TRB 10--1698



### A Plug-in Hybrid Consumer Choi ce 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 mode l for projecting U.S. demand for plug-in hybrid electric vehicles (PHEV) in competiti on among 13 light-duty vehicle technologies over the period 2005-2050. New car buyers are disaggre gated by region, residential area, attitude toward technology risk, vehicle us age intensity, home parking and work recharging. The nested multinomial logit (NMNL) model of vehicle choice incorporates daily vehicle usage distributions, refueling and rech arging 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 tec hnological progress assume d. PHEV sales reach one million in 2037 in the Base Case but in 2020 in the FreedomCARGoals Case. In the FreedomCARGoals Case, PHEV cumulative sa les 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 progre ss, PHEV’s market success appears to be most sensitive to recharging availability, consumers’ attitudes toward novel technologies, and vehicle usage intensity. Success ful market penetration of PHEVs he lps bring down battery costs for electric vehicles (EVs), resulting in a significant EV mark et 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 c onsumers and the largest source of greenhouse gas (GHG) emissions in the transportation s ector, accounting for 60% of transportation petroleum use and an equivalent share of transportation’s 1.9 million metric tons of CO 2 emissions in 2008 ( 1 ). According to the U.S. DOE’s mo st recent assessment, existing policies including higher fuel economy st andards 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-w ide 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 inte grate the energy efficiency of hybr id 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 petroleu m 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 thr ough technological change and lear ning-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 potent ial to use vehicles as a source of back-up power or the value of vehicl e-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 ener gy use that will come from electricity can also depend strongly on how consumers use the vehicl es. Their impact on the grid will depend on market success, recharging behavior and usag e 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 represen ting the attributes and behavior s of consumers at a relatively detailed level. A consumer choice model re lying on the preferences of a single, typical consumer would not be able to reflect key factors such as ac cess to recharging, daily driving patterns and willingness to accep t 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. Th e 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 appropria te level of detail, whether or not the data are fully available or the behaviors are fully understood at the presen t time. As more is learned about PHEVs and consumers’ preferences towards them, the m odel will be continuously updated and improved. This paper describes a model of consumer choice and use of 13 advanced vehicle technologies applied to two vehi cles types: passenger cars and light-duty trucks. The household market is disaggregated into 1,458 segments by si x dimensions: nine regions, three types of settlements, three attitudes towards technology ri sk, 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 th e theory and structur e of the ORNL PHEV model, focusing on features that are especially impor tant 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 cas e to illustrate the beha vior of the model.

THE ORNL PHEV MODEL: FRAMEWORK AND METHODOLOGY

Model Framework The ORNL PHEV model is designed to pred ict vehicle choice probabilities (market shares) for advanced technologies dependent on c onsumer attributes, chan ges in the cost and performance of advanced technologies, energy pri ces, and policies. The core of the model is a nested NMNL module that estimates the probabili ties 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-dut y 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 technolog y. A national vehicle stock turnover (scrappage) module estimates vehicle retirements by age and cal culates 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 us ed to compute energy use and CO 2 emissions. Several recursive feedbacks l oops are included. The previous year’s sales volumes by technology affect the number of makes and m odels available for each technology, cumulative sales affect vehicle purchase pr ices 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 proj ections of vehicle sales, stocks, vehicle travel, energy efficiency and energy prices from th e 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 improvi ng the assumptions as research on PHEVs and other advanced technologies progresses. In the following sections, key module s of the model are further described.

THE NMNL MODULE AND CHOICE SET
The consumer choice module is an NMNL mode l with fixed preferences for most vehicle attributes and random utility components varying acro ss individuals and nests. In principle, each market segment could have different preferences fo r 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 factor s that affect vehicle travel, including region, residential area and daily driving di stribution. At present, preferen ces 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 in to three classes: (1) conventional/HEV, (2) hydrogen, and (3) battery -electric vehicles. Within the c onventional/HEV class, technologies are grouped into conventional internal co mbustion engines (Spark Ignition Conv and Compression Ignition Conv), hybrid el ectric 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) ar e 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 choos e technology i, given a choice among the vehicles in nest jkl, is given by a NMNL function of the weighted attribute va lues of technology i.



## 

 



h c c

jkl i hjkl jkl

ijkl jkl

e e p  

|

1 |  

## 

i jkl i p
(1)

In equation 1, c ijkl is the generalized cost, or utilit y value in present value dollars of technology i in nest jkl. The parameter β jkl determines the sensitivity of technology choices in nest jkl to their generaliz ed cost. Each technology’s generalized cost is comprised of a weighted sum of functions of the values of its attributes. Let the z th attribute’s value be represented by

x zijkl , its function be f z (x zijkl ), and its weight w zjkl . The generalized cost for choice ijkl is given by equation 2. Note that generalized cost c ijkl could vary across market segments.

