TRB 10--1698 

A Plug-in Hybrid Consumer Choice Model with Detailed Market 

Segmentation 

November 11, 2009 

Word Count: 8,777 (including 2 tables and 7 figures) 

Zhenhong Lin 

Oak Ridge National Laboratory 

National Transportation Research Center 

2360 Cherahala Boulevard 

Knoxville, Tennessee 37932 

Phone: 865-946-1308 

Fax: 865-946-1314 

Email: linz@ornl.gov 

David L. Greene 

Oak Ridge National Laboratory 

National Transportation Research Center 

2360 Cherahala Boulevard 

Knoxville, Tennessee 37932 

Phone: 865-946-1310 

Fax: 865-946-1314 

Email: dlgreene@ornl.gov 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 1 

ABSTRACT 

This paper describes a consumer choice model for projecting U.S. demand for plug-in 

hybrid electric vehicles (PHEV) in competition among 13 light-duty vehicle technologies over 

the period 2005-2050. New car buyers are disaggregated by region, residential area, attitude 

toward technology risk, vehicle usage intensity, home parking and work recharging. The nested 

multinomial logit (NMNL) model of vehicle choice incorporates daily vehicle usage 

distributions, refueling and recharging availability, technology learning by doing, and diversity 

of choice among makes and models. 

Illustrative results are presented for a Base Case, calibrated to the Annual Energy Outlook 

(AEO) 2009 Reference Updated Case, and an optimistic technology scenario reflecting 

achievement of U.S. Department of Energy’s (DOE’s) FreedomCAR goals. PHEV market 

success is highly dependent on the degree of technological progress assumed. PHEV sales reach 

one million in 2037 in the Base Case but in 2020 in the FreedomCARGoals Case. In the 

FreedomCARGoals Case, PHEV cumulative sales reach 1.5 million by 2015. Together with 

efficiency improvements in other technologies, petroleum use in 2050 is reduced by about 45% 

from the 2005 level. After technological progress, PHEV’s market success appears to be most 

sensitive to recharging availability, consumers’ attitudes toward novel technologies, and vehicle 

usage intensity. Successful market penetration of PHEVs helps bring down battery costs for 

electric vehicles (EVs), resulting in a significant EV market share after 2040. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 2 

Introduction 

Light-duty vehicles remain the largest oil consumers and the largest source of greenhouse 

gas (GHG) emissions in the transportation sector, accounting for 60% of transportation 

petroleum use and an equivalent share of transportation’s 1.9 million metric tons of CO2

emissions in 2008 (1). According to the U.S. DOE’s most recent assessment, existing policies 

including higher fuel economy standards will only prevent an increase in petroleum use and 

greenhouse gas emissions by light duty vehicles through 2025 (1). For the United States to 

reduce oil dependence and reduce economy-wide greenhouse gas emissions by 50-80% by 2050, 

light duty vehicles will have to become much more energy efficient and transportation’s energy 

sources will need to be substantially decarbonized (2). These growing concerns about energy 

security and global warming will require the development of new propulsion technologies for 

light duty vehicles. PHEVs integrate the energy efficiency of hybrid powertrains with the ability 

to partially substitute electricity for petroleum. On February 17, 2009, the American Recovery 

and Reinvestment Act of 2009 (ARRA) was signed into law, providing up to $7,500 of tax credit 

for each new PHEV purchase starting from 2010 (3). 

How much reduction in petroleum use and greenhouse gas emissions can be expected from 

PHEVs depends on many factors and remains an open question (4, 5, 6, 7, 8). Market 

acceptance remains uncertain due to the current high cost of PHEV batteries and uncertainty 

about the gains achievable through technological change and learning-by-doing. There are also 

questions about how consumers will evaluate the opportunity to recharge vehicles at home, or 

react to other potential benefits such as the potential to use vehicles as a source of back-up power 

or the value of vehicle-to-grid (V2G) connections. Even the value consumers will place on 

increased fuel economy remains contentious (2). Market acceptance of PHEVs will also depend 

on the progress made by other, competing powertrain technologies. The fuel economy 

performance of PHEVs and the share of their energy use that will come from electricity can also 

depend strongly on how consumers use the vehicles. Their impact on the grid will depend on 

market success, recharging behavior and usage patterns. The greenhouse gas benefits of 

substituting electricity for petroleum will depend on the degree to which electricity generation 

becomes decarbonized over time. 

