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Abstract and Figures

Much recent attention has been drawn to providing adequate recharge availability as a means to promote the battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) market. The possible role of improved recharge availability in developing the BEV-PHEV market and the priorities that different charging options should receive from the government require better understanding. This study reviews the charging issue and conceptualizes it into three interactions between the charge network and the travel network. With travel data from 3,755 drivers in the National Household Travel Survey, this paper estimates the distribution among U.S. consumers of (a) PHEV fuel-saving benefits by different recharge availability improvements, (b) range anxiety by different BEV ranges, and (c) willingness to pay for workplace and public charging in addition to home recharging. With the Oak Ridge National Laboratory MA3T model, the impact of three recharge improvements is quantified by the resulting increase in BEV-PHEV sales. Compared with workplace and public recharging improvements, home recharging improvement appears to have a greater impact on BEV-PHEV sales. The impact of improved recharging availability is shown to be amplified by a faster reduction in battery cost.
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TRB 11--4094
Promoting the Market for Plug-in Hybrid and Battery Electric Vehicles: The
Role of Recharge Availability
Feb 15, 2011
Word Count: 5862+6*250 = 7362 (including 6 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
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
Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-
00OR22725 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, world-wide license to publish or
reproduce the published form of this manuscript, or allow others to do so, for United States
Government purposes.
Lin and Greene 1
Much recent attention is drawn to the provision of adequate recharge availability as one
means to promote the battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV)
market. What requires better understanding is the possible role of improved recharge availability
in the development of the BEV/PHEV market and the priorities the different charging options
should receive from the government. This study reviews the charging issue and conceptualizes it
into three interactions between the charge network and the travel network. Based on travel data
of 3755 drivers from the National Household Travel Survey (NHTS), we estimate the
distribution among the U.S. consumers of 1) PHEV fuel-saving benefits by different recharge
availability improvements; 2) range anxiety by different BEV ranges; and 3) willingness-to-pay
for workplace and public charging as added to home recharging. Using the ORNL MA3T model,
the impact of three recharge improvements is quantified by the resulting increase in the
BEV/PHEV sales. Overall, compared to workplace or public recharging improvement, home
recharging improvement appears to have a greater impact on the BEV/PHEV sales. The impact
of recharge availability improvement is shown to be amplified by faster reduction in battery cost.
Lin and Greene 2
Battery electric vehicles (BEV) and plug-in hybrid vehicles (PHEV) receive tremendous
attention in recent policy discussions that aim at reducing petroleum use and greenhouse gas
emissions in the transportation sector (1,2). Public enthusiasm appears to be growing, as evident
by media coverage of the Nissan Leaf BEV and Chevrolet Volt PHEV. BEV offers the promise
of ultimate electrification of the personal travel but its massive commercialization still requires
further battery cost reduction and availability of adequate charging (3,4). PHEV integrate the
energy efficiency of hybrid powertrain with the ability to partially substitute electricity for
petroleum. PHEV is less expensive and less dependent on charging availability, and therefore
viewed as a bridging technology to BEV (1).
Although most experts agree that battery cost and performance are still the largest barrier
toward massive commercialization of BEV/PHEV (5-8), much attention is being paid to recharge
availability improvement. The growth of a BEV/PHEV market will likely be affected by how
fast and how well a charging infrastructure is deployed, but to what extent? How much of the
limited social resources should be devoted to improving the charging infrastructure? What
charging technologies at what locations should receive a higher priority of public funding
support? In responding to these policy questions, it is necessary to assess the role of recharge
availability in the transition of a BEV/PHEV market.
This study assesses the potential impact of improved recharge availability on both the
individual BEV/PHEV consumer and the BEV/PHEV market development. It should be noted
that this study focuses on the benefit side of recharge availability improvement and ignores the
cost side. In the following sections, the complicated recharge availability problem is
conceptualized into three network interactions. We then estimate the distribution among the U.S.
consumers of 1) PHEV fuel-saving benefits by different recharge availability improvements; 2)
range anxiety by different BEV ranges; and 3) willingness-to-pay for workplace and public
charging as added to home recharging. Finally, The ORNL MA3T model is used to analyze the
potential impact of recharge availability on the BEV/PHEV market.
While the charger network is viewed as a set of spatially distributed chargers, the travel
network consists of spatially and temporally distributed drivers, driving routes and parking
locations. This study proposes to conceptualize the occurrence of charging as the result of
interactions between the charger network and the travel network.
The Charger Network
The charger network can be described by availability, location type (home, workplace,
etc) and charging speed (Level 1, 2, 3) of each charger. More chargers enable more potential
BEV/PHEV buyers, but the role of an additional charger depends on its location due to the
associated parking time, parking frequency and technology constraints. Home recharging is
presumably more important because homes are where vehicles park the longest and most often.
Adopters of converted PHEVs appear used to plugging in when arriving home (9-10). While a
home charger may enable a new BEV/PHEV buyer, adding a workplace charger may make the
purchase more likely. For enthusiastic consumers without home charging, workplace charging
may be seen as an acceptable alternative, as workplace parking is usually long and routine for
most workers. Public chargers can equivalently extend the electric range. Public recharging may
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be important in alleviating the range anxiety of prospective BEV owners, and such benefits
should not be underestimated by the relative small portion of energy contributed by public
chargers. For example, BEV owners in Japan rarely use public chargers, but stated that they
would not have bought a BEV if those public chargers were not in place (11).
A charger’s range extension ability depends on the charging time and the charging speed.
Faster charging may be necessary in public places, where parking time is usually short. But for
consumers with home recharging and typical driving patterns, topping off partially depleted
battery may be more often demanded than full recharging. This could reduce the need for high
charging speed. For home or workplace recharging, the usual long parking time makes expensive
upgrade to faster charging less necessary, especially for PHEVs with a small battery.
Three levels of charging speeds are currently under consideration (12). Level 1 charging,
using a standard 110 volt, 15-20 usable ampere circuit, and is sufficient to fully charge a small
PHEV20 (PHEV with 20-mile CD range) sport-utility vehicle (SUV) during 7-9 hours of home
nighttime. Depending on residential areas, 50% -70% of households have access to Level 1
recharging (5,10). With Level 2 charging on a 220 volt, 40 ampere circuit, a full recharge
requires less than 4 hours for a PHEV40 SUV and about half hour for a PHEV10 small car.
Level 2 charging can be added to garages or parking lots at probably moderate costs, but
currently it is not commonly offered (1). Level 3 charging uses a 440 volt, three-phase circuit,
typically providing 60-150 kW of off-board charging power. Level 3 charging for PHEVs is
probably not necessary at home or even workplace where vehicle parking duration is normally
long. Safety concern and high cost also make Level 3 implementation for PHEV charging in
these places dubious, at least for the near term. However, Level 3 charging may be more
necessary for BEVs. A full recharge of a 150-mile BEV midsize car will likely require more than
10 hours with a Level 2 charger, but only 2-3 hours with a Level 3 charger. In commercial places
where drivers park and conduct personal or business activities, 1-2 hours of Level 3 charging is
sufficient to provide a full recharge for most PHEVs and can significantly extend the BEV
driving range.
