Do Battery-Switching Systems Accelerate the
Adoption of Electric Vehicles?
A Stated Preference Study

Nobuyuki Ito
Kenji Takeuchi
Shunsuke Managi
November 2016
Discussion Paper No.1645

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANDo Battery-Switching Systems Accelerate the Adoption ofElectric Vehicles? A Stated Preference Study
Nobuyuki Ito†, Kenji Takeuchi¶, and Shunsuke Managi‡†

Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Japannobuyuki.itoh@gmail.com¶

Graduate School of Economics, Kobe University, Japan‡

takeuchi@econ.kobe-u.ac.jp

Urban Institute & School of Engineering, Kyushu University, Japan

managi.s@gmail.com

November 25, 2016
Abstract
We estimate willingness-to-pay (WTP) for battery-switching electric vehicles (SEVs)
by using a stated choice experiment. Our estimation results show that individuals havehigh WTP for SEVs, provided sufficient battery-switching stations exist. Whenbattery-switching infrastructure represents 50% of the current number of gasoline
stations, individuals are indifferent between conventional gasoline vehicles and SEVs,
which have a 521 thousand yen lower price than gasoline vehicles. Moreover, theestimation results suggest that vehicle drivers may recognize SEVs as vehicles for
shorter drives such as daily shopping trips and thereby have lower marginal WTP withrespect to cruising range.

JEL Classification: L62, Q42, Q51
Keywords: electric vehicle, battery-switching stations, stated preference method, choice
experiment, willingness-to-pay

11 Introduction
Electric vehicles (EVs) are expected to play a significant role in reducing carbon
emissions from the transportation sector. More than 550 thousand EVs were soldworldwide in 2015, increasing the global stock of EVs to 1.26 million (Electric
Vehicles Initiative, 2016). While these figures are promising, EVs still represent only
0.1% of total passenger cars globally. One of the major obstacles to the further adoptionof EVs might be consumer anxiety about the new technology. EVs have a lower driving
range compared with conventional gasoline vehicles (GVs) and a longer waiting timefor fuel charging.
One solution to this problem might be investment in battery-switching systems forEVs. An EV battery-switching station allows drivers to exchange their depleted
battery for a fully charged one in a few minutes. Since the driver does not own thebattery in the car and is charged per mile driven, it is also possible to reduce theupfront costs that prevent people from purchasing EVs. However, the notion ofbattery-switching has been unsuccessful so far. Better Place, a venture company thatdeveloped battery-switching services for EVs and attracted significant attention andcapital, collapsed in May 2013 (Pearson and Stub, 2013). Tesla Motors presented theidea of battery swap stations in June 2013, but it is unlikely to pursue further
development at present (Korosec, 2015).
This study investigates potential demand for battery-switching EVs (SEVs) byusing a stated preference survey. Since demand for alternative fuel vehicles criticallydepends on the availability of fuel charging stations (Ito et al., 2013), it is important
to incorporate this feature into the analysis of demand for EVs. The choice
experiment design used in this study allows us to investigate whether consumers

2evaluate SEVs differently from battery-recharging EVs (REVs). More specifically,
the effect of fuel availability on willingness-to-pay (WTP) for EVs might be different
between these two types of EVs. WTP for SEVs can be strongly affected by theavailability of switching stations, since the attractiveness of switchable batteries
critically depends on the infrastructure network specially designed for this technology.
If consumers prefer SEVs to REVs, it suggests that there is still a chance for theinvestment in battery-switching stations as well as the development of vehicles
specially designed for switching.
Several studies have investigated the potential demand for EVs by using a statedpreference methodology (Beggs et al., 1981; Ewing and Sarigöllü, 1998; Bunch et al.,
1993; Brownstone et al., 2000; Axsen et al., 2013; Ito et al., 2013). These studies ask
respondents, by using hypothetical questionnaires, to choose their preferredalternative from a set of profiles that have different levels of car attributes. From theestimated coefficients of these attributes, one can calculate marginal WTP to improveparticular attributes of EVs. Many previous studies have focused on the driving range
issue of EVs. For example, Daziano (2013) found that the mean estimated WTP for aone-mile improvement in the driving range of an EV in the past studies in the UnitedStates is 100 dollars per mile. Infrastructure development for EVs has been lessfocused upon by previous studies even though consumers’ valuation of range may besensitive to the availability of refueling infrastructure (Dimitropoulos et al. 2013).
Moreover, to our knowledge, no study has thus far examined the difference in WTP
for SEVs and REVs.
The rest of the paper is organized as follows. Section 2 introduces the survey
design of our stated preference questionnaire. Section 3 explains the empirical3

strategy for the WTP estimation. Section 4 presents the estimation results and theirimplications. Section 5 concludes.

