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Energy and the Environment
Economic and Climate Benefits of Electric Vehicles in China, U.S., and Germany Xiaoyi He, Shaojun Zhang, Ye Wu, Timothy J. Wallington, Xi Lu, Michael A Tamor, Michael B McElroy, K. Max Zhang, Chris P. Nielsen, and Jiming Hao Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00531 • Publication Date (Web): 15 Aug 2019 Downloaded from pubs.acs.org on August 15, 2019
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Environmental Science & Technology
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Economic and Climate Benefits of Electric Vehicles
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in China, U.S., and Germany
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Xiaoyi He 1†, Shaojun Zhang 1, 2†, Ye Wu 1, 3*, Timothy J. Wallington 4, Xi Lu 1, 3, Michael
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A. Tamor 4, Michael B. McElroy 5, 6, K. Max Zhang 2, Chris P. Nielsen 5, Jiming Hao 1, 3
5
1
School of Environment, State Key Joint Laboratory of Environment Simulation and
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Pollution Control, Tsinghua University, Beijing 100084, P. R. China
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2
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca,
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New York 14853, USA 3
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution
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Complex, Beijing 100084, P. R. China 4
Research and Advanced Engineering, Ford Motor Company, 2101 Village Road,
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Dearborn, MI 48121, USA 5 John
A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
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6 Department
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of Earth and Planetary Sciences, Harvard University, Cambridge, MA, 02138, USA
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† These
authors contributed equally to this study.
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* Corresponding author.
[email protected] (YW)
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ABSTRACT
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Mass adoption of electric vehicles (EVs) is widely viewed as essential to address climate
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change and requires a compelling case for ownership worldwide. While the manufacturing
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costs and technical capabilities of EVs are similar across regions, customer needs and
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economic contexts vary widely. Assessments of the all-electric-range (AER) required to
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cover day-to-day driving demand, and the climate and economic benefits of EVs, need to
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account for differences in regional characteristics and individual travel patterns. To meet
26
this need travel profiles for 1681 light-duty passenger vehicles in China, the U.S., and
27
Germany were used to make the first consistent multi-regional comparison of customer
28
and greenhouse gas (GHG) emission benefits of EVs. We show that despite differences
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in fuel prices, driving patterns, and subsidies, the economic benefits/challenges of EVs
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are generally similar across regions. Individuals who are economically most likely to
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adopt EVs have GHG benefits that are substantially greater than for average drivers.
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Such “priority” EV customers have large (32%-63%) reductions in cradle-to-grave GHG
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emissions. It is shown that low battery costs (below approximately $100/kWh) and a
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portfolio of EV offerings are required for mass adoption of electric vehicles.
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INTRODUCTION
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Electrification of personal transportation using decarbonized electricity is widely viewed
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as essential in meeting the goals outlined in the Paris Agreement1-6. The scenario in which
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global warming is limited to 2℃ (2 Degree Scenario, 2DS) proposed by the International
40
Energy Agency (IEA) includes total cumulative sales of 140 million battery electric
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vehicles (BEVs) and plug-in hybrid-electric vehicles (PHEVs) by 20301. In the present
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work, we refer to plug-in electric vehicles (BEVs and PHEVs) as electric vehicles (EVs).
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While the unique driving characteristics and the social value of EVs are attractive to many
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customers, mass adoption requires a combination of customer-focused technology and
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policy incentives that make EV ownership compelling in all regions.
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A number of studies have examined the role of key factors impacting customer
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acceptance of EVs, including ownership cost, charging infrastructure, household income,
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social norms, customer perceptions, and travel patterns7-13. These studies typically focus
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on one location, usually a single urban area and assume that every driver follows a
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common aggregated travel pattern usually derived from travel surveys8, 10-13. The results
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of such studies can be highly misleading because both the fuel savings (that tend to make
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BEV attractive) and the inconvenience factors (that tend to make BEV unacceptable)
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accrue to the vehicles that are driven the most. Recent studies have used representations
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of individual travel patterns to quantify cost savings and EV preferences in single regions
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where suitable usage data are available.14-16 However, differences in assumptions and
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methodology make it difficult to provide a global comparative analysis across multiple
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regions.
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Climate benefits are influenced strongly by electricity carbon intensity and electrification
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potential of EVs17. Electricity carbon intensity differs widely with different regional grid mix
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and charging strategies17-23. Marginal emission factors that account for changes in
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generation mix in response to demand changes have been considered in some studies17-
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20,
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Other studies15,
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determining marginal carbon emissions attributable to EV charging. GHG mitigation
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potential of EVs is also dependent on driving patterns, and the impact of driver
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heterogeneity has been investigated for simulated driving conditions.11 However, the
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relationship between EV climate benefits and heterogeneous travel patterns in real-world
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driving has not been fully elucidated using a consistent methodology in multiple regions.