Three Charger-Travel Interactions
Conceptually, three steps of interactions between the charger network and the travel
network mostly determine how, when and where the recharges take place and therefore the value
of the charger network to the BEV/PHEV owners.
The first interaction, named Attract & Change, regards how the change of one network
attracts that of the other. This includes the emergence, growth and upgrade of the charger
network in response to the increase of BEV/PHEV traffic and drivers in the travel network, and
the emergence, adaptation and growth of BEV/PHEV traffic and drivers in response to the
charger network. For example, a workplace is equipped with some chargers in its parking lot in
response to the observation that some employees drive BEVs to workplace and express need for
recharging. The built charger network may also impact the travel network. For example, people
may drive less after switching to a BEV. Do BEV owners buy a BEV because they drive less
than others or do they drive less because of owning the BEV? Will the provision of fast public
chargers encourage BEV owners to driver more? These questions are among many that concern
the correlation and causality between the charger network and the travel network.
The second interaction, named Opportunity Matching, regards the temporal and spatial
match between the two networks that determines the probability of a charger being available
where and when a recharge is possibly needed. This affects the potential for a BEV/PHEV
Lin and Greene 4
consumer to increase the share of driving on electricity. As consumer surveys show, A PHEV
consumer, who have both home and workplace recharging, has a short commute distance and
rarely conducts non-commute travel, will have the opportunity to drive on electricity most of the
time (10).
However, even presented with the recharge opportunity, a BEV/PHEV consumer may or
may not let the recharge happen. Therefore, the third interaction, named Recharge Execution,
concerns the act of recharge when the opportunity to recharge is presented. If the Opportunity
Matching interaction concerns the probability that a recharge opportunity is presented, the
Recharge Execution interaction concerns the conditional probability of a recharge being
conducted. With the battery depleted, the BEV would require a recharge for continued vehicle
operation while the PHEV would not. When a recharge opportunity is presented, a BEV/PHEV
driver would consider the recharge urgency, recharge cost and hassle based on remaining battery
capacity, knowledge about the given charger, and knowledge about the next recharge
opportunity. The UC Davis survey reveals that the early PHEV consumers are generally
enthusiastic about any available opportunity to plug in their vehicle, but also found a few
consumers refuse to recharge because the available time for recharge is too short and probably
perceived as not worth the hassle (9, 10). Overall, it can be stated that the conditional probability
of recharge is greater with more perceived benefit or less perceived cost and these perceived
costs or benefits depends on the condition of the battery, the perceived recharge hassle, the travel
plan and the perception of the next recharge opportunity.
For a PHEV user, the maximum possible fuel-saving benefit from improved recharge
availability occurs if and only if the PHEV switches from a lifetime constant charging-sustaining
(CS) operation before the improvement to a lifetime constant charge-depleting (CD) operation
after, and the user bothers to make such an ideal switch. Such a worst-to-best switch is only
theoretically possible and not necessary desirable, but the resulting fuel-saving benefit provides
the upper bound of consumer benefits from improved recharging availability and therefore worth
examining. As shown on Figure 1, this theoretical maximum fuel-saving benefit can be as much
as $7000 for the most frequent drivers and is in the range of $1000-$4000 for most drivers. This
curve is estimated based on the travel demand of a sample of 3755 drivers, who are full time
workers, mainly drive to workplace, and drive a relatively new car, from the 2001 National
Household Travel Survey (NHTS) data (13). The details of the sample have been covered by a
separate paper (5). Daily VMT variations over time and among drivers are represented in
calculating fuel cost and electricity cost, assuming $3/gge for gasoline and $0.1/kWh for
The same driver sample is used to calculate the fuel-saving benefits of home recharging
to PHEV consumers assumed with no other recharging options. As shown on Figure 1, the
provision of home recharge only will result in a narrow range of $400-$500 of lifetime fuel-
saving benefit for the majority of PHEV10 owners, as shown in Figure 1. Whether or not this
range represents the allowed extra price for PHEV10 to compete with HEV depends on
consideration of other values of PHEV, such as the fewer trips to gas stations and the hedonic
value of being able to plug in and helping the country’s oil independency.
In comparison of the charging options, some frequent drivers may value home charging
less than the combined value of the other two options, because longer commute distance and
higher daily VMT may increase utilization of charging time at work and public places. For
Lin and Greene 5
frequent drivers of PHEV10, the incremental fuel-saving benefits from workplace and public
recharging added to home recharging can reach up to nearly $1000 (Figure 1), based on the
assumption that the PHEV10 is charged at all three locations during work days and two locations
during non-work days. On the other hand, about 72% of consumers appear to value home
charging more.
Thus, the relative importance of recharge locations depends on driving intensity and
patterns. What appears to be clear is that the maximum fuel-saving benefit from better recharge
availability for PHEV is still much less than the extra premium for PHEV purchase in the near
term for the majority of consumers. This may suggest the limitation of improved recharge
availability in the event of no significant battery cost reduction. This argument can only be
strengthened by the consideration of the probably even lower actual fuel-saving benefit of PHEV,
because in reality PHEV consumers will not be given a recharge opportunity every time she
needs one (with respect to the Opportunity Matching interaction) and she will not always bother
to conduct the recharge even if she is given the opportunity (with respect to the Recharge
Execution interaction).
BEV consumers are expected to have at least one regular charging option, most likely
home recharging. For them, the value of additional recharge availability is to extend driving
range and alleviate range anxiety, which comes from the fear that an alternative transportation
means will need to be sought in days of expected long trips and even worse hassle will occur in
days of unexpected travel. A consumer is assumed to have a perceived uncertain daily driving
distance, described by the random variable x in miles per day following the probability density
function p(x) and up to M mile per day. Assume the regular charger enables a certain driving
range Rcd in miles, which is the lesser of the BEV range and the effective range that is
determined by the charging time and speed of the regular charger. The consumer is also assumed
to perceive some degree of availability of other recharging opportunities. Assume the consumer
perceive the probability (Q) of these opportunities being present when she need one, the charging
speed (E, in mile per hour charged), and the available charging time (T, in hour). The alternative
charging opportunities will result in a perceived effective range (R, as in Equation 1). The
probability (Pa) that the daily driving distance exceeding the effective range (Equation 2) or the
annual days (Na) of insufficient BEV range (Equation 3) can be used to measure the range
anxiety of the prospective consumer.
