markel, T. and Simpson, A. “Energy Storage

Considerations for Grid-Charged Hybrid

Electric Vehicles.” IEEE Vehicular

Technologies Conference. September 7-9, 

2005. Chicago, IL. 

Acknowledgements 

The authors would like to acknowledge the

programmatic support of the U.S. Department of 

Energy Office of Energy Efficiency and

Renewable Energy FreedomCAR and Vehicle

Technologies Program.

168
Section 4.3 

Title: “Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In HEV”

Type: Paper

Authors: Michael Patrick O’Keefe and Tony Markel 

Date: October 2006 

Conference or Meeting: To be published at the 22nd International Battery, Hybrid, and Fuel Cell 

Electric Vehicle Symposium and Exposition 

Abstract: Compares the performance of an electric-centric charge-depleting hybrid vehicle control 

strategy with a near-optimal dynamic programming-optimized control strategy utilizing a priori

knowledge of the driving profile 

 169
Dynamic Programming Applied to Investigate Energy 

Management Strategies for a Plug-In HEV1

Michael Patrick O’Keefe

National Renewable Energy Laboratory

Tony Markel 

National Renewable Energy Laboratory

Abstract 

Plug-in hybrid electric vehicles (PHEVs) are an advanced dual-fuel powertrain technology that 

combine features of the battery electric vehicle (BEV) and hybrid electric vehicle (HEV). One of the 

fuels of the PHEV is electricity which is supplemented by another fuel (typically gasoline). The 

gasoline consumption for a PHEV is distance dependent based on the vehicle control strategy. In this 

paper, we explore two basic control concepts applied to a PHEV: an “electric vehicle centric” control

strategy and an “engine-motor” blended control strategy. A near optimal control solution is derived 

using the dynamic programming optimization algorithm. Based on comparison with the dynamic 

programming results, we show that for urban driving, a PHEV should typically operate closer to an 

“electric vehicle centric” control strategy to provide consistently high fuel savings. We also show that

PHEVs with smaller motors and lower power-to-energy ratio batteries can save nearly the same 

amount of fuel as a full-size PHEV—but perhaps at a reduced cost. 

Keywords: Plug-In Hybrid, Hybrid Strategy, Energy Efficiency, Modeling, Dynamic Programming

1 Introduction 

1.1 Plug-In Hybrid Electric Vehicles 

Plug-in hybrid electric vehicles (PHEVs) are a dual-fuel technology capable of transforming the

transportation energy infrastructure away from non-renewable, high-carbon fuels to more

environmentally responsible options. One of the PHEV fuels is electricity. The other fuel could be one

of any number of options, although gasoline is considered here. 

PHEVs can deliver performance equivalent with today’s modern vehicles. Furthermore, compared 

with other technology options, the PHEV does not suffer from some of the infrastructure issues (e.g., 

fuel cell vehicles) nor the limited range issues (e.g., battery electric vehicles) exhibited by other

technologies. These positive benefits are the result of both efficient delivery of fuel-energy from the 

tank to the wheels and, more importantly, a transition from conventional transportation fuels to

electricity. This is possible because PHEVs exhibit aspects of both battery electric vehicles (BEVs)

and hybrid-electric vehicles (HEVs): 

1

 This work has been authored by an employee or employees of the Midwest Research Institute under Contract 

No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government retains and the 

publisher, by accepting the article for publication, acknowledges that the United States Government retains a 

non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, 

or allow others to do so, for United States Government purposes. 

170
• A large energy storage unit capable of being recharged from the electrical utility grid and

supplying net motive energy over a significant distance 

• A hybrid powertrain typically using an internal combustion engine with an electrical motor 

By blending aspects of the BEV with conventional HEVs, one can gain many of the advantages of a 

BEV while eliminating several disadvantages. Because fewer batteries are needed than a full BEV, the

PHEV comes with a reduced cost penalty versus a BEV of equivalent performance. Furthermore, the

PHEV has no range penalty and charging times are much shorter than an equivalent BEV. In contrast

to an equivalent HEV, fuel consumption is further reduced since fuel energy is supplied from both

electricity and liquid fuel as opposed to just liquid fuel as is the case for conventional HEVs. 