 To understand the likely impacts of PHEVs, it is necessary to simulate market demand 

for PHEVs and their use by representing the attributes and behaviors of consumers at a relatively 

detailed level. A consumer choice model relying on the preferences of a single, typical 

consumer would not be able to reflect key factors such as access to recharging, daily driving 

patterns and willingness to accept technological risk. Because PHEVs are a novel vehicle 

technology, much remains to be learned about how consumers will evaluate their attributes, as 

well as how they are likely to be operated. The approach taken in developing the (Oak Ridge 

National Laboratory (ORNL) PHEV model is to create a framework for integrating data and 

behavioral models at an appropriate level of detail, whether or not the data are fully available or 

the behaviors are fully understood at the present time. As more is learned about PHEVs and 

consumers’ preferences towards them, the model will be continuously updated and improved. 

 This paper describes a model of consumer choice and use of 13 advanced vehicle 

technologies applied to two vehicles types: passenger cars and light-duty trucks. The household 

market is disaggregated into 1,458 segments by six dimensions: nine regions, three types of 

settlements, three attitudes towards technology risk, three categories of vehicle use intensity, 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 3 

three categories of parking and therefore access to home recharging, and two categories of access 

to recharging at work. 

 The next section of the paper describes the theory and structure of the ORNL PHEV 

model, focusing on features that are especially important to the market for PHEVs. Key issues in 

calibrating the model are highlighted. Next, a Base Case scenario, calibrated to the AEO 2009 

Reference Case, is described. The Base Case is intended to characterize the future as one 

without explicit new policy intervention nor significant technological breakthroughs. The 

FreedomCARGoals Case is defined as an optimistic case to illustrate the behavior of the model. 

 

THE ORNL PHEV MODEL: FRAMEWORK AND METHODOLOGY 

Model Framework 

The ORNL PHEV model is designed to predict vehicle choice probabilities (market 

shares) for advanced technologies dependent on consumer attributes, changes in the cost and 

performance of advanced technologies, energy prices, and policies. The core of the model is a 

nested NMNL module that estimates the probabilities that consumers in each of the 1,458 market 

segments will choose each of the 13 technologies. Each market segment represents a fraction of 

the potential buyers of new light-duty vehicles in the United States. 

 The model’s predictions cover the period 2005 to 2050. Sales by market segments are 

aggregated to national sales by vehicle technology. A national vehicle stock turnover (scrappage) 

module estimates vehicle retirements by age and calculates the evolution of the vehicle stock by 

vehicle type and region (Figure 1). Vehicle use (annual miles of travel) and energy efficiency by 

vintage and technology type are used to compute energy use and CO2 emissions. 

 Several recursive feedbacks loops are included. The previous year’s sales volumes by 

technology affect the number of makes and models available for each technology, cumulative 

sales affect vehicle purchase prices via learning-by-doing, and the previous year’s stock of 

alternative fuel vehicles affects the availability of alternative fuels. 

 The ORNL PHEV model draws information on households and their characteristics from 

census data and national travel surveys, obtains projections of vehicle sales, stocks, vehicle travel, 

energy efficiency and energy prices from the Energy Information Administration’s AEO (1) and 

makes use of vehicle technology characterizations developed by vehicle simulation modelers at 

Argonne National Laboratory (9). However, in some cases the data needed do not exist or are 

highly uncertain or insufficiently detailed. In such cases we have made plausible assumptions 

with the intent of improving the assumptions as research on PHEVs and other advanced 

technologies progresses. 

 In the following sections, key modules of the model are further described. 

THE NMNL MODULE AND CHOICE SET 

The consumer choice module is an NMNL model with fixed preferences for most vehicle 

attributes and random utility components varying across individuals and nests. In principle, each 

market segment could have different preferences for all attributes. However, there is insufficient 

information to specify preferences at such a level of detail. For example, in the current version 

of the model, the proportion of consumers in each technology risk category (early adopter, early 

majority, late majority) does not vary by region, residential area or any other category. Other 

attribute values, on the other hand, do vary across market segments. The value of fuel economy 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 4 

and range, for example, will vary with factors that affect vehicle travel, including region, 

residential area and daily driving distribution. At present, preferences for attributes do not vary 

over time, except as energy prices change. 

 At the top of the NMNL decision structure is the choice between a passenger car and a 

light-duty (LD) truck (Figure 2). Within each vehicle type consumers choose among thirteen 

advanced powertrain technologies, grouped into three classes: (1) conventional/HEV, 

(2) hydrogen, and (3) battery-electric vehicles. Within the conventional/HEV class, technologies 

are grouped into conventional internal combustion engines (Spark Ignition Conv and 

Compression Ignition Conv), hybrid electric vehicles (SI HEV and CI HEV), and SI PHEVs. SI 

PHEVs come in three types according to their on-board electricity storage capacity and electric 

motor power: (1) 10-mile all electric range (AER), (2) 20-mile AER, and (3) 40-mile AER. All 

the PHEV designs (SI PHEV10, SI PHEV20, and SI PHEV40) are blended hybrids with limited 

all-electric capabilities. It is relatively easy to change the model’s nesting structure or to add or 

subtract technologies from the choice set. 