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Assessment of the potential for EV adoption and climate benefits relies on a
70
representation of vehicle usage. Many U.S. studies use the National Household Travel
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Survey (NHTS) datasets24 consisting mainly of single day travel profiles. The NHTS
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captures some individual heterogeneity, but does not account for day-to-day variations in
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travel demand. The NHTS report provides range distributions for commuting trips, but
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these represent only 19% of U.S. travel, compared to 27% for social or recreational
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activities, and 26% for errands and shopping (NHTS201725). Conclusions from studies
but different assumptions and system boundaries make it hard to compare results. 21, 22
use the regional average grid mix to avoid uncertainties in
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often suggest that large fraction of trips/distance could be covered by EVs of moderate
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range. However, it has been shown that investigations using short periods (less than one
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week) of travel data do not provide a reliable estimate of the frequency of long-distance
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travel days of individual users26. Customers select vehicles that meet all their needs, not
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just their typical daily needs, or those of an average driver in the same region14.
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Occasional days of long distance travel that exceed the BEV range impose
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inconvenience, extra cost, and may lead potential adopters to reject a BEV outright.
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Therefore, reliable estimates of BEV acceptance and usage require studies that capture
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the use of individual vehicles for periods of weeks or months.
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There have been no studies using large-scale multiday driving profiles that evaluate EV
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usage, cost, and life-cycle GHG emissions for individual vehicles in multiple geographic
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regions. This study fills this research gap, and is novel in four aspects. First, individual
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travel patterns are characterized based on longitudinal surveys for a large number (1681)
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of light-duty passenger vehicles in different regions: China (Beijing), the U.S. (Atlanta,
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Minneapolis, and Seattle), and Germany. Second, customer benefits from EV adoption
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are evaluated at the individual-level in terms of comparative total cost of ownership (TCO)
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between EVs and internal combustion engine vehicles (ICEVs), using local energy prices,
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alternative transportation availability, and EV incentives. Third, a common cross-region
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alignment of customer and climate benefits is documented with individuals whose driving
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patterns are most economically suited to EVs generally having the greatest GHG
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mitigation. Fourth, this study shows that the economic and emission benefits of EVs are
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generally similar across regions and that mass electrification requires a portfolio of EVs
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to suit a variety of customer needs.
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MATERIALS AND METHODS
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Vehicle trajectory data. Multiday disaggregated travel profiles for 1681 personal
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vehicles were taken from The Beijing Private Drivers’ Trip Chain Study27 (373 vehicles),
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the Commute Atlanta Value Pricing Program28 (651 vehicles), the Minnesota Mileage-
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Based User Fee Demonstration Project29, 30 (133 vehicles), the Seattle Regional Council
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Traffic Choices Study31 (446 vehicles), and the Europe Field Operations Test (Euro-
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FOT)32 project (78 vehicles). Data were collected for each vehicle for at least a month.
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The study participants in China and the U.S. were recruited based on demographically
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representative selections according to geographic distribution and further stratified
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demographical considerations. The Euro-FOT project was conducted primarily for
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automobile safety and energy efficiency analysis, with participants recruited from
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customers of participating dealerships. Further details of the studies including
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comparisons with aggregated city, state, and national level statistics are provided in
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section 1.1 of the supporting information (SI).
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Trip chain modeling.
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A statistical model was applied to describe individual trip chain distributions (ITCD),
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reproduce a full year’s driving profile for every vehicle, and determine the number of
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inconvenience days which are defined as days when battery electric vehicle range is less
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than the daily travel. In the analysis of data from instrumented vehicles, a ‘trip’ is defined
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as the travel between turning the vehicle on (‘key on’) and turning it off (‘key off’). A trip
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chain is the total distance driven in a series of consecutive trips completed in a given time
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period. For this study, a daily trip chain is travel in the 24-hour period beginning at 4:00
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am. Our base case assumption is that charging only occurs overnight, either at home or
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at a nearby public charging station, and the vehicle begins each day fully charged. The
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distribution of daily travel (total trip chain distance) for each vehicle was described using
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equation (1) 27, 29
𝑓𝑖(𝑥) = 𝜆𝑖 ×
)
(
𝑤𝑖 ―𝑥/𝑘 1 ― (𝑥 ― 𝜇𝑖)2/2𝜎𝑖2 𝑖 𝑒 + (1 ― 𝑤𝑖) 𝑒 𝑘𝑖 2𝜋𝜎𝑖2
(1)
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𝑓𝑖(𝑥) is the probability that an individual car, i, has a trip chain distance of 𝑥 km. The
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exponential part of the distribution represents a ‘random’ travel pattern with frequent short
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trips and occasional long trips, where 𝑘𝑖 is the characteristic distance. The Gaussian part
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of the distribution depicts a ‘habitual’ travel pattern, where 𝜇𝑖 is a ‘habitual’ distance related
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to repeated commuting trips or other regular trips. The parameter 𝑤𝑖 is a weighting factor
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of the ‘random’ pattern; 1 ― 𝑤𝑖 is the weighting of the ‘habitual’ pattern. The parameter 𝜆𝑖
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is the probability that vehicle i is driven on a given day.