PHEVs work well for vehicles that operate where relatively short trips comprise the bulk of distance

traveled. By recharging between these short trips, a large portion of the motive energy can come from

the electrical grid as opposed to gasoline or other fossil fuels. The transition from today’s petroleumbased transportation fuels to electricity opens up many opportunities. By recharging the vehicle’s 

batteries overnight, electrical utilities can increase their operating efficiency. Furthermore, due to the

difference between peak capacity and base-load capacity, power utilities will typically have enough 

excess capacity at night to recharge a large number of vehicles before having to add new capacity. If

done correctly, this can lead to reductions in carbon dioxide (CO2) and other greenhouse gas emissions. 

Furthermore, if effort is made to transform the power plants within the national electrical grid to use 

more renewable sources of energy, the benefits of renewable energy can be brought to the

transportation sector through PHEVs. 

However, PHEVs do have some challenges for commercialization. Chief among these are cost which 

is largely connected with batteries and battery life, in addition to the added cost of PHEV power

electronics and powertrain components. 

In this paper, we will explore two separate PHEV architectures. Both vehicles yield equivalent

performance and have the same electrical capacity in the vehicle energy storage system. The first,

referred to as the full-size PHEV, contains a high-power motor and energy storage unit with smaller

internal combustion engine (ICE). The second vehicle, referred to as the half-size PHEV, uses a lower

power motor with lower power energy storage and larger ICE. The second vehicle represents a lowercost PHEV solution because it deemphasizes the (expensive) electric powertrain components such as 

high-powered batteries and emphasizes ICE technology.

In addition to the vehicle architectures, we also discuss two different PHEV energy management 

strategies: an “electric vehicle centric” approach and a blended-control approach. In the “electric

vehicle centric” approach, the motor and batteries attempt to meet all traction demand electrically,

with the engine only helping when the motor is not powerful enough. This strategy uses electricity

whenever possible. The blended control approach attempts to spread the electrical consumption over a 

larger distance by blending engine power with motor usage at times when the system is more efficient. 

In order to compare these control paradigms, the dynamic programming algorithm, which can

determine the near-optimal solution of any control problem, is used. 

1.2 The Dynamic Programming Method 

Dynamic programming is a numerical technique that can be applied to any problem that requires

decisions to be made in stages with the objective of finding a minimal penalty decision pathway [1]. 

“Penalty” used in this sense refers to a quantitative measure of the undesirable outcomes of a decision. 

Dynamic programming combines knowledge of the immediate penalty of the decision at hand with

knowledge of future penalties that arise as a result of the immediate decision. This algorithm has been 

applied with success to HEVs in the past [2], though the authors are unaware of any application to

PHEVs to date. 

171
Dynamic programming requires the definition of a discrete time dynamic system (DTDS) and a

penalty function. Because the dynamic programming algorithm is quite computationally intensive, a

fast computational model is desired for the DTDS. In the context of this paper, the DTDS is a vehicle

model which calculates the change in state (battery state-of-charge) resulting from a given control

setting (the engine shaft out power) over a time-step along a duty cycle. The duty cycle, the vehicle’s

commanded speed versus time, is treated as deterministic for this study. The algorithm proceeds from

the end of a duty cycle to the beginning, calculating the penalty of possible control settings at each

time step. Because knowledge of the duty cycle is required beforehand, the dynamic programming 

algorithm cannot be implemented in actual control systems in real life. However, outputs from the

dynamic programming algorithm can be used to formulate and tune actual controllers. 

The penalty function used in this study attributes a penalty for using fuel, not meeting the specified 

duty cycle speed-time trace, and for not holding end state-of-charge at a reasonable level. 

The dynamic programming algorithm as used in this study only explores a subset of the entire design

space. Because of this, the control cannot be said to be “optimal,” only “near-optimal”. 

There are two main reasons to employ the dynamic programming method in this study: 

• To compare PHEV architectures under “near-optimal control” 

• To gain insights into what the “optimal” control is for a PHEV under various circumstances. 

By comparing all PHEV architectures under an “optimal control,” control itself is eliminated as a

design variable from the problem. 

2 Analysis Overview

This study compares the energy implications for two PHEV architectures over multiple urban duty

cycles (driving patterns) and multiple distances using a “near-optimal” control strategy derived via the 

dynamic programming algorithm. Both vehicles contain enough electrical energy to drive 

approximately 32 km (5.5 kWh usable capacity). The selection of this capacity and specific sizes is

based on a cost benefit analysis of PHEVs conducted by Simpson [3]. Both vehicles in this study use a

parallel hybrid design where the engine and/or motor can contribute to tractive effort at any time. 