The probability that a consumer will choose technology i, given a choice among the 

vehicles in nest jkl, is given by a NMNL function of the weighted attribute values of technology i. 

 





h

c

c

i jkl jkl hjkl

jkl ijkl

e

e

p 



|

 |  1

i

i jkl p

(1) 

In equation 1, cijkl is the generalized cost, or utility value in present value dollars of 

technology i in nest jkl. The parameter βjkl determines the sensitivity of technology choices in 

nest jkl to their generalized cost. Each technology’s generalized cost is comprised of a weighted 

sum of functions of the values of its attributes. Let the zth attribute’s value be represented by 

xzijkl , its function be fz(xzijkl), and its weight wzjkl . The generalized cost for choice ijkl is given 

by equation 2. Note that generalized cost cijkl could vary across market segments. 

(2) 

At present the following attributes are included in the generalized cost function. 

 vehicle retail price 

 fuel and electricity cost 

 battery replacement cost 

 acceleration 

 cargo space 

 towing 

 range 

 home backup power 

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Lin and Greene 5 

 refueling and recharging accessibility cost 

 model availability 

 technology risk 

 policies 

 purchase subsidy, tax credit, HOV access, free parking, etc 

 V2G costs and revenues 

 implemented but not reflected in the current scenario runs 

 greenness (placeholder) 

Generalized costs of the choices within a lower nest are “averaged” and passed up to the next 

level. 

ln( ) 1     i

c

jkl

jkl ijkl

jkl

c e





(3) 

The choice among nests at the next level is a logit function of their generalized costs, cjkl , 

and price sensitivity at the next level of the nest, represented by βkl. 

 The unconditional choice probability for technology i in nest jkl, is the product of the 

conditional choice probabilities. 

ijkl i jkl j kl k l l p p p p p  | | |

(4) 

At present the model does not include a “no buy” option, also referred to as an “outside 

good.” As a consequence, total light-duty vehicle sales are fixed at the levels of the AEO 

projection to which the model is calibrated. A “no-buy” option will be included in future 

versions of the choice model. 

LDV Sales Market Segmentation 

Through market segmentation, market barriers associated with consumer attributes can be 

exposed and equity implication of policies can be inferred. The model groups all U.S. new lightduty vehicles (LDV) consumers by six dimensions (region, area, attitude toward technology risk, 

vehicle usage intensity, home parking, and work recharging) into 1,458 segments. 

The model is calibrated to AEO 2009 projections of LDV sales by each census division over the 

period 2005-2030, extended to 2050 by assuming growth continues at the average growth rate 

from 2015 to 2030. LDV sales within each census division are split into three residential areas, 

Urban, Suburban, and Rural, based on the census population data (10). 

 Within each area, sales are further subdivided by attitude toward technology risk into 

Early-Adopter (16%), Early-Majority (34%), and Late-Majority (50%), based on innovation 

diffusion theory (11, 12). An Early-Adopter perceives some benefit of owning a vehicle with a 

novel technology (novelty measured by the accumulated stock of the technology). Members of 

the Early-Majority perceive a cost and those in the Late-Majority perceive an even higher cost. 

Benefits or costs decay in a manner analogous to a learning-curve, approaching zero as the 

vehicle stock of that technology increases and the technology becomes less novel. 

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Lin and Greene 6 

 Within each attitude group, three levels of vehicle usage intensity are defined, ModestDriver (driving 8,656 miles annually), Average-Driver (16,068 miles), Frequent-Driver (28,288 

miles). The shares of these driver types by region and area are estimated from the National 

Household Travel Survey 2001 (13). Each driver type is also characterized by a daily vehicle 

usage distribution, explained later in this paper. 

The LDV market is then further split by home parking amenities into Garage/Carport, 

Offstreet Parking, and Neither. Consumers in the Garage/Carport group can recharge their 

PHEV at home, while those in the other two groups cannot. The share estimates are based on 

the number of households by each parking type in each census division and each residential area, 

according to the 2005 American Housing Survey (14). Similarly, the market is also split by 

work recharging availability into With_Work_Recharge (20%) and Without (80%), according to 

an early survey (5). 

LDV sales by each segment change over time but are exogenous. That is, no LDV demand 

competition between segments is considered in this paper. Within each segment, the LDV sales 

are endogenously split by the NMNL module into sales by vehicle technology type. 