(2)

At present the following attributes are in cluded in the genera lized cost function.

 vehicle retail price

 fuel and electricity cost

 battery replacement cost

 acceleration

 cargo space

 towing

 range

 home backup power

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.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 lowe r nest are “averaged” and passed up to the next level.

) ln( 1 

## 



 

i c jkl ijkl jkl

jkl e c   (3)

The choice among nests at the next level is a logit function of their generalized costs, c jkl , and price sensitivity at the next level of the nest, represented by β kl
. The unconditional choice probability for technol ogy i in nest jkl, is the product of the conditional choice probabilities.

l l k kl j jkl i ijkl 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 vehicl e sales are fixed at th e 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 as sociated 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, a nd 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 grow th continues at the average growth rate from 2015 to 2030. LDV sales within each census di vision are split into three residential areas, Urban, Suburban, and Rural, based on the census population data ( 10 ). Within each area, sales are further subdivide d by attitude toward technology risk into Early-Adopter (16%), Early-Ma jority (34%), and Late-Major ity (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 th e 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.

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.Lin and Greene 6

Within each attitude group, three levels ofvehicle 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, explaine d later in this paper. The LDV market is then further split by ho me 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 in to 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 modul e 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-sustain ing (CS) mode when the battery is depleted to a certain low level of state-of-charge ( 4 ). As a consequence, th e 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 ch aracterize both driving behavi or and rechar ge 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 nati onwide. As an alternative approach, Greene ( 16 ) assumes the gamma distribution for vehicle da ily usage and then obtains maximum likelihood estimates of the parameters based on longitudi nal refueling survey data of over 2000 vehicles. The gamma distribution is suitable for repres enting household vehicle travel due to its nonnegativity and skewness flexibil ity. Another desirable attrib ute of the gamma distribution, making it easy to estimate is that it contains on ly two parameters: shape and scale. The product of shape and scale equals the mean of the distri bution, which can be treated as the average daily vehicle usage. The difference between mean and scale is the mode of th e distribution, i.e. the most frequently occurring daily distance. It ma y 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 distribu tion. Therefore, the dist ribution can be estimated by only knowing the average daily driving distance and the co mmuting distance. A subset of the NHTS2001 data was selecte d, 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 th e primary vehicle, commuting distances, and associated region and residentia l area are extracted. 3,755 gamma distributions of daily vehicle usage are then estimated by following the prev iously described relationship between the distribution parameters and the daily usage mean and mode. For simplicity, these 3,755 distributions are cl ustered in three levels of vehicle usage intensity: Modest-Driver, Averag e-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, Averag e-Driver, and Frequent-D river by region and area are also obtained.

Vehicle Characterization The data characterizing vehicle technologies were provided by re searchers 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 im portant issues and presents some key data. An LDV is is assumed to consist of a powertr ain and a glider. Within each of the car and light-duty truck types, 13 powertr ain technologies share the identical glider. The same set of vehicle performance standards, e.g. 9 sec ond for 0-60 mph and 65 mpg at 6% grade for 20 minutes, applies to all 26 car a nd truck technologies. The Powert rain 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 amon g technologies is range . All technologies with a gasoline or diesel ta nk 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 in terpolation. 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 fo r both vehicle types. The vehicle retail price is th e sum of glider and powertrain component costs multiplied by 1.5, as shown in Equation 5. The details of each component cost function f i (S i ) can be found in ( 18 ). Such a retail price varies over time, due to component co st reduction, and thus reflects technological progress due to R&D, but does not re flect lack of production e xperience or scale. That is, the price, called fully -learned price, is based on ma ss production and sufficient learning experience.