Equation 1
Equation 2
Equation 3
These three equations help link the factors that affect BEV range anxiety. Alleviation of
range anxiety means reducing the probability of daily VMT exceeding the effective range R. To
accomplish that, the BEV consumer can choose to adjust her travel behavior by reducing days of
long distance travel, i.e. adjusting her density function p(x). Without travel behavior change, the
consumer needs to increase the effective range R by: 1) having adequate charging time or speed
with the regular charger to ensure a full utilization of the battery; 2) having more non-regular
chargers to increase the possibility of available charging when needed; 3) having faster charging
Lin and Greene 6
speeds with these non-regular chargers; 4) making more time available for charge waiting; and 5)
getting a longer-range BEV. While the first three measures are related to provision of charging
infrastructure, the viability of the fourth measure depends on the consumer’s own situation, e.g.
value of time.
To quantify range anxiety, the distribution of Na among the U.S. new car drivers is
calculated. The same 3755 new car drivers from the NHTS 2001 data are used to represent the
possible BEV buyers in the U.S. Based on the 3755 distinct gamma functions that represent the
drivers’ daily VMT distributions, the days of insufficient range are calculated based on Equation
3 for each driver. These drivers are sorted based on Na associated with 100-mile BEVs and thus a
BEV range cost curve can be graphed to show the increasing difficulty to alleviate the range
anxiety of more potential BEV buyers (Figure 2). What appears interesting is the diminishing
value of a larger BEV range. Take the driver who drives 20209 miles annually, typically drives
34 miles daily (thus for the gamma distribution, mode=34, mean=55), and ranks about 70% in
the 100-mile range cost curve. The number of insufficient BEV range days is 55 days with a 100-
mile BEV and drops to 20 days with a 150-mile BEV and 7 days with a 200-mile BEV.
Not surprisingly, consumers of larger BEV will have less range anxiety. More
interestingly, between drivers, a driver who has greater range anxiety when compared based on
shorter range BEVs does not have greater range anxiety when compared based on longer range
BEVs. This reflects the importance of considering daily VMT variations over time and among
For interpretation convenience, the monetary value of Na is also provided on the
secondary vertical axis by assuming an arbitrary $15 penalty per day of insufficient range. A
possible upper bound of this daily penalty is the daily rate of a delivered rental car based on the
assumption that a BEV consumer will feel no range anxiety if such a rental car is provided for
free in days of insufficient range. However, a BEV consumer with easy access to another car in
the household may perceive little range anxiety. Without enough information to segment
consumers by household vehicle ownership and accurately estimate the daily value of range
anxiety, we assume the $15 to approximate the average, which may involve great uncertainty and
needs to be further investigated. What the resulting monetary values in Figure 2 imply is that it is
difficult for a 200-mile BEV to satisfy the majority of drivers, not to mention a 100-mile BEV,
unless driving behavior changes significantly after the BEV purchase.
For BEV owners with home recharging only, the provision of workplace and public
charging can increase R, reduce Na and therefore alleviate range anxiety. Such change of Na
measures the incremental value of the workplace and public charging to BEV consumers and
represents their willingness-to-pay. The resulting Na reductions of the 3755 drivers are sorted
based on the 100-mile BEV results (Figure 3). Not surprisingly, consumers of larger BEV will
value the additional charging less. More interestingly, between drivers, a driver who values the
additional charging availability more when compared based on shorter range BEVs does not
necessarily value it more when compared based on longer range BEVs.
The ORNL MA3T Model
To assess the role of charging improvements in the BEV/PHEV market, we conduct an
integrated analysis based on the Market Acceptance of Advanced Automotive Technologies
(MA3T) model developed by Oak Ridge National Laboratory (ORNL). We briefly describe the
MA3T model as below and refer the readers to our separate paper (5) for the model details.
Lin and Greene 7
The MA3T model at its core employs the discrete choice theory to simulate the
competition of BEV and PHEV against conventional internal-combustion-engine (ICE) vehicles
(gasoline and diesel), hybrid vehicles (gasoline and diesel), hydrogen-ICE vehicles, fuel cell
hybrid vehicles (FCV), and fuel cell PHEV (FCPHEV). PHEVs with different levels of CD
range are considered. The MA3T considers the U.S. light-duty personal vehicle market for the
time horizon of 2005-2050. Key aspects of consumer value reflected in the market projection are
vehicle price, fuel economy, energy price, distribution of daily driving distance, government
PHEV incentives, recharging availability, learning-by-doing, technology novelty, and make &
model diversity (Figure 4). The MA3T model represents a total of 1458 market segments,
covering 9 regions, 3 residential areas, 3 driver types, 3 technology adopters, 3 home recharging
situations (Level 1, Level 2, and neither), and 2 workplace recharging situations (with or without
workplace recharging).
There are several updates of the model since our last publication (5). First, the battery
replacement cost to the consumer is removed from the model. Such a modification is based on
the announced battery warranties of Leaf and Volt. Second, in representing the expiration of the
federal purchase incentives for PHEV/BEV, each PHEV or BEV technology has its independent
cumulative production for expiration and has 3 manufacturers. These two updates are expected to
result in a more optimistic projection of the PHEV/BEV market. Third, the model now allow the
users to configure the percentages of consumers with access to home and workplace recharging
and also allow specification of public recharging availability measured by the probability of a
recharging opportunity present at public places when visited.
Scenario Definition
To run MA3T, the current and future levels of recharge availability and technology cost
need to be specified. Based on various sources (5, 9, 10), it is assumed that currently 52% of
consumers have Level 1 home recharging and the rest do not have home recharging, 5% of
consumers have Level 1 workplace recharging, the rest do not have workplace recharging, and
the probability that an recharging opportunity is available at a visited public place is zero.
The deployments of chargers for homes, workplaces and public places will likely affect
each other, but are assumed to be independent in this study for the purpose of isolating the
impact of each. The recharge infrastructure deployment is assumed to be exogenous and not
affected by the BEV/PHEV sales. Such an interaction is likely, but beyond the study scope.
Also because of the study purpose on impact comparison, deployment progress is
assumed to be as fast as possible, instead of reflecting ongoing or proposed activities. Therefore,
when home recharge improvement is assumed, it means that the percentage of consumers with
Level 2 home recharge grows linearly from 0% in 2010 to 100% in 2025 and the percentage of
consumers with Level 1 home recharge keeps constant at 52% until 2018 and decrease after that
to 0% in 2025. When workplace recharge improvement is assumed, it is assumed the percentage
of consumers with workplace recharge grows from 5% in 2010 to 100% in 2025. When public
recharge improvement is assumed, it is assumed the probability of available Level 3 recharge
opportunity at the visited public place grows from 0% in 2010 to 100% in 2025. These simplified
deployment scenarios are not meant to predict the future reality, but for the purpose of analyzing
the possible impact of each recharge option by 2025.