2 Survey design
2.1 Vehicle attributes and their levels
The decision to purchase a car depends on various factors. In this study, we selectednine attributes based on our research objectives and the findings of previous studies.
Attributes connected with refueling, refueling rate, and fuel availability are importantfactors that affect vehicle choice (Ewing and Sarigöllü, 1998; Potoglou and Kanaroglou,
2007). Table 1 presents the attributes and their levels used in our study. Details of theattributes can be summarized as follows:
Fuel type: To investigate potential demand for SEVs relative to other vehicles thatuses different types of fuel, we considered the following four fuel types: GVs, hybrid
electric vehicles (HEVs), REVs, and SEVs. GVs were treated as the base alternativethat respondents were willing to purchase.
// Table 1: The attributes and levels of the choice experiments //Body type: Respondents were asked to choose two vehicle body types out of the ninealternatives that they would consider in their next purchase decision; these two bodytypes were used to create respondent profiles. The following nine categories of vehicle
body types were included in the choice experiment: subcompact, compact/hatchback,coupe, sedan, convertible, wagon, minivan, SUV/pickup truck, and truck/bus. The bodytypes are assumed to be unrelated to fuel types.
Manufacturer: Respondents were asked to choose one automobile manufacturer

4

from the list of manufacturers that they would consider in their next purchase decision.
The list comprised 32 manufacturers, including foreign companies. We used their choice
to create profiles for the base alternative. It was assumed that only the following four
representative automobile manufacturers in Japan produce HEVs, REVs and SEVs:
Toyota Motor Corporation, Honda Motor Company, Nissan Motor Company, andMitsubishi Motors Corporation.
Cruising range: Cruising range is a critical attribute for choosing alternative fuelvehicles. Cruising ranges were set as 800 kilometers for GVs; between 800 kilometersand 1,200 kilometers for HEVs; and between 100 kilometers and 300 kilometers for
REVs and SEVs.1
Refueling rate: The total time for refueling was set as 5 minutes except for REVs.
Compared with other vehicles, REVs usually take a longer time to recharge. The
refueling rate of REVs were set as 10, 15, and 30 minutes. These are typical charging
times for fast-charging stations that have the latest technology. When battery-switchingstations are available for SEVs, the time taken to recharge is less than 5 minutes. For
example, Tesla Motors announced that it takes only 90 seconds to switch batteries forEVs.2 Respondents watched a few minutes of video that introduced how drivers can
switch the discharged battery in an SEV for a fully charged one at a battery-switchingstation.
Carbon dioxide emissions: It was assumed that by choosing HEVs or EVs, drivers
can reduce their emissions of carbon dioxide. When a respondent chooses an HEV, hisor her emission levels of carbon dioxide are reduced by 20%, 30%, 40%, or 50% fromThe cruising range of Nissan’s EV model Leaf is between 100 kilometers and 220 kilometers
depending on the speed, climate, road, and so on. (Source: Nissan Leaf’s website)
2
https://www.tesla.com/jp/videos/battery-swap-event
15

the current levels of emissions from GVs. When a respondent chooses REVs or SEVs, it
is reduced by 50%, 60%, 70%, or 80% from the current levels.
Fuel availability: Fuel availability was described in terms of the percentage ofrefueling stations among existing gasoline stations. Thus, the fuel availability of GVs
and HEVs is 100%. The fuel availability of REVs and SEVs was set as 10%, 25%, 50%,
and 75%. In Japan, there were more than 13,000 spots where a fast-charging service for
EVs is available as of October 20163 and 32,000 gasoline stations as of March 2015.4
Purchase price: The purchase price for GVs was based on respondents’ answers
regarding the amount they will spend on their next purchase opportunity. The purchaseprices for HEVs, REVs, and SEVs are represented by the increase from the price that
customers will pay for GVs.
Annual fuel cost: The annual fuel costs for GVs were calculated from respondents’current number of refuels per month and the average amount they spend per refuel. The
annual fuel costs for HEVs, REVs, and SEVs were indicated by the decrease in theannual fuel costs from that of GVs. Respondents were instructed to assume that theannual fuel costs include the cost of replacing the batteries of REVs. The attributes ofdifferent fuel types, refueling frequencies, and refuel stations are correlated because of
the shared technological characteristics among these vehicles.
Respondents were also instructed to assume that all the other attributes that are notindicated in choice set as being identical among the alternatives. Respondents were able
to obtain information regarding the above vehicle attributes by clicking a link whileanswering the choice experiment questions.

