Economic and Climate Benefits of Electric Vehicles in China, U.S., and

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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

4

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

7

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

10 11

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

ACS Paragon Plus Environment

1


15

6 Department

16

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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

25

account for differences in regional characteristics and individual travel patterns. To meet

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this need travel profiles for 1681 light-duty passenger vehicles in China, the U.S., and

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Germany were used to make the first consistent multi-regional comparison of customer

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and greenhouse gas (GHG) emission benefits of EVs. We show that despite differences

29

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.

32

Such “priority” EV customers have large (32%-63%) reductions in cradle-to-grave GHG

33

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

41

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,

63

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

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representation of vehicle usage. Many U.S. studies use the National Household Travel

71

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

112

comparisons with aggregated city, state, and national level statistics are provided in

113

section 1.1 of the supporting information (SI).

114

Trip chain modeling.

115

A statistical model was applied to describe individual trip chain distributions (ITCD),

116

reproduce a full year’s driving profile for every vehicle, and determine the number of

117

inconvenience days which are defined as days when battery electric vehicle range is less

118

than the daily travel. In the analysis of data from instrumented vehicles, a ‘trip’ is defined

119

as the travel between turning the vehicle on (‘key on’) and turning it off (‘key off’). A trip

120

chain is the total distance driven in a series of consecutive trips completed in a given time

121

period. For this study, a daily trip chain is travel in the 24-hour period beginning at 4:00

122

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

124

distribution of daily travel (total trip chain distance) for each vehicle was described using

125

equation (1) 27, 29

𝑓𝑖(𝑥) = 𝜆𝑖 ×

)

(

𝑤𝑖 ―𝑥/𝑘 1 ― (𝑥 ― 𝜇𝑖)2/2𝜎𝑖2 𝑖 𝑒 + (1 ― 𝑤𝑖) 𝑒 𝑘𝑖 2𝜋𝜎𝑖2

(1)

126

𝑓𝑖(𝑥) is the probability that an individual car, i, has a trip chain distance of 𝑥 km. The

127

exponential part of the distribution represents a ‘random’ travel pattern with frequent short

128

trips and occasional long trips, where 𝑘𝑖 is the characteristic distance. The Gaussian part

129

of the distribution depicts a ‘habitual’ travel pattern, where 𝜇𝑖 is a ‘habitual’ distance related

130

to repeated commuting trips or other regular trips. The parameter 𝑤𝑖 is a weighting factor

131

of the ‘random’ pattern; 1 ― 𝑤𝑖 is the weighting of the ‘habitual’ pattern. The parameter 𝜆𝑖

132

is the probability that vehicle i is driven on a given day.

133

We applied a maximum likelihood (ML) fitting procedure to estimate model parameters.

134

The ML selects the set of parameters that maximizes the likelihood function (i.e., the

135

probability that certain travel distance will occur), labeled as 𝛬. The normalized log-

136

likelihood,

𝑙𝑛(𝛬) 𝑁

,, serves as a metric of goodness-of-fit, where 𝑁 is the number of trip chains


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137

for a certain fitting procedure. Larger values of log-likelihood indicate better fits. The

138

appropriateness of Eq. (1) was confirmed and discussed in section 1.1 of the SI.

139

The method used here enables assessment of the sensitivity of results to different

140

charging availability assumptions. We consider overnight home charging as the base

141

case. The impact of workplace charging, assumed here to be available at the mid-point

142

of habitual travel, is investigated as a sensitivity analysis with details given in the SI. We

143

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

147

SI.

148

Individual TCO modeling.

149

We estimated the TCO savings on switching from ICEV to EV specific to each

150

individual’s travel pattern. The calculation considers major attributes relevant to the

151

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

156

technologies and are not considered in the present work.

157

158

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

160

[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

162

of a EV using power from its battery packs; 𝐶𝐵,𝑖 [USD/kWh] is the battery pack

163

manufacturing cost multiplied by a markup factor to convert to retail price; 𝑇𝑖 [USD] is tax

164

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

167

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

174

ranges were considered: PHEV20, PHEV50, BEV150, BEV300 in current and future

175

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

179

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


13


2030

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

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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

229

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

252

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

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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

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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

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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

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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

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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

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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

283

from home would cost more (including tangible costs such as parking fees and service

284

fee and intangible costs such as time spent finding and waiting for chargers),

285

requirements for additional fast charging on long-distance trips could also been seen as

286

an inconvenience. Access to fast charging 52 days per year (~1 day per week) would

287

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

289

per year for 76%~100% individuals in the different locations. However, in our model the

290

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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292 293

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

295

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.

297

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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311

This observation is enhanced by the sensitivity of the adopter percentage to

312

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

325

overnight charging opportunities and for individuals with long commutes.


24

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326


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.


25


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341

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.


26

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351

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),


27


Page 28 of 42

356

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.


28

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371

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,


29


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386

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


30

Page 31 of 42


396

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,


31


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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


32

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428

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.


33


Page 34 of 42

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


34

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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


35


Page 36 of 42

471

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


36

Page 37 of 42


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


37


Page 38 of 42

502

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.


38

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509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552


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Economic and Climate Benefits of Electric Vehicles in China, U.S., and

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