Based on literature information (1, 12), we assume charging rates of 1.1 kW, 6kW, and
90kW for Level 1, 2 and 3 charging, respectively. For either home or workplace recharging,
consumers are assumed to perceive no waiting burden. Public charging is assumed to occur as a
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secondary purpose at the destination, e.g. charging at a restaurant. Therefore, no value of
charging time is considered in the paper.
The electricity charged is affected by charging time, charging speed and the battery state-
of-charge (SOC). Because of such complexity, it becomes necessary to assume some typical
daily routes for simplification. We assume a workday driving route is in the sequence of home,
workplace, a public place and home, and a non-workday route (weekends, holidays or vacations)
is in the sequence of home, a public place and home. The available charging time per day is
assumed to be 8 hours for home, 7 hours for workplace, and 2 hours for a public place.
The Reference battery cost projection reflects the technical potential according to expert
estimates (5, 14, 15). The Reference battery costs seem to be much higher than what people are
guessing about the Leaf battery. The Nissan Leaf is equipped with a 24 kWh battery and priced
at $32,780, which implies less than $400/kWh in battery cost. We have insufficient information
to verify the battery cost of Leaf, but we use the Battery20 case to reflect an optimistic battery
cost projection where the battery cost reduction is 20 years earlier than in the Reference case.
Therefore, there are 10 scenarios being simulated:
1) Reference: Experts projected battery cost; current recharge infrastructure unchanged till
2) ImproveHomeRechargeOnly: Fast deployment of home chargers, all homes with Level
2 charging by 2025
3) ImproveWorkRechargeOnly: Fast deployment of workplace chargers, all workplaces
with Level 3 charging by 2025
4) ImprovePublicRechargingOnly: Fast deployment of public chargers, 100% probability
by 2025 of available Level 3 charging opportunity at any visited public place
5) ImproveAllRechargeLocations: Combined ImproveHomeRechargeOnly,
ImproveWorkRechargeOnly, and ImprovePublicRechargingOnly
6) Battery20: No recharge improvement; battery cost advanced by 20 years
7) ImproveHomeRechargeOnly+ Battery20: combined ImproveHomeRechargeOnly with
8) ImproveWorkRechargeOnly + Battery20: combined ImproveWorkRechargeOnly with
9) ImprovePublicRechargingOnly + Battery20: combined
ImprovePublicRechargingOnly with Battery20
10) ImproveAllRechargeLocations + Battery20: combined ImproveAllRechargeLocations
with Battery20
Result and Discussion
Given the current policies, technology status, and charge availability, the model shows a
PHEV annual sales of nearly 2 million by 2025 (Figure 5), but projects little penetration by BEV
(Figure 6). The major barrier is the battery cost, as the cost of a near-term PHEV10 is expected
to be about $5,500 to $6,300 more than that of an equivalent non-hybrid gasoline midsize car (1),
not to mention the even higher cost of PHEV40 and BEV. It should be noted that we consider
150-mile BEVs, rather than 100-mile BEVs. Within the PHEV market, PHEV10 with its much
cheaper battery cost appears to be more cost effective for most consumers than PHEV 40, as the
fuel-saving benefit of larger battery will be restrained by limited charging availability. With
home charging only, BEVs for its limited range cannot satisfy the occasional, but probably
important, needs for long-distance travel, and therefore may be more appealing to multi-vehicle
Lin and Greene 9
households, where a conventional vehicle with no range concern is available during days of
long-distance travel.
Improved charging availability allows PHEV owners to reduce fuel cost and BEV
shoppers to perceive less range anxiety, and therefore can affect market acceptance of
BEV/PHEV (Figure 5 and Figure 6). For a particular PHEV owner, such a fuel-saving benefit
depends on the coordination among the location and charging speed of the chargers, the battery
capacity to store onboard energy, and the spatial and temporal driving patterns. Larger battery
capacity allows a long trip without operation on the CS mode. Faster charging speed along the
route enables the electric drive to continue without hours of stay at the station. With the current
recharge infrastructure, a PHEV will be operated on the CS model for much of the miles
travelled, especially for frequent drivers of small-battery PHEVs. Better charging availability
allows increase in the portion of travel distance on the CD mode and predicts more value for
prospective PHEV consumers.
However, without significant technology improvements or policy changes, increase in
charge availability alone is unlikely to result in a substantially higher BEV/PHEV penetration. In
other words, inadequate charging availability will not be the key barrier in holding back the near-
term penetration of BEV/PHEV. As discussed previously, even with a worst-to-best theoretical
switch, the fuel-saving benefit from PHEVs will be still below half of the extra price of buying a
first-generation PHEV. Because of the variation of travel route and distance and the constraint of
battery storage capacity, most of PHEV consumers will receive far less than the maximum fuel-
saving benefit even with widespread recharge infrastructure. Improved charging availability
alone does not significantly boost the BEV sales either, as the model predicts, because the BEV
battery cost still overwhelms the fuel-saving benefits and range anxiety reduction.
On the other hand, if substantial progress in reducing battery cost is concurrently
achieved, the impact of recharge infrastructure deployment on the market acceptance of
BEV/PHEV can be greater. As Figure 5 and Figure 6 show, the sales differences between
charging scenarios enlarge when battery cost decrease. If technology progress is made to reduce
battery cost or more incentives are provided for BEV/PHEV consumers, the fuel-saving benefit
perceived by PHEV buyers and the range anxiety reduction perceived by BEV buyers due to
better recharge availability will become more significant relative to the decreased price gap with
conventional vehicles. The model projects that the resulting PHEV sales from better recharge
availability in the near term can be much greater if progress in battery cost reduction is made 20
years earlier (Figure 6), and such an effect is more evident for BEVs.
Similarly, the impact of technology improvement can be strengthened by improved
recharge infrastructure deployment. The potential market base for the BEV/PHEV will be
expanded by equipping more homes with recharge capabilities and further excited by adding
chargers to workplaces and public places. A larger and higher-potential market base resulting
from better recharge availability will enable greater sales impact of cost reduction. As the model
projects, the 20-year technology advance boosts the BEV sales in 2025 by 10 thousands with
improved recharge availability, but by less than 1000 without it (Figure 6).
Among the three locations, improved home recharging is shown to have the most impact
on PHEV/BEV sales (Figure 5 and Figure 6), because home is usually where a vehicle parks the
most often and longest, resulting in more recharging energy and fuel-saving benefit. Some
experts stated that 97%-99% of charging energy will be delivered at home (16). Even with
available workplace or public charging, consumers will probably feel more convenient and less
stressful to charge at home. Surveys show that consumers state stronger preferences for home
Lin and Greene 10
recharging. Other surveys show that BEV users with home recharging rarely use public
recharging and some PHEV users refuse available recharging during weekdays because of the
hassle they perceived (9).
