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 

Cost-Benefit Analysis of 

Plug-In Hybrid Electric 

Vehicle Technology 

A. Simpson 

Presented at the 22nd International Battery, Hybrid and Fuel Cell 

Electric Vehicle Symposium and Exhibition (EVS-22) 

Yokohama, Japan 

October 23–28, 2006 

Conference Paper 

NREL/CP-540-40485 

November 2006 

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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

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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. 

1
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. 

For a certain distance D, the 

Utility Factor is the fraction of 

total vehicle-miles-traveled 

(VMT) that occurs within the first 

D miles of daily travel. For a 

distance of 30 mi (48 km), the 

utility factor is approximately 

40%. This means that an allelectric PHEV30 can displace 

petroleum consumption 

equivalent to 40% of VMT, 

(assuming the vehicle is fully 

recharged each day). Similarly, 

an all-electric PHEV60 can 

displace about 60%. This lowdaily-mileage characteristic is 

why PHEVs have potential to 

displace a large fraction of pervehicle petroleum consumption. 

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

Figure 1: Daily mileage distribution for US motorists based on 

the 1995 National Personal Transportation Survey 

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]. 

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. 

2 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: 

[ ] ( )

CV

CS

CV

PHEVx

FC

FC xUF

FC

FC 1−= (1) 

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, 

Figure 2 compares several 

contemporary HEVs to their 

conventional counterparts (in the 

case of the Toyota Prius, a 

comparison is made to the Toyota 

Corolla which has similar size and 

performance). At the low end of the 

spectrum, the “mild” HEV Saturn 

Vue achieves a modest reduction of 

less than 20%. The “full” HEV 

Toyota Prius achieves the highest 

percentage reduction (40%) of all 

HEVs currently on the market 

although, in addition to the platform 

enhancements employed in 

production hybrids, it also uses an 

Potential Reduction of Petroleum Consumption in PHEVs

Prius (Corolla)

Civic

Accord Highlander

Escape

Vue

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

Reduction in CS-mode Petroleum Consumption (%)

Total Reduction in Petroleum Consumption (%)

PHEV60

PHEV40

PHEV20

HEV

Challenging region 

for HEV technology 

Figure 2: Potential per-vehicle reduction of petroleum 

consumption in PHEVs 

3
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 

4
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 

Tray/straps + thermal mgmt = 0.06 kg/kg [15] Pack mass 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 

5
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 

chosen such that the average daily 

SOC swing is consistent across the 

range of PHEVs. Figure 4 shows 

the SOC design windows assumed 

in the PHEV cost-benefit model, 

based on cycle-life data presented 

by Rosenkrantz [12] and a target 

battery life of 15 years (assuming 

one full recharge each day). Figure 

4 also shows the resulting average 

daily SOC swing which is 

consistent across the range. 

Electric Motor 

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. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 6

Daily Mileage / PHEVx

0

Design SOC window 

based on PHEVx

Average daily SOC 

swing based on daily 

mileage distribution

Daily mileage probability distribution

Figure 4: SOC design window for PHEVs

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 

6
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 

status listed in the FreedomCAR 

and Vehicle Technologies 

Program Plan [13]. The longterm motor-controller mass is 

based on technology 

demonstrated in the GM Precept 

concept vehicle [14]. The 

engine cost function is based on 

manufacturers’ data provided to 

the EPRI Hybrid-Electric 

Vehicle Working Group 

(HEVWG) [14]. The near-term 

and long-term motor cost 

functions are also based on data 

reported by EPRI [14]. 

Powertrain Components - Normalised Efficiency Curves

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

Normalised Power (P/Pmax)

Efficiency (%)

Engine

Motor-drive

Motor-regen

Battery

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

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 (near-term scenario)

LI-ION (long-term scenario)

2

5 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)

Module Specific Cost ($/kWh)

NiMH (near-term)

Li-Ion (long-term)

Figure 6: Battery design functions and module cost curves assumed for NiMH and Li-Ion technology 

7
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 driveline −−= SOCSOCkP arg ett (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. 

8
Figure 7 shows the battery 

specifications for the spectrum of 

PHEVs in the long-term scenario. 

The total battery energy varies 

from approximately 1.5 kWh for 

the HEV0/PHEV2 to 

approximately 25kWh for the 

PHEV60. The battery power 

varies from approximately 10–

100kW across the range of DOH. 