3
4

The data is from CHAdeMo. http://www.chademo.com/wp/
http://www.meti.go.jp/press/2016/07/20160712003/20160712003.html

6

2.2 Design of choice sets
The number of alternatives in each choice set was set as four. An example of a choice
set is presented in Figure 1. The profile for vehicle 1 (GVs) was created on the basis of
respondents’ answers regarding their next purchase opportunity. Throughout the eightchoice sets that each respondent faced, vehicle 1 was fixed as the base alternative.
The profiles for HEVs, REVs, and SEVs were created by using orthogonal arrays for
10 attributes and four levels. In total, 64 profiles for each alternative fuel vehicle wereconstructed; therefore, 192 (64 × 3) profiles of alternative fuel vehicles were compiled.
The profiles of GVs were the same between choice sets. Then, two profiles from thealternative fuel vehicles were randomly drawn and matched with the GV profile; hence,
128 choice sets and 16 versions of a series of eight choice sets were created. One of the
16 versions was randomly assigned to each respondent.

// Figure 1: Example of a choice set //2.3 Data
We carried out a web-based survey between February 21 and 25, 2011. The clarity ofthe questionnaire was checked by a pretest conducted in December 2010. We sente-mails to invite registered monitors to the online survey and 53,066 people aged
between 19 and 69 responded. The response rate was 17%. Table 2 reports the summary
statistics.

// Table 2: Summary statistics of respondents’ characteristics //7

3 Discrete choice model
We adopt a random utility model to analyze vehicle purchase behavior (McFadden,1974). The utility of the respondent k choosing alternative i is denoted by:

𝑈𝑘𝑖 = 𝑉(𝑥𝑘𝑖 , 𝑡𝑘𝑖 ) + 𝜖𝑘𝑖(1)where 𝑉(∙) and 𝜖𝑘𝑖 are the observable and unobservable terms, respectively, 𝑥𝑘𝑖 is theattribute vector, and 𝑡𝑘𝑖 is the price of alternative i. The probability of choosing
alternative i from the choice set C is represented by

𝑃𝑘𝑖 = Pr⁡(𝑈𝑘𝑖 > 𝑈𝑘𝑗 , ∀𝑗 ∈ 𝐶, 𝑗 ≠ 𝑖)
= Pr(𝑉𝑘𝑖 − 𝑉𝑘𝑗 > 𝜖𝑘𝑗 − 𝜖𝑘𝑖 , ∀𝑗 ∈ 𝐶, 𝑗 ≠ 𝑖).

(2)

Assuming a Gumbel distribution for the difference in the unobservable error terms, theprobability of choosing alternative i can be represented by the conditional logit model
as:

𝑃𝑘𝑖 =

𝑒 𝑉𝑘𝑖
𝑉

∑𝑗 𝑒 𝑘𝑗.

(3)

The parameters of the utility function were estimated by maximizing the following
log-likelihood function:𝐽𝑖ln𝐿 = ⁡ ∑𝐾
𝑘=1 ∑𝑖=1{𝛿𝑘 ln𝑃𝑘𝑖 }.

8(4)where 𝛿𝑘𝑖 is a dummy variable that takes one when respondent k chooses an alternativei, and zero otherwise. Marginal WTP to increase a level of each attribute is calculatedby dividing the estimated coefficient of an attribute variable by the estimated coefficientof a price variable.

4 Estimation results
4.1 Main results
The estimation results of the conditional logit model are shown in Table 3. We
include the quadratic terms for cruising ranges and establishment of infrastructure in themodel to capture the diminishing effect of these factors on vehicle choice. The
estimated results for each coefficient of the explanatory variables used in our models are
as follows:
Alternative specific constants (ASCs): There are five ASCs in our model: HEVs,
REVs (three refueling rates), and SEVs. All ASCs except for SEVs are positive andstatistically significant. This means that an individual prefers HEVs and REVs to GVs
provided that all other vehicle attributes are the same. On the contrary, the ASCs of
SEVs is not statistically significant. This means that an individual is indifferent to the
choice between SEVs and GVs. SEVs should be more attractive in terms of price,environmental friendliness, and other attributes. The difference between the coefficientsof REVs with different recharging times indicates the utility increase by reducing thetime required for recharging at a station. For example, an individual is willing to pay
127.5 thousand yen (about 1,100 US dollars) to reduce the recharging time from 30
minutes to 15 minutes.
Fuel availability: The coefficient of Station is positive, whereas that of Station9squared is negative. These results are consistent with our expectations that the marginalutility of expanding stations for refueling is diminishing. The interaction term between
SEVs and Station is positive and statistically significant. This means there is a higher
benefit in expanding stations for SEVs than that for other vehicles.
Cruising range: The estimated coefficient of cruising range and its squared term
represent their marginal utilities in the utility function. As expected, the coefficient of
cruising range is positive and the squared term is negative. These results indicate thatmarginal utility for a longer cruising range is diminishing. We also estimated theparameters of the cross terms between REVs or SEVs and the square term of cruising
range. The coefficients of these cross terms are negative, which means that the marginalWTP for the cruising range of REVs or SEVs is lower than that for HEVs. Note that thecruising ranges of HEVs are different to those of EVs.
Body types: The coefficients of coupe, sedan, minivan, and wagon are negative,
whereas the coefficient of SUV/pickup truck is positive. These results indicate that
although WTP for HEVs, REVs, and SEVs decreases when the body type is coupe,
sedan, minivan, or wagon compared with subcompact, compact/hatchback, convertible,
or truck/bus, it increases when the body type is SUV/pickup truck.
Manufacturers: The coefficients of Toyota, Honda, Nissan, and Foreign are
significantly positive and the coefficient of Mitsubishi is not significant. These results
indicate that although WTP for HEVs, REVs, and SEVs increases when the
manufacturers are Toyota, Honda, Nissan, and foreign automobile manufacturers
compared with other automobile manufacturers, there is no difference in it between
Mitsubishi and them.