The impact of the availability of one recharging option is conditional on the availability
of another recharging option. For example, available workplace recharging may be more
important to consumers without home recharging. It is unclear whether enabling workplace
recharging will generate more incremental BEV/PHEV sales from those with home recharging
than from those without. In normal cases, home recharging is considered necessary for owning a
BEV. For long distance travel, the BEV owner will likely have to use another vehicle, unless fast
public recharging is available along the trip. Therefore, fast public recharging can be an
important supplement to home recharging in promoting BEV sales, especially when BEV
becomes price competitive.
Although recharge infrastructure has often been thought as a facilitating factor for
BEV/PHEV penetration, there are likely synergies between charger deployment and a growing
BEV/PHEV fleet. With increasing popularity of BEV/PHEV, businesses will more likely
provide recharging to attract consumers; apartment complex and new homes may increasingly
use recharging capabilities as a selling point. At certain market stages, deployment of chargers
can be partially driven by sales of BEV/PHEV. As observed in our market simulation, the
growth of the PHEV market can motivate the deployment of public recharging stations, leading
to the emergence of the BEV market (5). The key question is what needs to be done to push the
market quickly into such a synergetic state.
This study conceptualizes the BEV/PHEV recharge availability issue into three steps of
interactions between the charger network and the travel network. Based on varying daily VMTs,
the study then quantifies both the upper bound and the more realistic level of fuel-saving benefits
for PHEV owners from improved charge availability and quantify the BEV range anxiety of the
prospective U.S. new car owners. Using the ORNL MA3T model, the impact of improvement on
home, workplace and public recharging is quantified by the resulting increase in the BEV/PHEV
sales. Overall, home recharging is shown to have a greater impact on the BEV/PHEV sales. The
impact of recharge availability improvement is shown to be amplified by faster reduction in
battery cost. On the other hand, future technology progress will also have a larger impact if a
better recharge infrastructure is in place.
The authors gratefully acknowledge the support of the U.S. Department of Energy’s
Vehicle Technologies Program. The views expressed are the authors’ and not necessarily those
of the Department of Energy.
1. National Research Council, 2009, Transitions to Alternative Transportation Technologies
Plug-in Hybrid Electric Vehicles, The National Academies Press, Washington D.C.,
ISBN: 0-309-14851-0.
2. EPRI (Electric Power Research Institute)/NRDC (Natural Resources Defense Council).
2007. Environmental Assessment of Plug-in Hybrid Vehicles. EPRI Report# 1015325
Lin and Greene 11
3. Markel, T. and A. Simpson (2006). "Cost-Benefit Analysis of Plug-In Hybrid Electric
Vehicle Technology," Journal of World Electric Vehicle Association Vol.1, 2006
4. 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, SAE World Congress, Tory, MI, April 14-17,
5. Lin, Zhenhong and David Greene (2010). The MA3T Model: Projecting PHEV Demands
with Detailed Market Segmentation. 2010 TRB Annual Meeting CD-Room.
6. Vyas, A. D., D. J. Santini, et al. (2009). “Plug-in Hybrid Electric Vehicles’ Potential for
Petroleum Use Reduction: Issues Involved in Developing Reliable Estimates,” TRB 88th
Annual Meeting Compendium of Papers DVD, Paper #09-3009.
7. Elgowainy, A., A. Burnham, M. Wang, J. Molburg, and A. Rousseau. 2009. Well-to-
wheels Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Electric
Vehicles. Argonne National Laboratory report ANL/ESD/09-2.
8. Thomas H. Bradleya, Andrew A. Frank. 2009. Design, demonstrations and sustainability
impact assessments for plug-in hybrid electric vehicles. Renewable and Sustainable
Energy Reviews 13 (2009) 115128
9. Kurani, Kenneth S., John Axsen, Nicolette Caperello, Jamie Davies, Tai Stillwater (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-
10. Axsen, J. and K. S. Kurani (2009). “Early U.S. Market for Plug-in Hybrid Electric
Vehicles: Anticipating Consumer Recharge Potential and Design Priorities,” TRB 88th
Annual Meeting Compendium of Papers DVD, Paper #09-1272.
11. Scott, Richard. "Electric car subsidy spared cuts by government." BBC News. July 27,
2010. (accessed July 31, 2010).
12. Kevin Morrow, Donald Karner, James Francfort. Plug-in Hybrid Electric Vehicle
Charging Infrastructure Review. Idaho National Laboratory report INL/EXT-08-15058.
November 2008.
13. Federal Highway Administration (2002). 2001 National Household Travel Survey.
14. Plotkin, S., M. Singh, et al. (2009). Multi-Path Study: Vehicle Characterization and Key
Results of Scenario Analysis. Argonne National Laboratory report ANL/ESD/09-5.
15. Rousseau, A. (2009). Personal Communication on Vehicle Data from Powertrain System
Analysis Toolkit (PSAT) Z. Lin. Knoxville, TN.
16. Woodard, Tracy, representing Nissan Motor Co., Ltd. (2010). Department of Energy
Plug-In Vehicle and Infrastructure Workshop, Washington, DC. July 22, 2010.
battery electric vehicles; vehicles powered by a battery only
charge depleting; one mode of PHEV powertrain operation
charge-sustaining; one mode of PHEV powertrain operation
charging speed
fuel cell PHEV
fuel cell hybrid vehicles
internal combustion engine
Lin and Greene 12
maximum daily VMT, the upper bound of x
the Market Acceptance of Advanced Automotive Technologies model
developed by ORNL
annual days of insufficient BEV range
National Household Travel Survey
Oak Ridge National Laboratory
probability density function of x
plug-in hybrid electric vehicle
a PHEV with 10/20/40 miles of CD range, respectively
probability of being able to access a charger when in need of one
perceived effective range of BEV
the PHEV CD range or the BEV range in miles
battery state of charge
sport-utility vehicle
available charging time
vehicle miles travelled
random variable, representing daily VMT of a driver
Lin and Greene 13
List of Figures
Figure 1 Fuel-saving Benefit of Recharge Availability for PHEV10
Figure 2 BEV Range Anxiety Supply Curve
Figure 3 Demand Curve for Range Anxiety Alleviation
Figure 4 Illustrative Diagram for the ORNL MA3T Model
Figure 5 Impact of Recharge Availability on PHEV Penetration, Conditional on Battery Cost
Figure 6 Impact of Recharge Availability on BEV Penetration, Conditional on Battery Cost
Lin and Greene 14
Figure 1 Fuel-saving Benefit of Recharge Availability for PHEV10
Lin and Greene 15
Figure 2 BEV Range Anxiety Supply Curve
Lin and Greene 16
Figure 3 Demand Curve for Range Anxiety Alleviation
Lin and Greene 17
Range & Fuel Availability
Sales Policy
Vehicle Usage
and Scrappage
Fuel and
Electricity Use
Tailpipe and
Lifecycle GHG
ORC Survey
PHEV Survey
Purchase Discount, Tax
Credit, Free Parking,
Free HOV, etc
Figure 4 Illustrative Diagram for the ORNL MA3T Model
Lin and Greene 18
Projected PHEV Sales in millions
by scenario of recharge deployment
Note: 1. Reference assumes existing policites, recharge availability and moderate technology progress. 2.