Figure 7 includes dashed lines of 

constant P/E ratio, which varied 

from approximately 1–50. 

Figure 7 also indicates the 

minimum battery power 

requirement (approximately 

45kW) for the PHEVs to have 

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. 

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

Figure 7: Battery specifications for the spectrum of PHEV 

designs (long-term scenario)

Figure 8 presents the reductions 

in annual petroleum consumption 

and incremental costs for the 

spectrum of PHEVs in the longterm scenario. Taking a 

macroscopic view, we see that 

increasing PHEVx provides 

increasing reduction in 

petroleum consumption. 

Relative to the baseline CV, 

which consumes 659 gal (2494 

L) of petroleum based on 15,000 

mi (24,100 km) each year, the 

HEVs reduce petroleum 

consumption by 20%–28%. The 

PHEVs reduce petroleum 

consumption further, ranging 

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. 

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$-

$2,000

$4,000

$6,000

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. 

$8,

ail Cost In

000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

0 100 200 300 400 500 600

Reduction in Annual Petroleum Consumption (gals.)

Ret crement

HEV0

PHEV2

PHEV5

PHEV10

PHEV20

PHEV30

PHEV40

PHEV50

PHEV60

UDDS AER

vehicles

Figure 8: Incremental costs and annual petroleum consumption 

for the spectrum of PHEV designs (long-term scenario)

9
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 

Engine 

Power 

Motor 

Power 

DOH Battery 

Energy 

P/E 

Ratio 

SOC 

Window 

Fuel 

Cons. 

Elec. 

Cons. 

Retail 

Cost 

(kg) (kW) (kW) (kWh) (1/h) (L/100km) (Wh/km) (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 

Engine 

Power 

Motor 

Power 

DOH Battery 

Energy 

P/E 

Ratio 

SOC 

Window 

Fuel 

Cons. 

Elec. 

Cons. 

Retail 

Cost 

(kg) (kW) (kW) (kWh) (1/h) (L/100km) (Wh/km) (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. 

10
Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 510 15

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0 510

Years after purchase

Cumulative Cost

Years after purchase

Cumulative Cost

15

PHEV40

PHEV20

PHEV10

HEV0

CV

PHEV40

PHEV20

PHEV10

HEV0

Near-term CV Long-term 

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. 

11
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 

[1] Sanna, L., "Driving the Solution: The Plug-In Hybrid Vehicle." EPRI Journal, 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 EPA, 2003. 

[3] "Plug-In Hybrids." California Cars Initiative online, www.calcars.org/vehicles.html. Accessed July 30, 2006. 

[4] 1995 National Personal Transportation Survey (NPTS), npts.ornl.gov/npts/1995/doc/index.shtml. 

[5] "Sales Numbers" hybridCARS.com online, www.hybridcars.com/sales-numbers.html. Accessed July 30, 

2006. 

[6] Plug-In Partners online, http://www.pluginpartners.org/. Accessed July 30, 2006. 

[7] Markel, T.; O'Keefe, M.; Gonder, J.; Brooker, A.. Plug-in HEVs: A Near-term Option to Reduce Petroleum 

Consumption. NREL 39415. Golden, CO: National Renewable Energy Laboratory, 2006. 

[8] Wipke, K.B.; Cuddy, M.R.; Burch, S.D. "ADVISOR 2.1: A User-Friendly Advanced Powertrain Simulation 

Using a Combined Backward/Forward Approach." IEEE Transactions on Vehicular Technology; Vol 48, No. 6, 

1999; pp. 1751-1761. 

[9] Rush, D. Market Characterization for Light Duty Vehicle Technical Targets Analysis, National Renewable 

Energy Laboratory, 2003. 

[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 

Research Institute (EPRI), 2001. 

[15] Delucchi, M. "Electric and Gasoline Vehicle Lifecycle Cost and Energy-Use Model." Institute of 

Transportation Studies. University of California, Davis, 2000. 

[16] Markel, T.; Simpson, A.; "Plug-In Hybrid Electric Vehicle Energy Storage System Design," Proc. Advanced 

Automotive Battery Conference; 2006, Baltimore, Maryland. 

[17] Gonder, J.; Simpson, A. "Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles," 

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.

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This paper presents a comparison of vehicle purchase and energy costs, and fuel-saving benefits of plug-in hybrid 

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