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We applied a maximum likelihood (ML) fitting procedure to estimate model parameters.
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The ML selects the set of parameters that maximizes the likelihood function (i.e., the
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probability that certain travel distance will occur), labeled as 𝛬. The normalized log-
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likelihood,
𝑙𝑛(𝛬) 𝑁
,, serves as a metric of goodness-of-fit, where 𝑁 is the number of trip chains
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for a certain fitting procedure. Larger values of log-likelihood indicate better fits. The
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appropriateness of Eq. (1) was confirmed and discussed in section 1.1 of the SI.
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The method used here enables assessment of the sensitivity of results to different
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charging availability assumptions. We consider overnight home charging as the base
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case. The impact of workplace charging, assumed here to be available at the mid-point
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of habitual travel, is investigated as a sensitivity analysis with details given in the SI. We
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assume that personal travel patterns are unchanged when switching from ICEV to EV,
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and that the trips that exceed BEV range are satisfied by alternative transportation. The
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ITCD function for each vehicle was normalized to a one-year basis for TCO estimation.
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Trip chain parameters and driving distances over a year were provided in Excel format as
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SI.
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Individual TCO modeling.
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We estimated the TCO savings on switching from ICEV to EV specific to each
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individual’s travel pattern. The calculation considers major attributes relevant to the
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economics of vehicle electrification, including initial fixed vehicle cost exclusive of battery
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cost, tax and other initial fees, incremental cost of high-voltage battery systems, potential
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electrified mileage, improved fuel economy, fuel price, and one-time economic incentives
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(e.g., purchase subsidies). Other costs, such as maintenance, insurance, parking,
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cost/income of recycling/scraping are assumed not to be differentiators between the
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technologies and are not considered in the present work.
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The TCO of vehicle i over its service life is
𝑇𝐶𝑂𝑖 = (𝐶𝐹, 𝑖 + 𝐵𝐶𝑖 × 𝐶𝐵,𝑖) + 𝑇𝑖 ― 𝑆𝑖 + (𝐹𝑖 + 𝐴𝑖) ×
1 ― (1 ― 𝑟)𝑎 𝑟
(2)
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where 𝐶𝐹, 𝑖 [USD] is the initial vehicle cost excluding the high-voltage battery system; BCi
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[kWh] is the battery capacity required for an EV to achieve a given all-electric-range (AER)
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on the road; AER [km] is all electric range, namely the maximum usable mileage range
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of a EV using power from its battery packs; 𝐶𝐵,𝑖 [USD/kWh] is the battery pack
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manufacturing cost multiplied by a markup factor to convert to retail price; 𝑇𝑖 [USD] is tax
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and registration fee; 𝑆𝑖 [USD] is the total purchase subsidy plus the monetary value of
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other tangible incentives (e.g., tax exemption); 𝐹𝑖 [USD] is the annual fuel cost, including
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expenses for electricity and liquid fuels; 𝐴𝑖[USD] is annualized alternative transportation
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cost, applicable to circumstances when BEV range is insufficient to cover a trip and
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alternative transportation is needed; r is the discount rate; and a [year] is the vehicle
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lifetime. Key parameters are summarized in Table S1.
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Vehicle cost is estimated based on a mid-size passenger car over the entire vehicle
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lifetime of 15 years33. A shorter ownership period of 3 years is examined to reflect the
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perspective of a first owner who does not plan to keep the vehicle for its entire service life
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(see SI section 2.1). Five mid-size EV models with different powertrains and all electric
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ranges were considered: PHEV20, PHEV50, BEV150, BEV300 in current and future
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scenarios, and BEV450 in the future scenario only. Range values are average ‘real-world’
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AERs in km over the vehicle life time. The battery storage requirements listed in Table 1
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account for the difference between dynamometer-tested and on-road energy
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consumption, battery maximum and minimum state of charge, and battery deterioration
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over the vehicle life time.31 See SI section 1.3 for calculation details.
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Table 1. All-electric-range (AER) and battery capacity for EVs considered Name
in
this
study
2015
AER
Battery
(km)
(kWh)
PHEV20
20
5.3
PHEV50
50
19.3
BEV150
150
42.4
capacity
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BEV300
300
86.4
PHEV20
20
4.3
PHEV50
50
14.2
BEV150
150
34.5
BEV300
300
67.5
BEV450
450
111.4
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Battery pack manufacturing costs of $250/kWh and $300/kWh in 2015 for BEVs and
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PHEVs, respectively, were taken from the literature.1, 33-35 We assume different battery
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types for PHEVs and BEVs; manganese oxide spinel with a graphite electrode (LMO-G)
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for PHEV models and lithium nickel cobalt aluminum oxide with a graphite electrode
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(NCA-G) is for BEV models. Costs of future vehicle technologies were estimated from
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manufacturing cost for major components (e.g., battery pack system)36 multiplied by a
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factor of 1.5 to provide retail prices for calculating TCO (the 50% mark-up accounts for
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non-manufacturing costs including R&D, sales and marketing, corporate staff, and
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profits33). The future Reference scenario considers battery pack costs of $80/kWh for
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BEVs and $100/kWh for PHEVs, and the High-battery-cost and Low-battery-cost
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scenarios explore the sensitivity to $20/kWh higher and lower costs than the Reference
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scenario. TCO modeling parameters are provided in Table S1.