Battery20 stands for 20 years earlier reduction of battery cost. 3. Deployment of each recharge option is
assumed to be aggressive during 2017-2025. 4. Projections generated by ORNL MA3Tmodel. 5. the temporary
kinks are due to expiration of the ARRA PHEV subsidies.
Figure 5 Impact of Recharge Availability on PHEV Penetration, Conditional on Battery Cost
Lin and Greene 19
Projected BEV Sales in thousands
by scenario of recharge deployment
Note: 1. Reference assumes existing policites, recharge availability and moderate technology progress. 2.
Battery20 stands for 20 years earlier reduction of battery cost. 3. Deployment of each recharge option is
assumed to be aggressive during 2017-2025. 4. Projections generated by ORNL MA3Tmodel. 5. the temporary
kinks are due to expiration of the ARRA PHEV subsidies.
Figure 6 Impact of Recharge Availability on BEV Penetration, Conditional on Battery Cost
... Risks for the grid may be further amplified by direct current fast charging (DCFC), which multiplies the grid's peak load during peak charging times [22][23][24][25][26]. Nevertheless, DCFC has become more prevalent in recent years due to its faster charging times [27,28], which is particularly convenient for long-distance travel [10,29]. A detailed review of charging systems and plug types can be found in [26,30]. ...
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For a user-centered deployment of electric vehicle supply equipment (EVSE) infrastructure, it is vital to understand electric vehicle user charging behavior. This study identifies user behavioral patterns by analyzing data from more than 7000 charging stations in Canada, comparing residential vs. public Level 2, and public direct current fast (DCFC) vs. public Level 2 charging. A novel algorithm, CHAODA, was applied to identify differences between DCFC and other Level 2 charging options. Through a multivariate and holistic methodology, various patterns emerge, identifying differences in the utilization and seasonality of different EVSE types. The study provides evidence of an “EV Duck Curve” that amplifies the baseline of the power production “Duck Curve,” confirming future challenges for grid stability. Implementations of this study can support future EVSE infrastructure planning efforts and help improve the overall service of electric vehicle supply equipment and grid stability.
... It is more practical to utilize dwelling time at origins and destinations to fully refuel the battery. These facts have been observed by many empirical and analytical studies such as Lin and Greene (2011); Xu et al. (2017); Chen et al. (2017); Meng et al. (2019); Quirós-Tortós et al. (2018); Quirós-Tortós et al. (2015); Wang et al. (2021); Yuan et al. (2019). Although it is more realistic, to the best of our knowledge, so far, no studies have considered drivers' partial charging behavior in the deployment of charging stations. ...
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In this study, we aim to optimally locate multiple types of charging stations, e.g., fast-charging stations and slow-charging stations, for maximizing the covered flows under a limited budget while taking drivers’ partial charging behavior and nonlinear demand elasticity into account. This problem is first formulated as a mixed-integer nonlinear programming model. Instead of generating paths and charging patterns, we develop a compact formulation to model the partial charging logic. The proposed model is then approximated and reformulated by a mixed-integer linear programming model by piecewise linear approximation. To improve the computational efficiency, we employ a refined formulation using an efficient Gray code method, which reduces the number of constraints and binary auxiliary variables in the formulation of the piecewise linear approximate function effectively. The ε-optimal solution to the proposed problem can be therefore obtained by state-of-the-art MIP solvers. Finally, a case study based on the highway network of Zhejiang Province of China is conducted to assess the model performance and analyze the impact of the budget on flow coverage and optimal station selection.
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Technological developments in charging speed and battery capacity are leading to an increased use of electric vehicles (EV) for long trips, but the charging infrastructure network is too scarce to satisfy the growing energy needs. Few public charger stations are available outside urban areas, triggering long queues waiting for a vacant charger. A better understanding of charging behaviour on long trips is needed to optimise the provision and distribution of charging facilities. This research contributes to existing literature by estimating the willingness to pay for reducing waiting time for charging, as well as understanding the role of explanatory variables in influencing decisions about charging at public charging stations on long trips. Responses from a stated preference (SP) survey in Norway in 2021 were analysed with a mixed logit model. Results showed that price, waiting time, the type of charger, and facilities were significant variables for station characteristics, while for trip features, the distance to destination and remaining range, also play a significant role. There is extensive heterogeneity in preferences across travellers.
With an increasing interest for electric vehicles (EV) and the associated charging needs, understanding how public charging infrastructure networks impact the adoption of such vehicles is central for planners and policymakers. While most studies are conducted at the regional or national scales, urban contexts present specificities for the deployment of these networks, given the scarcity of private parking spaces and the strong competition for land. Using data from a survey (n = 642) conducted in Montreal, Canada, this work investigates the influence of public charging infrastructure accessibility on EV adoption in an urban context. Three measures of accessibility are analyzed: 1) objective (number of charging stations near home), 2) perceived (perceptions about the current public charging network), and 3) prospective (expectations on the public charging network in five years). The findings reveal the importance of perceived and prospective accessibility measures as opposed to objective ones, and thus demonstrate that the deployment of public charging networks is an issue tied by perceptions. We also find that non-EV owners underestimate their accessibility to public chargers, thereby illustrating how perceptions may vary across individuals. The results suggest that, in addition to expanding the public charging network, efforts aiming at better informing citizens on the presence and the locations of these charging facilities could contribute to increasing EV adoption. This study highlights the importance of perceptions and expectations and provides a nuanced understanding of how integrating accessibility-based policies into electrification plans might speed up EV adoption in metropolitan areas.
Transport needs are becoming more dynamic in nature. The change in nature and buying power of individuals and businesses has augmented the bar in the usage of various means of transportation. Despite, the quick rise in manufacturing of multiple alternative fuel vehicles (AFV) handful of review studies are studied in the said area of AFV. This study has tried to combine the studies from 1998 to 2021. An integrative method of review along with the TCCM (Theory, Context, Characteristics, and Methods) model was cast to undertake the study. The current study identified the various models of alternative fuel vehicles discussed in the initial part of the study. Within TCCM; Theory discussed various models that were apportioned in earlier studies; Context depicted sites of studies; Characteristics discussed various themes such as Eco-friendliness, Economical aspects, Health Impact, National security, and Battery Storage; whereas, Method discussed the sources of data, method of selection of earlier studies and its descriptive statistics. The study lastly concluded holistically and throws light on constraints for the further advancement of the AFV market globally, especially in developing countries.