Vehicle Daily Usage Distribution 

In practice, a PHEV with the battery fully charged will first run on blended chargedepleting (CD) mode then switch to charge-sustaining (CS) mode when the battery is depleted to 

a certain low level of state-of-charge (4). As a consequence, the driving distance between 

recharge intervals can strongly impact the share of electricity or fuel consumption, as also 

observed in survey research (4, 15). To accurately estimate energy consumption and GHG 

impact, it is necessary to characterize both driving behavior and recharge behavior. 

Early research on PHEV users indicates that the overwhelming majority of drivers with 

home recharging will fully recharge their vehicles during the night (15). Then, the task can be 

simplified as estimating the distribution of daily vehicle usage. However, longitudinal travel 

data are not readily available, especially nationwide. As an alternative approach, Greene (16) 

assumes the gamma distribution for vehicle daily usage and then obtains maximum likelihood 

estimates of the parameters based on longitudinal refueling survey data of over 2000 vehicles. 

The gamma distribution is suitable for representing household vehicle travel due to its nonnegativity and skewness flexibility. Another desirable attribute of the gamma distribution, 

making it easy to estimate is that it contains only two parameters: shape and scale. The product 

of shape and scale equals the mean of the distribution, which can be treated as the average daily 

vehicle usage. The difference between mean and scale is the mode of the distribution, i.e. the 

most frequently occurring daily distance. It may be a reasonable approximation to assume that 

for full-time workers whose main commute mode is driving, the commuting round-trip distance 

is the mode of their daily vehicle usage distribution. Therefore, the distribution can be estimated 

by only knowing the average daily driving distance and the commuting distance. 

A subset of the NHTS2001 data was selected, containing 3,755 new car owners that are 

full-time workers, who mainly drive to work and rarely work at home. A data set including their 

weight in the population, annual usage of the primary vehicle, commuting distances, and 

associated region and residential area are extracted. 3,755 gamma distributions of daily vehicle 

usage are then estimated by following the previously described relationship between the 

distribution parameters and the daily usage mean and mode. 

For simplicity, these 3,755 distributions are clustered in three levels of vehicle usage 

intensity: Modest-Driver, Average-Driver, and Frequent-Driver. The average distribution of 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 7 

each intensity group is estimated for all drivers within the same intensity group, as shown in 

Figure 3. The shares of Modest-Driver, Average-Driver, and Frequent-Driver by region and area 

are also obtained. 

Vehicle Characterization 

The data characterizing vehicle technologies were provided by researchers at Argonne 

National Laboratory (9, 17, 18). The process of constructing the data is fully explained in their 

Multi-path draft report (18). This paper highlights several important issues and presents some 

key data. 

An LDV is is assumed to consist of a powertrain and a glider. Within each of the car and 

light-duty truck types, 13 powertrain technologies share the identical glider. The same set of 

vehicle performance standards, e.g. 9 second for 0-60 mph and 65 mpg at 6% grade for 20 

minutes, applies to all 26 car and truck technologies. The Powertrain System Analysis Toolkit 

(PSAT) from Argonne National Lab is then used to size the powertrain components in meeting 

these standards, and simulate the vehicle operation with the two EPA test cycles (18). 

Component costs are calculated based on component sizes. 

One performance requirement that varies among technologies is range. All technologies 

with a gasoline or diesel tank are assumed to carry 60 kg of fuel onboard. Hydrogen related 

technologies are designed to have a range of 190 miles during the period 2005-2015 and 320 

miles starting from 2030. The range target and efficiency determines hydrogen storage capacity. 

Values between 2015 and 2030 are based on linear interpolation. For PHEV, battery capacity is 

determined by the required AER range and adds to the total vehicle range. The range for EV is 

designed to be 150 miles for both vehicle types. 

The vehicle retail price is the sum of glider and powertrain component costs multiplied by 

1.5, as shown in Equation 5. The details of each component cost function fi(Si) can be found in 

(18). Such a retail price varies over time, due to component cost reduction, and thus reflects 

technological progress due to R&D, but does not reflect lack of production experience or scale. 

That is, the price, called fully-learned price, is based on mass production and sufficient learning 

experience. 

 1.5 (   ( )) 

i

g i Si P C f

(5)

One common approach to represent learning effects is the experience curve, in which the 

logarithm of new product prices has a linear relation to the logarithm of cumulative production 

(19). A problem with the experience curve is that price can approach zero when the cumulative 

production is sufficiently large. Alternatively, the price can be assumed to approach a horizontal 

asymptote (20). Price Pn is an exponential function of the cumulative production n, governed by 

the fully-learned price of the vehicle technology P∞, the fully-learned price of the reference 

technology Pref, and two parameters a and b (Equation 6). With the asymptotic learning-bydoing function, the retail price is formed by amplifying the fully-learned price difference 

between the vehicle technology of concern and the reference technology (gasoline conventional 

vehicle). The fully-learned prices are based on the data in Table 1. The parameters a and b are 

calibrated by ensuring the equivalent varying progress ratio falls close to the mean of the 

empirical range 0.7-0.9 (19). 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 8 

 (1 )(  )   0  P ae P P P b ref ref

bn

n

(6)

 Energy efficiency is the key factor determining energy costs. PSAT provides detailed 

energy efficiency characterizations, including 4 fuel efficiency estimates for PHEVs (CD and CS 

modes, UDDS and HWFET cycles) and 2 electricity efficiency estimates (CD only, 2 test cycles), 

wherever applicable. 