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For BEV adopters, we include the cost for alternative transportation options when the
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BEV is incapable of covering long-distance travel. Alternative transportation options
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include rail-based transit, bus, household second car (ICEV), taxi, car rental, and car-
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pooling. Due to the scarcity of information on how people would adjust their travel
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behavior when the BEV range is insufficient to meet daily travel needs, we assumed that
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the proportion of different alternative transportation modes is the same as the current
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travel modal split in the five locations (Figure S4). A weighted average cost for alternative
201
transportation was estimated for each location. We do not include inconvenience costs
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associated with BEV users needing to find alternative transportation on days where the
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BEV range fails to satisfy their travel needs (e.g., time and expense to travel to pick-up
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or drop-off rental cars). An analysis of the sensitivity of the results to the alternative
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transportation costs is given in the SI.
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The heterogeneity for travel patterns among individual vehicle users affects their
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potential electrified mileage and capability of operating a BEV. While there are many
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factors other than ownership cost that influence vehicle purchase choices, a compelling
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economic case is essential to the high levels of EV adoption needed to meet climate
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goals. We identify an individual user as a potential EV adopter if the total cost of
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ownership, TCO, is lower than for the ICEV counterpart. Inconvenience caused by limited
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EV range is treated as an additional threshold to define potential BEV adopters. By
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varying the inconvenience threshold, the impacts of non-monetary factors including
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tolerance for inconvenience, travel restrictions on conventional cars, and accessibility to
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fast charging are evaluated. We consider 12 inconvenience days per year, i.e. 1 day per
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month, as the baseline case and sensitivity analysis cases for 1 day per year and 52 days
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per year, i.e., 1 day per week, are given in the SI. The 52 days per year case approximates
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the inconvenience level of conventional gasoline vehicles in Beijing that are prohibited
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from urban streets one workday per week depending on their last digit of plate number.
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The estimated adoption rate serves to recognize the role of cost and inconvenience
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impact in EV adoption, not an ultimate prediction for EV penetration. Easy access to
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charging infrastructure is a challenge for mass EV adoption; effective policy support is
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required to overcome this challenge.
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Individual GHG mitigation potential modeling. A full life-cycle analysis of GHG
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emissions includes emissions during fuel production, vehicle operation and vehicle
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manufacture, and is calculated according to individual travel patterns. Marginal emission
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factors that reflect a shift in generation mix in response to demand for charging of EV
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were adopted in several studies17-20, but the different assumptions and system
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boundaries makes it hard to compare results. Estimating the marginal emissions due to
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EV charging requires a high-resolution profile of the power demand combined with a
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model of the generation fleet and dispatch strategy for the regional electricity supplier.
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This level of detail is beyond the scope of the present study. Furthermore, the generation
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mix most regions is shifting rapidly with the declining cost of natural gas and rapid
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deployment of wind and solar power. Similar to many other studies15, 21, 22, we use the
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regional average grid mix in this analysis as a benchmark, which might be adjusted to
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reflect local dispatching strategies, EV market penetration, charging behavior, etc. Coal-
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fired power plants accounted for 71% 37, 33% 38, 62% 38, 28% 38 and 46% 39 of electricity
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generation in Beijing, Atlanta, Minnesota, Seattle, and Germany in 2015. The shares of
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other feedstocks in 2015 and in the future scenario are given in Figure S5. Two sensitivity
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cases that assume 100% renewable electricity and 100% coal-fired electricity,
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respectively, are given in Table S14. The upstream emissions of fuel production (g CO2e
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per unit gasoline, diesel or electricity) in five locations were estimated using the
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GREET2016 model48, as shown in Table S5, with consideration paid to the local energy
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generation mix, generation efficiency, and transmission loss. For vehicle manufacturing
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processes, we employed the BatPac model40 to determine battery composition for each
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powertrain configuration as shown in Table S7, which provided the GREET model with
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input data to estimate GHG emissions for components production (see Figure S6), vehicle
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assembly, disposal, and recycling. Details of vehicle fuel economy and GHG emissions
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from alternative transportation systems are summarized in sections 1.3 and 2.4 of the SI.
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Finally, we calculated individual life cycle GHG emission reductions on a yearly basis to
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characterize the climate change mitigation potentials from switching to various EV
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technologies.