) ) ( ( 5

. 1 

##      i i i g S f C P

(5) One common approach to represent learning e ffects is the experience curve, in which the logarithm of new product prices has a linear re lation to the logarithm of cumulative production ( 19 ). A problem with the experience curve is th at price can approach zero when the cumulative production is sufficiently large. Alternatively, th e price can be assumed to approach a horizontal asymptote ( 20 ). Price P n is an exponential function of th e cumulative production n, governed by the fully-learned price of the vehicle technology P ∞ , the fully-learned pr ice of the reference technology P ref , and two parameters a and b (Equation 6). With the asymptotic learning-bydoing function, the retail price is formed by am plifying 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 th e data in Table 1. The parameters a and b are calibrated by ensuring the equivalent varying pr ogress 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

0 ) )( 1 (  

    b P P P ae P ref ref bn

n (6)

Energy efficiency is the key factor dete rmining energy costs. PSAT provides detailed energy efficiency characterizations, including 4 fu el 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 ef ficiencies are presented in Table 2. The Base Case represents engineering potential according to the literatu re and expert estimates. In the FreedomCARGoals Case, for non-hydrogen relate d technologies, cost es timates are based on the assumption that the DOE Freedom CAR 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 base d on engineering potential, excep t reflecting the DOE goals of battery technology for fuel ce ll PHEV. The price and efficiency estimates in the FreedomCARGoals Case are significantly more op timistic 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 co mes first depending on ve hicle 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 long er 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. Batte ry cost and capacity data are shown in Table 2.

Refueling and Rechar ging 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 pub lic 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 wa nts the vehicle to be more like a true PHEV rather th an 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 n eeded 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 rechargi ng, 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 th e 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 r echarging 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 partic ular technology and today’s SI Conv. stock. The functions for gasoline and diesel are calibrated to historical data including vehicle st ock and the number of diesel and gasoline stations. The functions fo r hydrogen and electricit y 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, gr eater diversity of choice among makes and models of vehicles leads to grea ter 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 th at 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 V d can be estimated as a function of the number of makes and models offered n j , 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 availa ble and still as larg e as $500-$1000 even if 40% of all makes and models offer the par ticular technology, depending on relative price elasticity of the technology to its direct competitors ( 28 ).

       

 N n V j

e d log  

(7)

It is difficult to predict model diversity as this involves uncertain marketing strategies. However, it is reasonable to assume that the numbe r 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 asym ptotic exponential rela tionship (Equation 8) between model number and sales to the historical sales and model numbers of hybrid vehicles.

0
, 80 Sales      b a e a n 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, ther eby 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 us ed 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 vehi cle characterization data previously presented. The same energy prices are used in the two cas es. 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) gasolin e 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 re flected in both the Base and FreedomCARGoals cases. If the PHEV has a gross vehicle we ight rating less than 14,000 pounds, has a battery capacity no less than 4 kWh, and is purchased be fore the manufacture’s PHEV cumulative sales reaches 200,000 units, the buyer earns the tax cr edit 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 a nd 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 domi nate 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 perfor mance improves, first HEVs and later PHEVs gain increasing shares of the market. HEV and PH EV 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 milli on 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-dut y vehicles are consuming just over 8 million barrels per day of petroleum, nearly all of whic h 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 a nd 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 vehicl es become significant, rising to over 2 million units by 2050. In both the Base and FreedomCARGoals cas es, PHEV-10 vehicles dominate the PHEV market. In the PHEV consumer choice modul e, 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 co st, they dominate the PHEV market. While PHEV-10 sales reach 7.5 million in 2050, combin ed sales of PHEV-20s and PHEV-40s amount to less than 100,000 units. At presen t, little is known about the va lue consumers might attach to all electric range or the potenti al 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 segmen t vary greatly as a result of market disaggregation. In Figure 6, the gray shaded area represents the enve lope of market shares for all 1,458 segments. The no-marker black line shows th e PHEV10 market share within the reference segment (SouthAtlantic, Suburban, EarlyA dopter, FrequentDriver, Garage, and NoWorkRecharge) during the period 2010-2025. By ch anging one segment dimension at a time, another five segment curve representing their PHEV 10 market shares are also shown to illustrate the variation of segment choice behavior. Early-adopt er, frequent drivers, w ith 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 essent ially uninterested in a PHEV. A similar consumer having a garage but a low tolerance fo r technology risk (late majority) is initially uninteres ted but gains interest as PHEV s penetrate the market and by 2023 is in essentially the same place as his early-a dopter counterpart. Thos e 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 segmenta tion of the market matters and may be crucial to accurately modeling the earliest phases of market evolution. Light-duty vehicle fuel use in the FreedomCA RGoals Case falls from about 8.5 mmbd in 2009 to less than 5 mmbd in 2050 (Figure 5). Ye t even with HEVs, PHEVs, and EVs making up 80% of vehicles on the road in 2050, the reduc tion in petroleum use by light-duty vehicles is only about 40% versus the Base Case. Howeve r, 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 us e 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 pr otect against dangerous climate changes ( 33
, 34 ). Even in the FreedomCARGoals Case, the qua ntity of electricity required for light-duty vehicles is modest relative to total U.S. electricity suppl y. In 2050, PHEVs and EVs use 120 billion kWh of electricity (Figur e 7). This amounts to only 3% of the total electricity generated in 2008 and 2.5% of the projected generation supplie d 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 elec tricity generation in the United States.