Gasoline is assumed for the liquid fuel in this study. However, it is important to note that PHEVs

could use other fuels such as diesel, ethanol (E85), or even hydrogen if the ICE is properly designed to

handle the given fuel. 

2.1 Vehicle Platform, Performance, and Assumptions 

The vehicle platform used for this study is a mid-size sedan with performance requirements specified 

so as to be competitive in the North American marketplace. The specific requirements are given below

in Table 1.

Table 1: Mid-Size Sedan Performance Requirements and Platform Assumptions 

Attribute Value

Top Speed

top speed to be maintained

177 km/hr (110 mph)

Full Acceleration

time from 0 km/hr to 96.6 km/hr (60 mph)

8.0 seconds

Passing Acceleration

time from 64.4 km/hr (40 mph) to 96.6 mph (60 mph)

5.3 seconds

Hill Climbing

grade (percent rise over road-surface run) to climb with engine at 66% of

rated power

6.5% grade @ 88.5 

km/hr (55 mph)

172
Range

maximum distance traveled starting fully fueled

643.7 km (400 miles)

Glider Mass

the mass of the vehicle minus the powertrain

905 kg

Cargo Mass

the mass of cargo carried while meeting performance constraints

136 kg

Accessory Loads

the accessory loads assumed for the PHEV

0.7 kW electric average 

4.0 kW electric peak

Transmission Efficiency

efficiency of the mechanical gearing between motor/engine and wheels

85%

Electrical Generation Efficiency for Accessories

efficiency of generating the power to electrical accessories

85%

These requirements imply minimum component sizes. The peak accessory loads are assumed to be

engaged for purposes of calculating the hill climbing and top speed power requirements. All other

calculations (including fuel consumption calculations) assume average accessory loads. The resulting

component requirements for a conventional vehicle and two PHEVs are given in Table 2. 

Table 2: Component Sizes and Weights Used in Study

Component Conventional 

Vehicle 

Full-Size 

PHEV 

Half-Size 

PHEV 

Spark Ignited Internal 

Combustion Engine

121.7 kW peak

238.6 kg

80.1 kW peak

171.3 kg

99.2 kW peak

202.2 kg

Electric Motor and 

Inverter

NA 44.1 kW peak

45.1 kg

21.4 kW peak

33.0 kg

Battery Pack NA 5.50 kWh usable

47.2 kW peak

11.8 kWh full capacity

94.4 kg

5.57 kWh usable

23.9 kW

11.9 kWh full capacity

79.6 kg

Gasoline and Tank 509 kWh

49.1 kg

396 kWh

38.2 kg

416.7 kWh

40.2 kg

Transmission 167.9 kg 176.0 kg 176.8 kg

Support Structure 68.3 kg 78.8 kg 79.8 kg

Glider Mass 905.0 kg 905.0 kg 905.0 kg

Cargo Mass 136.0 kg 136.0 kg 136.0 kg

Total Vehicle Mass 1428.9 kg 1508.8 kg 1516.6 kg

Tested Mass 1564.9 kg 1644.8 kg 1652.6 kg

Degree of

Hybridization

0% 35.51% 17.75%

There are some subtleties in Table 2 that should be pointed out. First, note that advanced battery 

specifications are assumed. Next, the type of battery used in the full size PHEV is different from that

used in the half-size PHEV. If one examines both batteries, you will quickly see that the usable

capacity of both packs is nearly the same (the slight difference is due to the difference in weight and

requirement for both vehicles to drive the same range). However, the weights of both packs and the 

pack powers are different. This arises from a difference in battery pack power-to-energy ratio. The

energy density of batteries differs by power to energy ratio. Low power-to-energy ratio batteries also

tend to be slightly less expensive as a technology. For more detail on how cost and weight of each

component interact with vehicle requirements, see the paper by Simpson [3]. 

The degree of hybridization of both PHEVs appears in Table 2. This percentage is the ratio of the 

motor power to the engine plus motor power. The degree of hybridization of the “half-size” PHEV is

half that of the full-size PHEV, hence the name. 

173
2.2 Vehicle Model 

The vehicle model is used as the discrete time dynamic system (DTDS) by the dynamic programming 

algorithm. The model takes in a single control setting for each time step—the desired ICE shaft-out

power for that time step. Based on the ICE shaft output power, the motor will either accept or transmit 

power so as to satisfy the tractive effort and accessory loads required for the given time step. The

model also contains state information. The only state variable is the battery state-of-charge at the 

beginning of a time step. Based on the tractive effort required during the time step (defined by the duty

cycle) and the control setting for ICE power, the battery state-of-charge will change. 