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RESULTS
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Regional average TCO breakdown. Customer TCO considers major attributes relevant
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to the economic competitiveness of EVs over their lifetime. In our model a mid-size car
258
BEV300 in 2015 costs ~32,000 USD more than a typical mid-size ICEV as shown in Fig.
259
1(A). This additional purchase cost accounts for 51%-68% of TCO and is offset only
260
partially by monetary incentives upon EV purchase and savings from lower fuel costs.
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Fuel costs are impacted by driving patterns, fuel economy and fuel price differentials
262
between conventional liquid fuels and electricity. U.S. cities have more expensive
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electricity and less expensive gasoline than Beijing, which decreases the economic
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attractiveness of EVs in the U.S. There are significant regional differences in the cost of
265
alternative transportation required when the range of BEVs does not match the individual
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travel demand on a given day. Beijing has an economical and convenient public
267
transportation system, which favors BEV adoption. Minneapolis does not, which drives
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the cost for alternative transport to 24% of the TCO for a BEV150 (Table S1).
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Individual TCO benefits and potential EV adopters. There is not only inter-city but also
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intra-city heterogeneity in individual customer benefits, depending on their travel patterns
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and local features. Currently, PHEVs are generally close to cost competitive. The TCO
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gap between PHEVs and ICEVs is less than $8,000 for half of the individuals (Fig. 1(B)).
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Some individuals in Beijing (42%), Seattle (12%), and Germany (9%) have TCO benefits
274
from adopting a BEV150 (Fig. 1(C)). However, long-distance travel that requires
275
alternative transportation poses significant inconvenience for BEV users. Here we identify
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the individuals with both TCO benefit and a level of inconvenience below certain
277
thresholds (i.e., number of days per year that driving distance exceeds BEV battery
278
range) as potential adopters. Assuming a 12-day per year inconvenience threshold (Fig.
279
1(C)) would prevent 21%, 3%, and 4% of individuals in Beijing, Seattle, and Germany
280
from switching to a BEV150, and would eventually lead to a potential adopter percentage
281
of 21%, 9%, and 5% in the three locations (Table S9). This finding is also crucial to
282
understand the impact of fast charging opportunity on EV adoption. Since charging away
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from home would cost more (including tangible costs such as parking fees and service
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fee and intangible costs such as time spent finding and waiting for chargers),
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requirements for additional fast charging on long-distance trips could also been seen as
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an inconvenience. Access to fast charging 52 days per year (~1 day per week) would
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enable a BEV150 to satisfy almost all TCO savers’ travel demands. BEV300s mitigate
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the range-related inconvenience (Fig. 1(C) insert), with less than 12 inconvenience days
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per year for 76%~100% individuals in the different locations. However, in our model the
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high battery cost results in low adoption rates for BEV300 (16% in Beijing and none in the
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other locations, Table S9).
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Fig. 1: Current (2015) total cost of ownership (TCO) for electric vehicles. (A) Regional
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average breakdown of TCO for internal combustion engine vehicles (ICEVs), plug-in
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hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs). (B) Box-whisker
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plots of individual TCO gaps between PHEVs and ICEVs, and (C) BEVs and ICEVs.
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Vehicle cost is estimated based on a generic mid-size passenger car; see Table S1 for
298
summary of TCO modeling parameters. In Beijing and U.S. cities, EVs are compared to
299
gasoline ICEVs. In Germany, EVs are compared to a 1:1 mix of gasoline and diesel
300
passenger cars. BEV adoption is additionally constrained by a battery range capable of
301
covering daily travel and keeping the number of days per year that driving distance
302
exceeds all-electric-range (AER) under certain thresholds (e.g., 12 days as shown in Fig.
303
1(C) insert; see Table S9 for 1-day and 52-day inconvenience threshold results). EVs are
304
assumed to be fully charged overnight at home; see Table S10 for results for various
305
considerations of alternative transportation costs; see Table S11 for results for a scenario
306
including working place charging.
307
Inconvenience sensitivity analysis. Low battery costs are essential for mass adoption
308
of BEVs. However, even with relatively high current battery costs, BEVs with small-sized
309
batteries (e.g., BEV150) could appeal to customers in regions with economic and
310
convenient alternative transportation systems and with strong EV-supportive policies.
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This observation is enhanced by the sensitivity of the adopter percentage to
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inconvenience thresholds and charging availability levels. By varying the inconvenience
313
thresholds from stringent (1 day per year) to tolerant (52 days per year), we observe that
314
BEV150 adoption increases by 37 (Beijing), 11 (Seattle), and 8 (Germany) percentage
315
points (Table S9). This finding helps quantify the benefits of non-monetary support for
316
electrification. For example, BEVs in Beijing are exempt from local traffic management
317
that prohibit using ICEVs in urban areas for one weekday per week27. If BEV users in
318
Beijing were to tolerate the same inconvenience level as ICEV users (~52 days per year),
319
the percentage of BEV150 adopters would increase to 42%, the highest acceptance
320
among all cities in this study (Fig. S7). In our model, additional workplace charging
321
provides a modest increase in adopter percentage (0-10 percentage points) for BEVs
322
(Table S11). However, we note that increased awareness and psychological comfort
323
provided by public and workplace charging may be effective enablers of EV adoption41.