The fully-learned prices and UDDS CS fuel efficiencies are presented in Table 2. The 

Base Case represents engineering potential according to the literature and expert estimates. In 

the FreedomCARGoals Case, for non-hydrogen related technologies, cost estimates are based on 

the assumption that the DOE FreedomCAR goals are met as scheduled (21). Energy efficiency 

estimates represent a postulated future market that attaches a very high priority to vehicle 

efficiency (18). For performance and prices of hydrogen-related technologies in 

FreedomCARGoals Case are based on engineering potential, except reflecting the DOE goals of 

battery technology for fuel cell PHEV. The price and efficiency estimates in the 

FreedomCARGoals Case are significantly more optimistic than the Base Case and generally 

more optimistic than other studies (22, 23, 24). 

PHEV and EV batteries must endure deep discharge and frequent recharge, raising the 

concern on battery life and replacement cost (4, 18). This paper assumes a 10-year or 100,000 

mile life for PHEV and EV batteries, whichever comes first depending on vehicle usage intensity. 

Battery costs are multiplied by 1.5 to obtain retail price equivalents, and discounted to compute 

the present value of battery replacement. No learning by doing is considered for battery 

replacement cost. The need to consider a longer battery life is acknowledged as well as the 

possibility that if replacement is needed, the replacement battery will likely be a cheaper one, 

either used or produced for the aftermarket. Battery cost and capacity data are shown in Table 2. 

Refueling and Recharging Availability 

Lack of available public recharging is more likely to be a barrier for potential PHEV 

consumers without home or work recharging. For hydrogen related technologies, refueling 

availability in the early stage is a more obvious barrier for all potential consumers. A FC PHEV 

will face both availability issues. 

 It is difficult to quantify the value of public recharging to PHEV owners without home or 

work recharging, because recharging is not required to use the vehicle. The frequency of visiting 

a public recharging station, if available, depends on how much the owner wants the vehicle to be 

more like a true PHEV rather than a regular hybrid vehicle. An early survey found that PHEV 

owners are generally eager to plug in (15). Thus, for PHEV owners without home or work 

recharging, one recharge is assumed to be needed during those days with driving distance 

exceeding the CD range of PHEV. For the other days during the year, the probability of wanting 

a recharge is proportional to the ratio of daily distance to the CD range. For PHEV owners with 

home or work recharging, a full recharge per day is assumed. 

 To quantify the annual travel time related to refueling or recharging, the linkage between 

fuel availability and travel time of each refueling or recharging trip needs to be established. The 

refueling travel time is found to follow a power function of the number of stations, when the 

locations of stations are optimized to minimize refueling travel time (25, 26, 27). If the refueling 

or recharging availability is known (measured by the ratio of the number of alternative stations to 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 9 

the number of gasoline stations) then the annual refueling or recharging travel time can be 

determined. 

The growth of fuel or recharge availability is assumed to be a logistic function of the ratio 

between the stock of the particular technology and today’s SI Conv. stock. The functions for 

gasoline and diesel are calibrated to historical data including vehicle stock and the number of 

diesel and gasoline stations. The functions for hydrogen and electricity recharge are based on 

analogy to gasoline and diesel but reflect the relative cost of these stations. 

Model Diversity 

In both the NMNL model and in reality, greater diversity of choice among makes and 

models of vehicles leads to greater utility for consumers. This is because the value attached by 

the consumer to each model is uncertain and thus the wider the range of options the more likely a 

consumer is to find one that suits his preferences (28, 29). New vehicle technologies are very 

likely to be offered with a limited number of models, putting them at disadvantage relative to 

established technologies. 

 According to the MNL theory, the value of model diversity Vd can be estimated as a 

function of the number of makes and models offered nj, and the ratio of a parameter γ to the price 

slope μ, as in Equation 7. Assuming γ =2/3, the estimated cost of lack of diversity is in the range 

of $3000-$8000 if only one make and model is available and still as large as $500-$1000 even if 

40% of all makes and models offer the particular technology, depending on relative price 

elasticity of the technology to its direct competitors (28). 















  N

n

V j

d e log





(7)

It is difficult to predict model diversity as this involves uncertain marketing strategies. 