Because the dynamic programming method is computationally expensive (requiring many model 

evaluations), the vehicle model used in this study has been constructed to contain only the minimum

required detail so as to be quick. For example, components in the powertrain use models of power and

efficiency as opposed to torque, speed, and efficiency. 

A schematic of the powertrain layout and a listing of component efficiencies by output power are

given for the ICE and motor/inverter components of the full-size and half-size vehicles in Figure 1. 

Internal Combustion Engine for a Full-Size PHEV Internal Combustion Engine for a Half-Size PHEV 

Motor & Inverter for a Full-Size PHEV Motor & Inverter for a Half Size PHEV

Internal Combustion Engine for a Conventional Vehicle

Powertrain Layout 

Figure 1: Component Efficiency Maps by Shaft Output Power and Powertrain Layout

174
The model is written in an open-source object oriented programming language called Python and uses

the following open source modules: Numeric Python, Scientific Python, and Matplotlib [4, 5, 6]. The

model does not at this time include details related to engine start-stop (i.e., durability constraints,

vibration, emissions considerations). 

2.2 Duty Cycles Examined 

The scope of this study is limited to urban driving. Two cycles, the Urban Dynamometer Driving

Schedule (UDDS) and the Los Angeles 1992 (LA92) cycle are used to represent aggressive and 

passive urban driving. A simplification of the UDDS cycle has been substituted for the real UDDS in

the interest of simulation time. The simplified UDDS cycle approximates the full UDDS using fewer 

time-steps, which aids in speeding up the dynamic programming algorithm. This is a great time saver 

during dynamic programming runs using higher design space resolution. Graphs of the time-speed 

traces appear below in Figure 2. 

Urban Dynamometer Driving Schedule

(UDDS) Los Angeles 1992 (LA92)

Urban Dynamometer Driving Schedule

Approximation (UDDS-a) 

Close-up of UDDS Cycle Close-up of UDDS-a Cycle 

Figure 2: Drive Cycles Used in this Study 

3 Analysis Results 

The gasoline fuel consumption predicted by the models over the LA92 and UDDS-a cycles is given

below in Table 3. A lower heating value of 32 MJ/liter of gasoline is assumed. The values given for 

fuel economy for this mid-size sedan are reasonable for the North American market. The charge 

sustaining fuel consumption numbers for the PHEVs represent a best case scenario for hybrid fuel

savings. Fuel savings from the PHEV relative to the conventional vehicle arise from three main 

sources: regenerative braking, engine-off operation for the PHEVs, and more efficient operation of the

internal combustion engine by supplementing with the traction motor and battery system. 

Table 3: Gasoline Fuel Consumption for Conventional Vehicle and Charge Sustaining PHEVs 

CYCLE Conventional Vehicle Full-Size PHEV

charge sustaining 

Half-Size PHEV

charge sustaining 

LA92 3512 J/m

10.98 L/100 km

21.43 mpg

1789 J/m

5.59 L/100 km

42.07 mpg

1862 J/m

5.82 L/100 km

40.42 mpg

UDDS-a 3508 J/m

10.96 L/100 km

21.46 mpg

1535 J/m

4.80 L/100 km

49.03 mpg

1522 J/m

4.76 L/100 km

49.45 mpg

175
Highway Federal

Emissions Test (US 

EPA)

2199 J/m

6.87 L/100 km

34.23 mpg

NA NA 

In Table 3, the values given for the highway federal emissions test (US EPA) are for the conventional

vehicle only and are used here to aid the reader in gaining a feel for the range of fuel consumption. 

Figure 3 shows two of the output graphs resulting from the dynamic programming algorithm being 

applied to the PHEV models. Here, the (near-) optimal engine command is shown versus the tractive

effort required between the tires and the wheel. This power measured at the tire-road interface is

referred to as the “roadload.” Two cycles are run in Figure 3: five UDDS-a cycles to the left (29.4

miles/47.3 km) and four LA92 aggressive urban driving cycles (39.3 miles/63.2 km) to the right. 