324
Workplace charging is important also for customers without opportunities for reliable
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overnight charging opportunities and for individuals with long commutes.
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Cradle-to-grave GHG emissions. Consistent with previous studies
17, 19, 42-44,
we find
327
that local power carbon intensity is the most important factor determining cradle-to-grave
328
GHG emissions for EVs (Fig. S8). Minneapolis has the highest life cycle GHG emissions
329
for PHEVs, median of 4.3 and 4.0 t CO2e/(vehicle∙year) for PHEV20 and PHEV50, due
330
to the highest mileage (Fig. S2) and a considerable reliance on coal-fired power. On the
331
other hand, Beijing has the highest coal power share of 71% but the lowest average
332
annual mileage. Thus, even with a carbon-intensive power mix, BEVs in Beijing have
333
GHG emissions comparable to those in Atlanta and Seattle, with medians of 2.1 t
334
CO2e/(vehicle∙year). Despite large variations, the GHG emissions for PHEV adopters in
335
Beijing are comparable to those in Atlanta, Seattle, and Germany, with medians of 2.3-
336
2.7 t CO2e/(vehicle∙year). Beijing is the only city studied where an individual has lower
337
GHG emissions using alternative transportation than using a BEV, 127 and 214 g
338
CO2e/km respectively, reflecting the high ridership of public transit (Fig. S4). The per
339
passenger km life cycle GHG emissions of alternative transportation assumed in this
340
study are given in Table S12.
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Widely and unevenly distributed individual GHG mitigation potential. Consideration of
342
heterogeneous driving patterns reveals ranges for GHG mitigation potentials from EV
343
adoption (Fig. 2) that are more extensive than suggested by previous results relying on
344
aggregated vehicle travel profiles. With BEV300s for example, the individual life cycle
345
GHG mitigation potential has a wide and positively skewed distribution, and the median
346
values based on individual travel profiles are lower by up to 15% than those based on
347
aggregated average travel profiles (Fig. 2). This shows that estimations based on
348
aggregated travel profiles are biased. The bias is even more significant for EV ‘priority
349
customers’ whose travel patterns are more favorable to EV adoption and whose GHG
350
mitigation benefits are greater than estimated from aggregated travel patterns.
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352
Fig. 2 Comparison of different approaches to estimate cradle-to-grave greenhouse gas
353
(GHG) emissions reduction for current (2015) vehicles. We estimate cradle-to-grave GHG
354
emission reductions for 300-km battery electric vehicles (BEV300s) compared to internal
355
combustion engine vehicles (ICEVs) at the individual customer level (shown as bars),
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based on multiday disaggregated travel profiles. The top 25% of individuals who save the
357
most from, or pay the least for, adopting EVs were defined as priority EV customers and
358
their average GHG emissions are shown as dark orange bars, separated from those
359
constrained by range-limit convenience and hence are not considered as potential
360
adopters (grey bars). Previous studies usually use aggregated individual travel profiles
361
(black lines), which assume every driver follows the average aggregated behavior. Dotted
362
lines show one standard deviation caused by single-day instead of multi-day analysis,
363
see SI section 1.1 for details.
364
Positive alignment of customer and climate benefits. Fig. 3 demonstrates a common
365
cross-region tendency that individual users whose travel patterns are more economically
366
favorable (in terms of TCO) to EV have greater GHG mitigation. PHEVs show a clear
367
positive alignment of customer and climate benefits. Here we define the top 25% of
368
individuals who save the most from or pay the least for ICEV-to-EV switching as “priority
369
customers”. PHEV priority customers are likely to be associated with higher annual
370
mileage levels. They have more electrified mileage and mitigate more GHG emissions.
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These PHEV priority customers have greater annual GHG savings than the average of
372
other users by 288% (Beijing), 80~102% (U.S. cities) and 98% (Germany), depending on
373
local features (e.g., average mileage, power system carbon intensity). For BEVs, we
374
identified a cohort of individuals with high emission mitigation potentials that are
375
constrained by range-limit-inconvenience and therefore are not considered as BEV
376
adopters (black dots in Fig. 3). They could achieve greater GHG savings were they willing
377
to tolerate more than 12 days per year of inconvenience. For BEV150, the alignment of
378
customer and climate benefits is less significant because many individuals are
379
constrained by the range-limit-inconvenience thresholds. For BEV300, priority customers
380
would save more GHG than other users by between 28% (Minneapolis) and 473%
381
(Beijing). One significant implication from the positive alignment of customer and climate
382
benefits is that the GHG emission mitigation potentials may be underestimated if
383
evaluations are based on average travel profiles, because “priority EV customers” have
384
greater economic motivation for electrification. Currently, even priority BEV customers do
385
not typically have TCO-savings. In 2030-2035, however, with decreased battery cost,
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almost all BEV300 priority customers would achieve positive TCO-savings and are likely
387
to be ready for EV adoption (Fig. S11).