Technical Report
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The primary purpose of this report is help local units of government develop a plan to support the use of plug-in electric vehicles (EV), and develop policies and strategies that support investment into public charging infrastructure. Michigan Department of Environment, Great Lakes, and Energy (EGLE) has funded the development of a comprehensive approach, including analytical models considering applied constraints, to find the optimum investment scenario for each urban area and has supported it through a series of stakeholders’ meetings. Researchers at Michigan State University led this effort by developing and executing the modeling framework. This study builds on a previous study conducted by the same research team at Michigan State University supported by EGLE (former MI Energy office) which located DC fast chargers across the state of Michigan supporting long-distance (highway) trips of EVs in 2030. During the highway study it became evident that there is a need for a framework to optimaly locate charging infrastructure in urban areas. This report presents the study approach and results of the optimization model for locating DC fast chargers in different urban areas in Michigan for the urban trips of EV users in the state by the year 2030. Note that level 2 chargers are not the focus of this study, however, the impact of these chargers, located at shopping centers or work places, is considered in the state of charge estimator function, as an input to the optimization framework. The results for major urban areas in Michigan are presented in more detail, while the results for smaller urban areas are presented in a more aggregate manner, depending on the availability of data for these urban areas. Through a series of stakeholder meetings, different scenarios with different battery and charger technologies were suggested and investigated for this study. The suggested battery energy levels are 70 kWh and 100 kWh, and power levels of 50 kW and 150 kW are considered for chargers. Also, the winter scenario is selected for this study, as the number of urban trips is known to remain relatively constant seasonally, while the reduced battery performance during the cold seasons requires more chargers and charging stations. Table 1 shows a summary of the findings for different urban areas sorted by their travel demand. The details of the scenarios and requirements are available in the report.
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Electric Technology Vehicles (ETVs: hybrid, electric, and plug-in hybrid) may reach price parity with incumbent internal combustion vehicles (ICEVs) in the near future. Climate policy for transportation will depend on the degree to which consumers prefer ETVs, and price parity is a key factor. In this study, we explore the interaction between future cost reductions and the economically motivated adoption of ETVs. We construct a model of the U.S. personal vehicle market accounting for heterogenous use and vehicle preferences, in which adoption induces cost reductions that increase future market share. Model results indicate that price parity is reached for most consumers in a number of cost scenarios, but not with constant ICEV costs and modest ETV cost declines. A price parity future suggests that government support could be temporary and phased out after a successful market transition. However, if ETVs continue to be more expensive than ICEVs, then lasting government support is needed. Heterogeneity is essential to understanding the market transition: treating consumers as heterogeneous results in an ETV market share 23% higher than assuming average consumers. Future work can clarify ETV support policy by resolving uncertainty in cost trajectories and modeling dynamic and heterogenous consumer markets.
This study focuses on the assessment of the factors affecting the adaptation of Hybrid/Electric Vehicles. The problem that arises when using conventional vehicles has some negative impacts on the environment (Greenhouse Gas emission), society (health issues), and economics (energy demand). There is a need to mitigate these effects by inducing a transportation mode with a fuel source of electricity and progressively reducing the use of gasoline. To find the socioeconomic and environmental impacts of the application of Hybrid/Electric Vehicles, the current research aims to explore potential factors that can be attributed to purchasing H/EVs to estimate their penetration in the U.S. Several multiple linear regression (MLR) models were applied to find the significant factors that impact the use of several types of Hybrid/Electric vehicles compared to conventional ones. The types of Hybrid/Electric Vehicles assessed are Plug-in Hybrid Vehicles (PHEV), Electric Vehicles (EV), Hybrid Vehicles (HEV). The models use data from the National Household Travel Survey website. R Studio software is applied to conduct statistical analysis. The results identify that the variables that have statistical significance are Fuel Expenditures and Household Income. The factors that impact the use of conventional vehicles compared to hybrid ones are MSA, Model Toyota Vehicles, Vehicles Driven in the Weekdays, Weekends, Zero Workers and One to Three Workers per Household. Furthermore, people with PHEVs, EVs, and HEVs tend to have more fuel expenditures and higher household income than conventional vehicles. Therefore, it is determined that “adults with and without children” are not significant among the models.
Supporting the adoption of zero-emission vehicle (ZEVs), including plug-in electric vehicles (EVs), has become a priority for governments due to their ability to reduce petroleum demand, improve air quality, and reduce carbon dioxide (CO2) emissions. Optimal strategies to accelerate EV adoption must weigh the relative value of alternative policy mechanisms to consumers, including public charging infrastructure and vehicle purchase subsidies. We use a historically validated light-duty vehicle consumer choice tool, the ADOPT model, to simulate personal light-duty vehicle adoption and related emissions in California. ADOPT is updated to incorporate a quantification of the tangible value of public charging infrastructure, allowing us to simulate the impact of investments in public charging infrastructure and vehicle purchase subsidies under different scenarios. We show that both policies result in increased EV adoption, with the most effective policy varying depending on vehicle technology assumptions. Under conservative technology improvement assumptions, infrastructure investments are most effective in promoting EV sales and reducing CO2 emissions, while under optimistic technology improvement assumptions a combination of infrastructure and subsidies best supports EV sales and CO2 emission reductions.
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Projecting the future role of advanced drivetrains and fuels in the light vehicle market is inherently difficult, given the uncertainty (and likely volatility) of future oil prices, inadequate understanding of likely consumer response to new technologies, the relative infancy of several important new technologies with inevitable future changes in their performance and costs, and the importance - and uncertainty - of future government marketplace interventions (e.g., new regulatory standards or vehicle purchase incentives). This Multi-Path Transportation Futures (MP) Study has attempted to improve our understanding of this future role by examining several scenarios of vehicle costs, fuel prices, government subsidies, and other key factors. These are projections, not forecasts, in that they try to answer a series of 'what if' questions without assigning probabilities to most of the basic assumptions.
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Plug-in hybrid electric vehicles (PHEVs) have emerged as a promising technology that uses electricity to displace petroleum consumption in the vehicle fleet. 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. The analysis finds that petroleum reductions exceeding 45% per vehicle 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. 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.