However, it is reasonable to assume that the number of available models increases with sales of 

the same technology. This paper assumes the maximum model number N is 80 for each 

technology in the choice set and calibrates an asymptotic exponential relationship (Equation 8) 

between model number and sales to the historical sales and model numbers of hybrid vehicles. 

80 , 0 Sales      n a e a b b

j

(8)

Other Model Assumptions 

The home backup power market is growing, and if a PHEV can be plugged in at home, it 

can also be used as home backup power, thereby providing additional value to consumers who 

need home backup power. Early surveys (30) asked consumers their willingness-to-pay for 

PHEV as a home backup power. The median value by region is used in this paper. 

 V2G revenue could be a significant value of owning a PHEV or EV. The model has 

structurally implemented this component in the NMNL utility function. However, due to limited 

knowledge in projecting demand for peak power and reserved capacity by region, the potential 

value is not reflected in this paper. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 10 

Scenario Definition 

Two scenarios are used to illustrate the behavior of the model, Base Case and 

FreedomCARGoals Case, with the two sets of vehicle characterization data previously presented. 

The same energy prices are used in the two cases. The 2005-2030 prices of gasoline, diesel, and 

electricity by census division come from AEO 2009 Updated Reference and extrapolated for 

2031-2050 at a constant annual rate of change equal to that from 2015 to 2030. The average 

(unweighted across census divisions) gasoline retail price grows from $2.77/gallon to 

$4.38/gallon in 2050. Hydrogen price comes from the HyTrans model (31, 32), decreasing from 

$3.5/gge in 2005 to $2.8/gge in 2050. 

 The American Recovery and Reinvestment Act of 2009 (ARRA) has been signed into 

law and therefore its PHEV subsidy policy is reflected in both the Base and FreedomCARGoals 

cases. If the PHEV has a gross vehicle weight rating less than 14,000 pounds, has a battery 

capacity no less than 4 kWh, and is purchased before the manufacture’s PHEV cumulative sales 

reaches 200,000 units, the buyer earns the tax credit starting at $2,500 plus additional $417 for 

each kWh above 5kWh. The total amount is capped at $7,500. 

 The Base Case maintains the current policy environment and is calibrated to the 2009 

AEO Updated Reference Case (1) regarding on-road LDV VMT and stock and sales shares of 

cars and light-duty trucks. On-road LDV travel begins at 2,634 billion VMT in 2005, increasing 

by 37% by 2030. The LDV stock grows from 222 million in 2005 to 294 million by 2030, a 

32% increase. The sales share of light-duty trucks decreases from half of LDV sales in 2005 to 

36% by 2030. 

RESULTS 

In the Base Case, conventional gasoline vehicles initially dominate the market with small 

shares for hybrid (HEV) and diesel vehicles. Even in 2050, conventional internal combustion 

engine (ICE) vehicles account for more than half of the light-duty vehicle market (Figure 4). 

Over time, as technology costs fall and performance improves, first HEVs and later PHEVs gain 

increasing shares of the market. HEV and PHEV are more successful in the passenger car 

segment, while diesel engines do better in the light-duty truck market. Sales of hybrid cars and 

light-duty trucks reach 2 million per year by 2022 and climb to 5.8 million by 2050. PHEV sales 

grow to one million per year in 2037 and eventually reach 3 million by 2050. 

Fuel demand does not increase in the Base Case due to the improvements in fuel 

economy across all technologies. In 2050, light-duty vehicles are consuming just over 8 million 

barrels per day of petroleum, nearly all of which is gasoline (Figure 4). Advanced spark-ignition 

internal combustion engine vehicles account for most of the petroleum use, 5.3 million barrels 

per day, due to their lower fuel economy. 

 The performance improvements but more importantly the cost reductions of the 

FreedomCARGoals Case have a major impact on the success of both hybrids and PHEVs. By 

2050 advanced ICE vehicles account for only 20% of the new vehicle market (Figure 5). Sales 

of hybrid vehicles reach 7.5 million units by 2020 and remain nearly constant thereafter. PHEV 

sales take off after 2020, increasing from just over 1 million units in that year to 7 million units 

by 2035. Cumulative sales of PHEVs reach 1.5 million by 2015, exceeding the goal of 1 million 

PHEVs on the road by that year. After 2030, sales of battery electric vehicles become significant, 

rising to over 2 million units by 2050. 