SUMMARY AND NEXT STEPS A model that projects market sh ares of PHEVs and other advan ced vehicle technologies for a highly disaggregated U.S. market has been deve loped 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, i ndicating that the segmentation is important to adequately representing the evol ution of markets for advanced powertrain technologies. The model’s predictions are highly dependent on the degree of tec hnological 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 advan ced 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 stru cture 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 preferen ce for technology risk and daily driving distributions,

 addition of lifecycle greenhouse gas emissions es timates 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 disaggreg ation of the PHEV market and also Dr. Danilo Santini of Argonne National Laboratory who for several years ur ged 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 conceptu alization, implementation, and ca libration of the PHEV Model.

REFERENCES
1. (U.S. DOE/EIA) U.S. Department of Ener gy Energy Information Administration (2009). An Updated Annual Energy Outlook Refere nce Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Changes in th e Economic Outlook
, SR/OIAF/2009-03, available online at <http://www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html>, Washington, D.C. 2. Greene, D. L., J. German and M.A. Delu cchi (2009). "Fuel Economy: The Case for Market Failure." Reducing Climate Impacts in the Transportation Sector : 181-205 3. Public Law (2009). The American Recovery and Re investment Act of 2009 . Public Law no. 111-5. 4. Vyas, A. D., D. J. Santini, et al. (2009). “P lug-in Hybrid Electric Vehicles’ Potential for Petroleum Use Reduction: Issues Involv ed in Developing Reliable Estimates,” TRB 88th Annual Meeting Compendium of Papers DVD , Paper #09-3009. 5. Axsen, J. and K. S. Kurani (2009). “Early U.S. Market for Plug-in Hybrid Electric Vehicles: Anticipating Consumer Rechar ge Potential and Design Priorities,” TRB 88th Annual Meeting Compendium of Papers DVD , Paper #09-1272.

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.Lin and Greene 13

6. Bomberg, M., K. Kockelman, et al. (2009). “Greenhouse Gas Emission Control Options: Assessing Transportation and Electricity Generation Technologies and Policies to Stabilize Climate Change,” TRB 88th Annual Meeting Compendium of Papers DVD
, Paper #09-3557. 7. Markel, T. and A. Simpson (2006). "Cost-Bene fit Analysis of Plug-In Hybrid Electric Vehicle Technology," Journal of World Electric Vehicle Association Vol.1, 2006 8. Samaras, C., C. T. Hendrickson, et al. (2009). “Policies to Promote Plug-in Hybrid Electric Vehicles for Greenhouse Gas Emi ssions Reductions and Oil Displacement,” TRB 88th Annual Meeting Compendium of Papers DVD , Paper #09-3077. 9. Rousseau, A. (2009). Personal Communication on Vehicle Data from Powertrain System Analysis Toolkit (PSAT) Z. Lin. Knoxville, TN. 10. Census Bureau (2001). Census 2000. 11. Rogers, E. M. (1962). “Diffusion of Innovations,” Free Press of Glencoe , Macmillan Company. 12. Santini, D. and A. Vyas (2005). Suggestions for a New Vehicl e Choice Model Simulating Advanced Vehicles Introduction Decisi ons (AVID): Structure and Coefficients , Argonne National Laboratory, ANL/ESD/05-1. 13. Federal Highway Administration (2002). 2001 National Household Travel Survey