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This paper delineates the various issues involved in developing reliable estimates of the petroleum use reduction that would result from the wide-spread introduction of plug-in hybrid electric vehicles (PHEVs). Travel day data from the 2001 National Household Travel Survey (NHTS) were analyzed to identify the share of vehicle miles of travel (VMT) that could be transferred to grid electricity. Various PHEV charge-depleting (CD) ranges were evaluated, and 100% CD mode and potential blended modes were analyzed. The NHTS data were also examined to evaluate the potential for PHEV battery charging multiple times a day. Data from the 2005 American Housing Survey (AHS) were analyzed to evaluate the availability of garages and carports for at-home charging of the PHEV battery. The AHS data were also reviewed by census region and household location within or outside metropolitan statistical areas. To illustrate the lag times involved, the historical new vehicle market share increases for the diesel power train in France (a highly successful case) and the emerging hybrid electric vehicles in the United States were examined. A new vehicle technology substitution model is applied to illustrate a historically plausible successful new PHEV market share expansion. The trends in U.S. light-duty vehicle sales and light-duty vehicle stock were evaluated to estimate the time required for hypothetical successful new PHEVs to achieve the ultimately attainable share of the existing vehicle stock. Only when such steps have been accomplished will the full oil savings potential for the nation be achieved.
This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum. However, continued reliance on fossil-fuels without effective carbon capture and sequestration for producing electricity and hydrogen constrain the GHG emission and energy reductions to about 60% below that of present-day spark-ignition technology.
The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model incorporated fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). Based on PSAT simulations of the blended charge depleting (CD) operation, grid electricity accounted for a share of the vehicle's total energy use ranging from 6% for PHEV 10 to 24% for PHEV 40 based on CD vehicle mile traveled (VMT) shares of 23% and 63%, respectively. Besides fuel economy of PHEVs and type of on-board fuel, the type of electricity generation mix impacted the WTW results of PHEVs, especially GHG emissions.For an all-electric range (AER) between 10 to 40 miles, PHEVs employing petroleum fuels (gasoline and diesel), a blend of 85% ethanol and 15% gasoline (E85), and hydrogen were shown to offer 40-60%, 70-90%, and over 90% reduction in petroleum energy use, and 30-60%, 40-80%, and 10-100% reduction in GHG emissions, respectively, relative to an internal combustion engine vehicle (ICEV) using gasoline. In addition, PHEVs offered reductions in petroleum energy use as compared to regular hybrid electric vehicles (HEVs). More petroleum energy savings were realized as the AER increased, except when the marginal grid mix was dominated by oil-fired power generation. Similarly, more GHG emissions reductions were realized at higher AER, except when the marginal grid mix was dominated by oil or coal. Electricity from renewable sources realized the largest reductions in petroleum energy use and GHG emissions for all PHEVs as AER increased. GHG emissions benefits may not be realized for PHEVs employing biomass-based fuels, e.g., biomass-E85 and -hydrogen, over regular HEVs if the marginal mix is dominated by fossil sources.
Plug-in hybrid electric vehicles (PHEVs) are proposed as both a near-term technology to achieve energy and environmental goals and as a transitional step toward viable all-electric vehicles. To replace assumptions with observations of potential PHEV drivers' behavior in market and impact analyses, an Internet-based survey of 2,373 new-car-buying households in the United States was conducted. The instrument required households to answer questions, complete a driving and parking diary, and then complete several PHEV design exercises. Three conclusions could be drawn from the resulting data. First, at least half of the target population is already equipped for at-home vehicle recharging but has little opportunity for recharging at the workplace or other locations. Second, the study found that the respondents had widely varied interests in four possible PHEV attributes: fuel economy (in both charge-depleting and charge-sustaining operations), blended versus all-electric operation, the distance over which the vehicle is in the charge-depleting mode, and the recharging speed. Nevertheless, the appeal of increased fuel economy appears to be the highest and that of faster recharging appears to be the lowest. Furthermore, there is little interest in all-electric operation. Third, given the previous two points, it was estimated that about a third of the target population has both the infrastructure to recharge a PHEV and interest in a vehicle with plug-in capabilities. Policy, technology, and energy providers may use this information to understand whether their plans, designs, and goals align with these present understandings or whether it would be collectively beneficial to foster new understandings of PHEVs among U.S. car buyers.
Plug-in hybrid electric vehicles (PHEVs) are hybrid electric vehicles that can draw and store energy from an electric grid to supply propulsive energy for the vehicle. This simple functional change to the conventional hybrid electric vehicle allows a plug-in hybrid to displace petroleum energy with multi-source electrical energy. This has important and generally beneficial impacts on transportation energy sector petroleum consumption, criteria emissions output, and carbon dioxide emissions, as well as on the performance and makeup of the electrical grid. PHEVs are seen as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This review presents the basic design considerations for PHEVs including vehicle architecture, energy management systems, drivetrain component function, energy storage tradeoffs and grid connections. The general design characteristics of PHEVs are derived from a summary of recent PHEV design studies and vehicle demonstrations. The sustainability impact of PHEVs is assessed from a review of recent studies and current research and development needs for PHEVs are proposed.
Will people recharge a vehicle that does not have to be recharged? This, and the degree to which plug-in hybrid electric vehicle (PHEV) designs emphasize gasoline or electricity, are central to assessing the energy and environmental effects of PHEVs. Plug-in conversions of hybrid vehicles are being made available to (predominately new-car buying) households throughout the Sacramento region for four to six weeks each. The vehicles are instrumented to report travel and energy; households are interviewed and surveyed. Results from the first 34 households—all selected in part because they can recharge a vehicle at home—indicate that on average they will recharge a PHEV about once per day, but with wide variation across households. The PHEV designs created by these households emphasize increased fuel economy rather than all-electric operation—as did the designs of prior representative samples of new-car buyers (who had not driven PHEVs). This result may be due in part to 1) “anchoring†(respondents are driving a PHEV that does not practically allow all-electric operation), and 2) households not creating integrated assessments of gasoline and electricity use/cost from the in-vehicle and internet-based instrumentation. Over the PHEV trials, narratives are co-authored about the PHEVs and their place in the ongoing life-stories of the participants. The primary themes to emerge are changing driving behavior, recharging habits and etiquette, confusion about PHEVs and how they work, and the role of payback analyses and more intuitive assessments of whether PHEVs are “worth it.†Tracing social interactions by the participants about the PHEVs reveals that complex translation of ideas and information about PHEVs is occurring as the PHEV drivers, in particular, use their trial period to reflexively explore lifestyle and identity possibilities of these new vehicles.
The MA3T Model: Projecting PHEV Demands with Detailed Market Segmentation
  • Zhenhong Lin
  • David Greene
Lin, Zhenhong and David Greene (2010). The MA3T Model: Projecting PHEV Demands with Detailed Market Segmentation. 2010 TRB Annual Meeting CD-Room.
Plug-in Hybrid Electric Vehicles' Potential for Petroleum Use Reduction: Issues Involved in Developing Reliable Estimates
  • A D Vyas
  • D J Santini
Vyas, A. D., D. J. Santini, et al. (2009). "Plug-in Hybrid Electric Vehicles' Potential for Petroleum Use Reduction: Issues Involved in Developing Reliable Estimates," TRB 88th Annual Meeting Compendium of Papers DVD, Paper #09-3009.