In both the Base and FreedomCARGoals cases, PHEV-10 vehicles dominate the PHEV 

market. In the PHEV consumer choice module, PHEV-10s, PHEV-20s, and PHEV-40s occupy 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 11 

the same nest as close substitutes. Because PHEV-10s achieve most of the fuel economy 

benefits of the longer AER PHEVs at a lower cost, they dominate the PHEV market. While 

PHEV-10 sales reach 7.5 million in 2050, combined sales of PHEV-20s and PHEV-40s amount 

to less than 100,000 units. At present, little is known about the value consumers might attach to 

all electric range or the potential for greater on-board electricity storage to create opportunities 

for adding value to a vehicle. As a result, this study may be undervaluing AER and related 

opportunities, such as V2G connection. 

 PHEV market shares within each segment vary greatly as a result of market 

disaggregation. In Figure 6, the gray shaded area represents the envelope of market shares for all 

1,458 segments. The no-marker black line shows the PHEV10 market share within the reference 

segment (SouthAtlantic, Suburban, EarlyAdopter, FrequentDriver, Garage, and 

NoWorkRecharge) during the period 2010-2025. By changing one segment dimension at a time, 

another five segment curve representing their PHEV10 market shares are also shown to illustrate 

the variation of segment choice behavior. Early-adopter, frequent drivers, with garage parking in 

urban areas of the South Atlantic region are close to the maximum market penetration. The same 

consumer who has neither a garage/carport nor off-street parking nor the opportunity to recharge 

at work (and thus effectively nowhere to plug in his vehicle) is essentially uninterested in a 

PHEV. A similar consumer having a garage but a low tolerance for technology risk (late 

majority) is initially uninterested but gains interest as PHEVs penetrate the market and by 2023 

is in essentially the same place as his early-adopter counterpart. Those traveling fewer miles 

each day value the PHEV’s fuel economy less and are about half as likely to choose a PHEV. 

The sudden dip in market shares after 2016 is due to the expiration of government incentives for 

PHEVs. Generally, those segments that are more interested in PHEVs are more sensitive to the 

tax credit incentive. Clearly the detailed segmentation of the market matters and may be crucial 

to accurately modeling the earliest phases of market evolution. 

Light-duty vehicle fuel use in the FreedomCARGoals Case falls from about 8.5 mmbd in 

2009 to less than 5 mmbd in 2050 (Figure 5). Yet even with HEVs, PHEVs, and EVs making up 

80% of vehicles on the road in 2050, the reduction in petroleum use by light-duty vehicles is 

only about 40% versus the Base Case. However, the Base Case includes very optimistic 

assumptions about technological advances in energy efficiency, and represents significant 

reductions in petroleum consumption over the original 2009 AEO Reference Case. In the 

AEO2009 Reference Case, light-duty petroleum use reaches 9.3 mmbd in 2030, the final year of 

the projection. Still, this is not enough to achieve the up to 80% reductions in greenhouse gas 

emissions that are likely to be necessary across the economy to protect against dangerous climate 

changes (33, 34). 

 Even in the FreedomCARGoals Case, the quantity of electricity required for light-duty 

vehicles is modest relative to total U.S. electricity supply. In 2050, PHEVs and EVs use 120 

billion kWh of electricity (Figure 7). This amounts to only 3% of the total electricity generated 

in 2008 and 2.5% of the projected generation supplied to the grid in 2030. If most light-duty 

vehicle electricity demand could be directed to off-peak hours it would require little or no 

additional capacity and improve the overall efficiency of electricity generation in the United 

States. 

SUMMARY AND NEXT STEPS 

A model that projects market shares of PHEVs and other advanced vehicle technologies for 

a highly disaggregated U.S. market has been developed and tested. Market success is seen to 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 12 

differ significantly across the market segments, indicating that the segmentation is important to 

adequately representing the evolution of markets for advanced powertrain technologies. The 

model’s predictions are highly dependent on the degree of technological progress assumed. In 

addition, much remains to be learned about the 1,458 market segments represented in the model, 

as well as how consumers are likely to value the novel attributes of advanced technologies. As 

research on in these areas continues to progress, the PHEV model can be updated and enhanced. 

 Future research and development will focus on improving the model in several key areas: 

 elaboration of the model’s structure to include a no-buy option, 

 expansion of the technology list to include, e.g., flex-fuel vehicles, 

 enhancement of the disaggregated market data, e.g., with a more precise estimation of the 

distribution of the population by preference for technology risk and daily driving 

distributions, 

 addition of lifecycle greenhouse gas emissions estimates and the sensitivity of emissions 

from electricity generation to GHG mitigation policies, 

 automation of the calibration of the model to new AEO projections and, 

 development of a convenient user interface. 

ACKNOWLEDGEMENT 

The authors gratefully acknowledge the support of this research by the Office of Vehicle 

Technologies, U.S. Department of Energy. They thank Dr. Philip Patterson, U.S. DOE for his 

insistence on a detailed disaggregation of the PHEV market and also Dr. Danilo Santini of 

Argonne National Laboratory who for several years urged the authors to try such an approach. 