. 14. Census Bureau (2006). American Housi ng Survey for the United States in 2005. 15. Kurani, K. S., J. Axsen, et al. (2009). Learning from Consumers: Plug-In Hybrid Electric Vehicle (PHEV) Demonstration and Consumer Education , Outreach, and Market Research Program, Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-09-21. 16. Greene, D. L. (1985). "Estimating Daily Vehicle Usage Distributions and the Implications for Limited-Range Vehicles." Transportation Research Part BMethodological 19(4): 347-358. 17. Singh, M. (2008). Unpublished Vehicle Data from the Multipath Study. Personal communication via Email on Aug 21. Knoxville, TN. 18. Plotkin, S., M. Singh, et al. (2008). Multi-Path Study Phase 2: Vehicle Characterization and Key Results of Scenario Analysis (DRAFT). 19. Wene, C. (2000). Experience Curves for Energy Technology Policy . Paris, International Energy Agency, OECD. 20. Yeh, S., E. N. Rubin, et al. (2007). "Uncerta inties in Technology Experience Curves for Integrated Assessment Models," SSRN eLibrary. 21. U.S. Department of Energy (2006). The Fr eedomCAR and Vehicle Technologies MultiYear Program Plan 22. Bandivadekar, A., K. Bodek, et al. (2008). On the Road in 2035: Reducing Transportation’s Petroleum Consumption and GHG Emissions , MIT Laboratory for Energy and the Environment, Report No. LFEE 2008-05 RP, July 2008. 23. Kromer, M. A. and J. B. Heywood (2008). A Comparative Assessment of Electric Propulsion Systems in the 2030 U .S. Light-Duty Vehicle Fleet ," SAE Paper 2008-01- 0459, Int. Journal of Engines 1(1): 372-391, SA E World Congress, Tory, MI, April 14-17, 2008. 24. EUCAR/CONCAWE/JRC (2006). "Well-to-Wh eels Analysis of Future Automotive Fuels and Powertrains in the European Context."

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25. Nicholas, M. A., S. L. Handy, et al. (2004) . "Using Geographic Information Systems to Evaluate Siting and Networks of Hydrogen Stations," Energy and Environmental Concerns 2004(1880): 126-134. 26. Lin, Z. H., C. W. Chen, et al. (2008a). "The Least-cost Hydrogen for Southern California," International Journal of Hydrogen Energy 33(12): 3009-3014. 27. Lin, Z. H., J. Ogden, et al. (2008b). "The Fuel -travel-back Approach to Hydrogen Station Siting," International Journal of Hydrogen Energy 33(12): 3096-3101. 28. Greene, D. L. (2001). TAFV Alternative Fuels and Vehicles Choice Model Documentation , Oak Ridge National La b, ORNL//TM--2001//134. 29. Leiby, P. N. and J. Rubin (2000). The Alternative Fuel Transiti on: Results from the TAFV Model of Alternative Fuel Use in Light- Duty Vehicles 1996-2010, Final Report – TAFV Version 1 , ORNL/TM-2000/168, Oak Ridge Nationa l Laboratory, Oak Ridge, TN, May. 30. ORC International (2008). CARAVAN Serv ices, Surveys of 1000 Households during MAY 9-12, 2008 for National Renewable En ergy Laboratory, Golden, Colorado. 31. Greene, D. L., P. L. Leiby, et al. (2007). Integrated Analysis of Market Transformation Scenarios with HyTrans , Oak Ridge, TN, Oak Ridge Na tional Laboratory, ORNL/TM2007/094. 32. Lelby, P. N., D. L. Greene, et al. (2006). "S ystems Analysis of Hydrogen Transition with HyTrans," Energy and Environmental Concerns 2006: Including 2006 Thomas B. Deen Distinguished Lecture(1983): 129-139 33. (IPCC) Intergovernmental Panel on Climate Change (2007). Climate Change 2007: Mitigation of Climate Change , Cambridge University Press, Cambridge, United Kingdom. 34. Stern, L. (2007). The Economics of Climate Change: The Stern Review , Cambridge University Press, Cambridge, United Kingdom.

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LIST OF TABLES

1 Vehicle Retail Price and Fu el 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 power train technology: base case. 5 LDV sales and fuel use by powertr ain technology: FreedomCARGoals. 6 PHEV market shares for selected market segments: FreedomCARGoals. 7 Light-duty vehicle electric ity use: FreedomCARGoals.

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

TABLE 1 Vehicle Retail Price an d 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)

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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.

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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)

A

verage Driver X = Γ (1.90, 23.20)

Frequent Driver X = Γ (1.80, 43.05)

FIGURE 3 Daily vehicle usage by vehicle usage intensity.

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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 2045Annual Sales (Million)

0 1 2 3 4 5 6 7 8 9 10

2005 2015 2025 2035 2045Fuel Demand (MBOED)

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

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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 2045Annual Sales (million)

0 1 2 3 4 5 6 7 8 9 10

2005 2015 2025 2035 2045Fuel Demand (mboed)

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

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0% 5% 10% 15% 20% 25%

2010 2015 2020 2025Market 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 2025Market 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 sele cted market segments: FreedomCARGoals.

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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 2050Electricity Demand (million kWh/year)

FIGURE 7 Light-duty vehicle electricity use: FreedomCARGoals.

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