The engine is not producing power (zero load) for a large portion of the duration of both cycles. When 

the engine is transmitting power, it appears to be supplementing the traction motor as evidenced by

operating commands falling below the line y=x/0.85, where y is the engine command and x is the 

roadload (i.e., relating roadload to power at the engine shaft via the constant transmission efficiency of

Table 1). This engine operation chosen by dynamic programming is a rather complex blending 

algorithm that minimizes fuel consumption within the constraints given. 

We quickly see that duty cycle does make a difference when we contrast the results of the dynamic

programming algorithm run over the UDDS-a with those of the LA-92 cycle (Figure 3). Due to the 

repetition and simplification of the UDDS-a, there is not as much point scatter. We do see similar

trends of increased engine usage with roadload. However, we do not see the same amount of point

scatter at low engine commands and low roadload as we do for the LA-92. 

0

10

20

30

40

50

60

-60 -30 0 30 60

Roadload (kW)

HALF UDDSax5

FULL UDDSax5

Engine Command (kW)

0

10

20

30

40

50

60

-60 -30 0 30 60

Roadload (kW)

FULL Size

HALF Size

Exact

Requirement

Engine C

o

m

mand (k

W)

Figure 3: Engine Command versus Roadload over UDDS-a x5 and LA92 x4

The engine commands versus time for the full-PHEV over five UDDS-a cycles and five LA92 cycles

appear in Figure 4. There are some similarities in the approaches taken by the dynamic programming 

algorithm in both cases. First we see a strong emphasis on engine commands in the 20-kW to 40-kW 

region repeating each cycle iteration (i.e., five times over the five cycle sequence). This is a sensible

approach. If it is known ahead of time that engine power must be used to supplement the electric

capacity of the ESS, it is best to use the engine when the engine is most efficient. As can be seen from

Figure 1, the engine is most effective when at higher power loadings (peak efficiency between 30-50

kW).

Another way to help understand more in-depth what the dynamic programming algorithm is doing is

to examine the energy storage system state-of-charge or capacity versus distance. These data inform us 

of how the battery system is being drawn down—aggressively discharged or at a reduced discharge

rate (due to a blending of battery energy with engine power).

Figure 5 (left) shows the energy storage system discharge over three dynamic programming runs: a

run of one UDDS-a cycle, a run of three UDDS-a cycles, and a run of five UDDS-a cycles. The fullsize PHEV is used on all of the runs. Note that before the vehicle reaches the “electric vehicle centric”

range of about 40 km (for the UDDS), the drawdown is completely along an “electric dominant”

176
drawdown path. As distance increases over this “electric vehicle centric” drawdown path, the slope of

discharge with distance decreases (i.e., a longer distance is required to discharge the same amount of

battery energy). Minimum gasoline consumption is obtained by spreading the battery energy out over 

the entire distance of the cycle (such that the desired end state-of-charge is reached when the desired 

range is reached). 

0

10

20

30

40

50

60

0 2000 4000 6000 8000

Time (s)

UDDSax05

En

gine Command (kW)

0

10

20

30

40

50

60

0 2000 4000 6000 8000

Time (s)

LA92x05

En

gin

e C

omman

d (kW)

Figure 4: Engine Commands for the UDDS-a and LA92 by Time

The right-hand side of Figure 5 shows the same state-of-charge curve for the LA-92 cycle. This time,

the PHEV simulations optimized by dynamic programming over 1 through 5 cycles are shown. In

addition, a charge sustaining (CS) run over one LA-92 distance and a run over five LA-92 cycles 

where the vehicle first has an “electric vehicle centric” discharge followed by charge sustaining

operation are shown. In the “electric vehicle centric” case (FULLx05 EV followed by CS operation),

the vehicle discharges its electrical energy as fast as possible and then goes into charge sustaining

operation. This case provides an interesting contrast to the dynamic programming run over the same 

distance (FULLx05). 

UDDS-a

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70

Distance (km)

FULLx01

FULLx03

FULLx05

UDDSa

State of Charge

LA92 

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80

Distance (km)

State of C

harge

FULL x05 EV 

followed by

CS operation

FULL x04

FULL x03

FULL x01cs

FULL x02

FULL x01 (bold)

FULL x05

Figure 5: SOC by Distance over UDDS-a and LA92 cycles

In the right-hand-side of Figure 5, for the first couple of LA92 cycles, the vehicle has not yet reached

its “all-electric range”. As such, the PHEV can operate in an “electric vehicle centric” mode relying

almost exclusively on battery energy. For distances below the “all-electric range”, the dynamic 

programming algorithm operates the PHEV with an “electric vehicle centric” discharge where possible 

because this type of operation minimizes gasoline consumption. When the vehicle is asked to operate

beyond a range that can be supplied exclusively by battery energy (i.e., beyond the all-electric range),

the dynamic programming algorithm blends engine operation with battery discharge to minimize 

gasoline usage while still driving the requested distance. 