388 389
Fig. 3 Current (2015) individual cradle-to-grave greenhouse gas (GHG) emissions
390
reduction versus total cost of ownership (TCO) savings. Dots represent individuals with
391
different driving patterns. The top 25% of individuals who save the most, or pay the least,
392
from ICEV-to-EV switching are defined as “priority EV customers”. The black dots are
393
individuals constrained by range-limit-inconvenience (assuming 12 days per year
394
threshold), who are not considered as potential BEV adopters.
395
DISCUSSION
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Customer benefits in 2030-2035: trends and implications. Battery cost is the most
397
critical determinant for EV economic competitiveness. Economies of scale, automation in
398
production, and improvements in cell chemistry have reduced battery cost substantially.
399
The manufacturing cost of lithium-ion battery packs fell at an annual rate of approximately
400
10-20% over the past decade.34, 35 While it is difficult to project the evolution of future
401
costs, battery pack costs below $100/kWh seem plausible beyond 2030.35
402
To understand the sensitivity of our results to battery cost assumptions, we estimate
403
customer benefits in 2030-2035 under High, Reference, and Low-battery-cost scenarios.
404
The reduction of battery cost to approximately $80/kWh makes BEV economically
405
advantageous for a significant fraction of individuals, even if subsidies are eliminated (Fig.
406
4(A)). In the Reference scenario with a battery pack cost of $80/kWh, the BEVs are
407
projected to draw an increased percentage of adopters (14%-67% for BEV150, 23%-49%
408
for BEV300) compared to the present levels (Table S13). A longer-range BEV (BEV450)
409
is considered for 2030-2035, with adopters estimated at 18% in Beijing, 2%~5% in U.S.
410
cities, and 18% in Germany. In the Low-battery-cost scenario ($60/kWh), BEV450
411
adoption increased to 28% in Beijing, 12%~29% in U.S. cities, and 40% in Germany,
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412
while BEV300 adoption is 63% in Beijing, 62~70% in U.S. cities, and 85% in Germany,
413
suggesting a more favorable balance between AER and vehicle cost.
414
Our analysis indicates that a successful electrification strategy must include a portfolio
415
of EV offerings from which customers can choose vehicles that best meet their needs.
416
BEV300 will be suitable generally for drivers with intermediate mileage ranges (12,000 to
417
24,000 km per year) with 12-day inconvenience threshold (Fig S7). Allowing users to
418
choose from a menu of three BEV configurations in our model (BEV150, BEV300 and
419
BEV450) results in BEV adoption rates (40% in Minneapolis and over 75% in the other
420
four locations, Table S13, Fig S7) greater than the fleet electrification goal required to
421
achieve the COP21 climate targets (30% EV sales by 20301). However, even dramatic
422
improvements in BEV range and cost will not enable complete electrification of personal
423
vehicle travel. Users with high travel mileage (over 24,000 km per year) and low tolerance
424
for inconvenience are unlikely to accept even high range (real-world 450 km) BEVs. Given
425
that high mileage drivers are constrained mainly by inconvenience instead of cost, a
426
program that offers BEV users easy access to an ICEV, a hybrid electric vehicle (HEV),
427
or a PHEV as a loaner car when they have to driver long trips may help. Offering a loaner
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ICEV for 3000 km driving per year increases BEV300 adoption by 10-15 percentage
429
points in Beijing and U.S. cities, and 23 percentage points in Germany.
430
431
Fig. 4 Future (2030-2035) projection for individual users of 300-km battery electric
432
vehicles. (A) Total cost of ownership (TCO) gaps between electric vehicles (EVs) and
433
internal combustion engine vehicles (ICEVs) for three different battery cost scenarios. (B)
434
Cradle-to-grave greenhouse gas (GHG) annual emissions. Results for other EVs are
435
given in Fig. S10 and adopter percentages are given in Table S13. Sensitivity analysis
436
for 100% renewable electricity and 100% coal-fired electricity is given in Table S14.
437
Climate benefits: progress and enhanced effects. Climate benefits from fleet
438
electrification will depend on future progress in decarbonizing the power system37, 42-44.