We also thank our colleagues, Mr. Jake Ward of the U.S. DOE, Dr. Aymeric Rousseau, Dr. 

Margaret Singh and Steve Plotkin of ANL, Drs. Tom Turrentina, Ken Kurani, and Andy Burke 

of the University of California at Davis, and Sujit Das of ORNL for their many valuable 

contributions to the conceptualization, implementation, and calibration of the PHEV Model. 

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TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
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Lin and Greene 15 

LIST OF TABLES 

1 Vehicle Retail Price and Fuel Economy (2005 USD/MPGGE) 

2 Battery Cost and Capacity for PHEV and EV (2005 USD/kWh) 

LIST OF FIGURES 

1 Illustrative diagram for the model. 

2 Nest structure. 

3 Daily vehicle usage by vehicle usage intensity. 

4 LDV sales and fuel use by powertrain technology: base case. 

5 LDV sales and fuel use by powertrain technology: FreedomCARGoals. 

6 PHEV market shares for selected market segments: FreedomCARGoals. 

7 Light-duty vehicle electricity use: FreedomCARGoals.

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 16 

TABLE 1 Vehicle Retail Price and Fuel Economy (2005 USD / MPGGE) 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 17 

TABLE 2 Battery Cost and Capacity for PHEV and EV (2005 USD / kWh) 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 18 

NMNL

Module

LBD

V2G

Stock

Technology

Risk

Model

Availability

Range & Fuel Availability

Sales Policy

Maker

Vehicle Usage

and Scrappage

Fuel and 

Electricity Use

Tailpipe and 

Lifecycle GHG

input

output

feedback

ANL/PSAT

ORC Survey

Census2000

EIA/AEO2009

NHTS2001

UCD

PHEV Survey

ANL/Greet

Refuel&Recharg

Infrastructure

Purchase Discount, Tax 

Credit, Free Parking, 

Free HOV, etc

FIGURE 1 Illustrative diagram for the model. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 19 

FIGURE 2 Nest structure. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 20 

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

0 50 100 150 200 250

X = Daily Vehicle Usage (mile)

probability density (100%) 

Modest Driver X = Γ(1.68, 14.11)

Average Driver X = Γ(1.90, 23.20)

Frequent Driver X = Γ(1.80, 43.05)

FIGURE 3 Daily vehicle usage by vehicle usage intensity. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 21 

SI Conv

Car

SI Conv

LTK

SI HEV Car

SI HEV LTK

SI P10 Car

SI P10 LTK

CI Conv

LTK

Others

0

5

10

15

20

25

2005 2015 2025 2035 2045

Annual Sales (Million)

0

1

2

3

4

5

6

7

8

9

10

2005 2015 2025 2035 2045

Fuel Demand (MBOED)

FIGURE 4 LDV sales and fuel use by powertrain technology: base case. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 22 

SI Conv

Car

SI Conv

LTK

SI HEV Car

SI HEV LTK

SI P10 Car

SI P10 LTK

EV Car

EV LTK

Other SI 

PHEV LDV

CI Conv

LTK

Others

0

5

10

15

20

25

2005 2015 2025 2035 2045

Annual Sales (million)

0

1

2

3

4

5

6

7

8

9

10

2005 2015 2025 2035 2045

Fuel Demand (mboed)

FIGURE 5 LDV sales and fuel use by powertrain technology: FreedomCARGoals. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 23 

0%

5%

10%

15%

20%

25%

2010 2015 2020 2025

Market Share (100%)

Reference Segment

Reference Segment:

SouthAtlantic

Suburban

EarlyAdopter

FrequentDriver

Garage

NoWorkRecharge

Garage vs no parking

FrequentDriver vs 

ModestDriver

EarlyAdopter vs 

LateMajority

SouthAtlantic vs 

MiddleAtlantic

Suburban vs Urban

0%

5%

10%

15%

20%

25%

2010 2015 2020 2025

Market Share (100%)

Reference Segment

Reference Segment:

SouthAtlantic

Suburban

EarlyAdopter

FrequentDriver

Garage

NoWorkRecharge

Garage vs no parking

FrequentDriver vs 

ModestDriver

EarlyAdopter vs 

LateMajority

SouthAtlantic vs 

MiddleAtlantic

Suburban vs Urban

FIGURE 6 PHEV market shares for selected market segments: FreedomCARGoals. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.
Lin and Greene 24 

SI P10 Car

SI P10 LTK

EV Car

EV LTK

Other SI PHEV, Car &

LTK

0

20000

40000

60000

80000

100000

120000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Electricity Demand (million kWh/year)

FIGURE 7 Light-duty vehicle electricity use: FreedomCARGoals. 

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.