In contrast to the dynamic programming strategies for distances above the “all-electric range”, the 

“electric vehicle centric” strategy draws down as fast as possible and then goes into a charge

177
sustaining mode. Note that charge sustaining operation is around 5% of full (usable) SOC. The results 

in Figure 5 were exclusively for the “full-sized” PHEV. Let us examine the effect that component 

sizing and powertrain architecture can have on consumption rates. 

Figure 6 shows the consumption rates of gasoline and electricity for the full-size and half-size PHEVs

over repeats of the UDDS-a and LA92 cycles. The values for electricity consumption for both full and 

half-size architectures are quite similar after all-electric range is exceeded (around 40 km on the

UDDS-a). This is because both vehicles discharge all of their capacity (albeit in different ways) over

the same distance. Electricity consumption also begins to decrease after all-electric range is exceeded. 

This is because a fixed electrical capacity is being spread out over longer and longer distances. In

contrast, gasoline consumption increases after exceeding all-electric range. Note that the full-size

PHEV does not require gasoline at any point below its all-electric range for the UDDS-a cycle. On that

same UDDS-a cycle, full-size gasoline consumption rates exceed half-size PHEV gasoline 

consumption rates at higher distances. This is not the case under more aggressive driving such as what

is seen on the LA92. The UDDS-a results are due to the smaller motor in the half-size PHEV being

better utilized and thus more efficient. Note that fuel consumption rates are not very different between

the full and half-size PHEVs for most distances. Full-size and half-size electricity consumption rates

are nearly identical after all-electric range is met. The largest disparity is under aggressive urban 

driving prior to all-electric range being met. However, in terms of absolute fuel usage, the fuel usage

of the half-size PHEV is still only about 1/9th the consumption rate of a conventional vehicle over the

same cycle. 

UDDSa

0.0

1.0

2.0

3.0

4.0

0 20 40 60 80

Distance (km)

FULL Electric

FULL Gasoline

HALF Electric

HALF Gasoline

Consu

m

ption (liters gasoline/100 km,

kWh electricity/10 km)

LA92 

0.0

1.0

2.0

3.0

4.0

0 20 40 60 80

Distance (km)

FULL Gasoline

FULL Electricity

HALF Gasoline

HALF Electricity

Consum

ption 

(liters gasoline/100 km,

kWh electricity/10 km)

Figure 6: Fuel Consumptions of PHEVs over UDDS-a and LA92 Cycles

Figure 7 shows us one of the key areas of impact for PHEV technology—gasoline fuel usage by

distance. The set of graphics in Figure 7 are run for the half and full-size PHEV vehicle over the

UDDS-a (mild urban driving) cycle. Each curve on the figure is the result of a dynamic programming 

run optimized for the given distance. That is, for example, the curve labeled “HALFx3” is the fuel

consumption minimized by the dynamic programming algorithm to have the lowest net fuel

consumption after 3 UDDSa cycles back-to-back. This distance aspect is key to understanding what is

going on with the dynamic programming control of PHEVs. In this case, the dynamic programming 

algorithm has a priori knowledge of the cycle and distance to be run and shows us the optimum

control under that circumstance. For distances above the “all-electric range,” the operation is a 

“blending strategy” that blends engine usage with motor usage so as to minimize fuel consumption 

while meeting the drive cycle trace. This blending is in addition to that required due to component size

limitations such as when an acceleration event requires more power than the traction motor can handle

in isolation and thus the engine assists. 

178
HALF-SIZE

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80

Distance (km)

Consumption (liters)

HALFx05 CS

HALFx05

HALFx04

HALFx03

HALFx02

HALFx01

FULL-SIZE 

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80

Distance (km)

Consumption (liters)

FULLx05 CS

FULLx05

FULLx04

FULLx02

FULLx03

FULLx01

Figure 7: Gasoline Consumption of Half and Full-Size PHEVs over UDDS-a Cycles

What is key to note here is that for both the half and full-size PHEVs, a fuel consumption optimally

blended to have minimum fuel consumption at a given target distance does not necessarily have the

minimum consumption at other distances. Figure 8 further expands upon this point by displaying 

gasoline consumption results for the LA92 contrasted with an “electric vehicle centric” control 

strategy. All runs change to charge sustaining operation after reaching their target distances. The

charge sustaining control is optimized by dynamic programming to minimize fuel. 