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439
Based on published projections, we assume progress by 2030-2035 to higher renewable
440
shares in the power mix (8-57%, depending on location), and lower carbon intensity in
441
the power system (257-713 CO2e g/kWh) (see SI section 1.4 for emission modeling
442
details). Together with improvements in vehicle fuel economy, these factors lead to much
443
lower cradle-to-grave GHG emissions for EVs in 2030-2035 with median values of 125-
444
192 CO2e g/km for PHEV50s and 82-196 CO2e g/km for BEV300s, compared with 220-
445
284 CO2e g/km for ICEVs, Fig. S9. BEV300s generally have greater GHG mitigation
446
potential than PHEV50s, because they have longer electrified driving ranges. The
447
replacement of ICEVs by BEV300s in 2030-2035 could reduce life cycle GHG emission
448
by 1.0-2.1 tonnes CO2e/(vehicle year) (location-specific median value), although large
449
variations exist amongst users. The average mitigation for priority BEV300 customers is
450
1.7-3.0 tonnes CO2e/(vehicle year) which is a 32 - 63% reduction from the ICEV baselines
451
(Fig. S11). Results for two sensitivity case, 100% renewable electricity and 100% coal-
452
fired electricity, are provided in Figure S14.
453
Limitations and future work. This study assumes that cost and inconvenience are the
454
primary determinants of customer purchase choices. There are important market and
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455
societal factors (e.g., symbolic attributes4, range anxiety, costs and reliability of charging
456
infrastructure) which are not considered in the present analysis. Electrification of mid-
457
sized cars is considered in this study. Future work is needed with a more realistic fleet of
458
light-duty vehicles (compact cars, sport-utility vehicles, and trucks) and medium- and
459
heavy-duty vehicles.45, 46 The effect of ambient temperature on EV performance can be
460
important
461
include the climate and economic benefits of integrating EV into the broader energy
462
system, such as using V2G technologies to facilitate integration of fluctuating renewable
463
power sources.
464
power which would decrease the carbon intensity of the power system.
465
collaborative strategy between the renewable power participants and EV owners may
466
guide consumer charging behavior towards a pattern that increase revenues and
467
incentive for both parties.
468
of EV and renewables integration is needed. Despite its simplicity, the model provides
469
useful insight into the major factors impacting the economic and climate benefits of EVs.
470
The present study shows that the economic and emission benefits of EVs are generally
47
but was beyond the scope of the present work. The present work does not
48, 49
Widespread EV adoption may facilitate an increase in renewable
51
50
Providing a
Future work that explicitly reveals such embedded benefits
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similar across the different regions and that mass electrification requires a portfolio of
472
EVs. A significant cohort EV customers is identified that would find EVs economically
473
attractive, will have large GHG reductions, and their adoption of EVs may initiate a mass
474
transition to electrified mobility. Low cost batteries (below approximately $100/kWh) are
475
required for mass EV adoption.
476
477
Acknowledgments
478
We thank Hyung Chul Kim, Robert De Kleine, Sandra L. Winkler, Wei Shen, and Weijian
479
Han of Ford Motor Company for helpful comments. The contents of this paper are solely
480
the responsibility of the authors and do not necessarily represent the official views of the
481
sponsors or the Ford Motor Company. While this article is believed to contain correct
482
information, Ford Motor Company (Ford) does not expressly or impliedly warrant, nor
483
assume any responsibility, for the accuracy, completeness, or usefulness of any
484
information, apparatus, product, or process disclosed, nor represent that its use would
485
not infringe the rights of third parties. Reference to any commercial product or process
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486
does not constitute its endorsement. This article does not provide financial, safety,
487
medical, consumer product, or public policy advice or recommendation. Readers should
488
independently replicate all experiments, calculations, and results. The views and opinions
489
expressed are of the authors and do not necessarily reflect those of Ford. This disclaimer
490
may not be removed, altered, superseded or modified without prior Ford permission.
491
Funding: This work is supported by the National Key Research and Development
492
Program of China (2017YFC0212100), the National Natural Science Foundation of China
493
Project (91544222, 71722003), Ford Motor Company’s University Research Programs,
494
Volvo Group in the Research Center for Green Economy and Sustainable Development
495
in Tsinghua University, and Harvard Global Institute. S.Z. is supported by Cornell
496
University’s David R. Atkinson Center for a Sustainable Future. Author contributions: X.H.
497
and S.Z. contributed equally to this study. Y.W., S.Z., and X.H. conceived the research
498
idea; M.A.T. and X.H. prepared the individual travel pattern dataset; X.H. contributed to
499
new analytic tools; X.H., S.Z., T.J.W., X. L. and Y.W. analyzed the data; M.A.T., K.M.Z.,
500
X.L., M.B.M, C.P.N and J.H. provided valuable discussions on research and paper
501
organization; X.H., S.Z., T.J.W., X.L. and Y.W. wrote the paper with contributions from all
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authors. Data and materials availability: The data that support the finding of this study are
503
provided in Supporting Information.
504 505
Supporting Information
506
Supporting information for this article is summarized in separate files, including a word
507
document of methods, data, and additional analysis and an Excel file of individual trip
508
chain dataset.
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