HALF-SIZE

0

1

2

3

4

5

0 25 50 75 10

Distance (km)

HALFx03

HALFx04

HALFx05

HALFx05 EV

Cycle End Points

Gasoline Consumption 

(liters)

FULL-SIZE 

0

1

2

3

4

5

0 25 50 75 100

Distance (km)

FULLx03

FULLx04

FULLx05

FULLx05 EV

Cycle End Points

Gasoline Consum

ption 

(liters)

Figure 8: Gasoline Consumption over LA92 for Half- and Full-Size PHEV 

Figure 8 shows the results of dynamic programming runs applied to multiple distances of the LA92

driving cycle. The designation “FULLxN” refers to the full-size PHEV optimized by dynamic

programming to run for a number of LA92 cycles equal to N. Dynamic programming for N cycles

operates the vehicle to have near minimum fuel consumption for the given distance. 

Figure 8 also contains the results of the PHEV run in an “electric vehicle centric” mode (labeled “EV”

in the figure) where the engine is commanded to be off unless doing so would cause the vehicle to not 

meet the cycle speed-time trace. Thus, simply put, “use electricity to power the vehicle if at all

possible and supplement with the engine if the motor does not have enough power to meet the cycle

speed-time trace.” When the vehicle has run its target distance, the PHEV begins to run in charge

sustaining mode. That is, the PHEV begins to run similar to a conventional HEV where no significant

net discharge of the energy storage system occurs over time or distance. This can be seen clearly for 

the “FULLx03” case where the vehicle has one of the lowest energy consumptions of the “blended”

modes until one LA92 distance and then changes over to charge sustaining mode. 

As can be seen from Figure 8, gasoline fuel consumption for the “electric vehicle centric” control is

nearly equivalent in all cases to the dynamic programming control set to minimize fuel consumption

over a specific distance. This is because we are continually running the same cycle back-to-back and 

thus it doesn’t matter if one uses the electrical energy all at once at the beginning or spread out

179

0
throughout the cycle. The equivalency between strategies would not necessarily be the case for varied

driving patterns though the effect of driving pattern variation has not been examined in this study. 

Now, let us consider these dynamic programming results in the context of what can be practically

implemented in real life. There are two general strategies that have been presented: a “blended”

engine/motor strategy and an “electric vehicle centric” strategy. The “blended” mode is what the

dynamic programming algorithm optimized to give minimum fuel usage for a given distance most

closely resembles. The engine is run at the most opportune times to minimize fuel consumption. In the 

“electric vehicle centric” strategy, focus is given to supplying transportation energy with electricity

and the engine is only used when necessary to meet vehicle performance constraints. 

Note that for distances below the PHEV’s electric range, the dynamic programming algorithm chooses

an “electric vehicle centric” strategy as this minimizes fuel usage for any range below the electric

range. That is, if we know we will be running less than the all-electric range of the vehicle, the

“electric vehicle centric” strategy is near optimal. For distances longer than the PHEV’s electric range, 

a blended strategy is chosen by dynamic programming. However, let us consider the “cost of being

wrong” in terms of choosing a control strategy for trip distances above the electric range of the vehicle.

Figure 8 shows us that a vehicle with a “blended” strategy optimized for an intended 80 km of intense 

urban driving would use more fuel than the “electric vehicle centric” strategy all the way up to above 

70 km of driving distance. It should be noted that the “electric vehicle centric” control strategy

benefits from a charge sustaining control optimized by dynamic programming. However, so does the

actual blending mode control. Thus, even if we imagine adjusting the fuel consumption rates upwards

during both blended charge depleting operation and charge sustaining operation, the point is still clear:

a vehicle operating in blended mode that deviates from the target distance uses more fuel than a

vehicle using an “electric vehicle centric” approach. Therefore, the “electric vehicle centric” mode is 

essentially the optimal fuel consumption case over most of the distance. In contrast, the “blended”

mode is optimal for specific distances but non-optimal for others. 

4 Conclusions

This study shows that a half-size PHEV using a smaller motor and low power-to-energy ratio batteries

has nearly the same fuel consumption of a full-size PHEV (and in some cases, can have an even lower

fuel consumption), but uses components that can be of lower cost. The biggest disparity in gasoline

fuel consumption rates is at low distances (below the all-electric range of the PHEV) over aggressive 

driving cycles where the engine is often forced to assist when meeting roadloads. Even so, under these

conditions, the absolute fuel usage is low (~1/9th the fuel consumption of a conventional vehicle for

the case of the LA92 cycle). 

Furthermore, this study shows that under optimal control, a blended control strategy uses 

approximately the same amount of fuel as an “electric vehicle centric” approach for known target

distances (and constant driving patterns). However, the penalty for “guessing wrong” on the target

distance and type of travel can be high for a blended strategy. That is, a control strategy optimally 

blended to have minimum fuel consumption at a given target distance does not necessarily have the

minimum consumption at other distances. Thus, it is typically better to run with an “electric vehicle 

centric” control strategy that emphasizes using electricity to supply vehicle power demand to the 

extend possible within the limits of the motor size. 

This work was conducted over urban driving cycles for PHEVs with specific energy capacities.

Further work should cover other energy capacity sizes and types of driving including the effect of

varied driving patterns along a given trip. Additionally, the details of engine on/off including cranking

energy and emissions implications should be addressed.

180
References 

[1] Bertsekas, Dimitri P., Dynamic Programming and Optimal Control, Athens Scientific, 2000

[2] Lin, Chan-Chiao; Peng, Huei; and Grizzle, Jessy W., Control System Development for an AdvancedTechnology Medium-Duty Hybrid Electric Truck, Society of Automotive Engineers 2003-01-3369, 2003 

[3] Simpson, Andrew, Cost-Benefit Analysis of Plug-in Hybrid Electric Vehicle Technology, EVS-22, 2006 

[4] Python Programming Language, http://www.python.org

[5] Numeric Python and Scientific Python, http://www.scipy.org

[6] Matplotlib Python Extension, http://matplotlib.sourceforge.net 

Authors

Michael Patrick O’Keefe, Senior Research Engineer, National Renewable Energy Laboratory,

1617 Cole Blvd; Golden, CO 80401 USA; Phone: 303.275.4268; Fax: 303.275.4415; e-mail:

michael_okeefe@nrel.gov. 2000-Present NREL; 1997-2000 MS Mechanical Engineering and 

MS Inter-engineering: Technical Japanese, University of Washington, USA; 1998 Nippon Steel

Corporation, Japan; 1992-1996 BS Mechanical from Northern Arizona University, USA

Tony Markel, Senior Research Engineer, National Renewable Energy Laboratory, 1617 Cole

Blvd; Golden, CO 80401 USA; Phone: 303.275.4478; Fax: 303.275.4415; e-mail: 

tony_markel@nrel.gov. 1996-Present NREL; 2002-2005 MS Mechanical Engineering from

University of Colorado, USA; 1995-1996 Argonne National Laboratory; 1991-1995 BSE 

Mechanical Engineering from Oakland University, USA. 

181
Summary 

Summary

NREL’s assessment of PHEV technology has added to the body of knowledge and continues the Vehicle 

Systems Analysis team’s long history of timely, innovative, objective, and quality contributions to 

advanced vehicle technology development. The President’s Advanced Energy Initiative defines the goal

of developing a plug-in hybrid vehicle with 40 miles of electric range as a means of changing the way we 

fuel our vehicles. The PHEV research completed in FY06 explored this and many other potential PHEV

design scenarios. PHEV technology has great potential to transition our nation’s transportation energy

demand away from petroleum. However, finding ways to address the high component costs and narrow 

the gap between vehicle design and consumer behavior through technology optimization will be critical to 

achieving the petroleum displacement potential of PHEVs. 

NREL will execute a continuation of its PHEV research in FY07. The goal will be to develop and 

demonstrate potential solutions to technical barriers identified by past research. Emphasis will be placed 

on fuel economy and emissions test procedures and reporting methods, real-world travel behavior

analysis, exploration of alternative economic scenarios, and engine and emissions control system

modeling for PHEV duty cycles. These tasks will contribute to the overall FreedomCAR PHEV research

plan in the areas of analysis, research and development, and test and validation. Finally, the team plans to 

continue strengthening its collaborative relationships with industry colleagues. With NREL’s 

contributions and the contributions of others, the auto industry and the U.S. Department of Energy can 

lead to way toward widespread introduction of PHEV technology. 

 182
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Plug-In Hybrid Vehicle Analysis 

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