10// Table 3: Estimation results //4.2 Establishment of infrastructure
The estimated coefficients suggest that infrastructure development for EVs is
positively evaluated by individuals; however, there is a maximum WTP value withregard to the percentage of stations relative to the current gasoline stations. WTP to
expand recharge stations for REVs by as much as X% of the current number of gasoline
stations from the situation of no recharge stations is calculated by
𝑊𝑇𝑃𝑅𝐸𝑉_𝑆𝑇𝐴𝑇𝐼𝑂𝑁 = (𝛽𝑠 𝑋 + 𝛽𝑠2 𝑋 2 )/𝛽𝑝 ,(5)where 𝛽𝑠 is the coefficient for Station, 𝛽𝑠2 is that for Station squared, and 𝛽𝑝 is thatfor price. The WTP to expand battery-switching stations for SEVs is calculated by
𝑊𝑇𝑃𝑆𝐸𝑉_𝑆𝑇𝐴𝑇𝐼𝑂𝑁 = (𝛽𝑠 𝑋 + 𝛽𝑠2 𝑋 2 + 𝛽𝑆𝐸𝑉 𝑋)/𝛽𝑝 ,(6)where 𝛽𝑆𝐸𝑉 is the coefficient of the interaction term between SEVs and Station. Since
this is positive and statistically significant, the expansion of SEV stations is evaluated as
being higher than the expansion of stations for other alternative fuel vehicles.
Figure 2 shows WTP to build infrastructure for REVs and SEVs, with the percentage
compared with current gasoline stations on the horizontal axis. For example, WTP tobuild infrastructure as much as 50% of current gasoline stations is 36.4 thousand yen(about 330 US dollars) for REVs and 52.9 thousand yen (about 4,80 US dollars) for
SEVs. Under a realistic assumption,5 the price of SEVs must thus be 521 thousand yen
lower than GVs for consumers to be indifferent when the number of battery-switching5

We assume Range=800km, Station=100%, and Emission reduction=0% for GVs andRange=200km, Station=50%, and Emission reduction=50% for SEVs.

11stations is half the number of current gasoline stations. Since the relationship between
the WTP and refuel station is quadratic, a maximum WTP value exists for expanding
infrastructure. WTP for REV or SEV infrastructure reaches the maximum value when
refuel stations represent 62% or 78% of current gasoline stations, respectively.

// Figure 2: WTP to build the infrastructure of REVs and SEVs //4.3 Cruising range of EVs
Figure 3 shows WTP for the cruising range of alternative fuel vehicles. Similar to therelationship between WTP and refuel stations, marginal utility diminishes with respect
to cruising range. WTP for the cruising range of HEVs is higher than that of REVs or
SEVs. For example, WTP for a cruising range of 1,000 km with regard to HEVs is
2,810 thousand yen (about 25,500 US dollars). On the contrary, WTP for a cruising
range of 200 km with regard to REVs or SEVs is 731 thousand yen (about 6,600 US
dollars). The cruising range of REVs or SEVs is shorter than that of HEVs; hence,
drivers may recognize REVs or SEVs as vehicles for shorter drives such as daily
shopping trips. In that case, the results might indicate that the difference in the purposeof use decreases marginal utility with respect to cruising range.

// Figure 3: WTP for cruising range //5 Concluding Remarks
Notwithstanding its expected significant role in reducing carbon dioxide emissions
for mitigating climate change, the current penetration level of EVs is still very low.
Battery-switching systems might greatly promote EV use by alleviating driving range
12

anxiety and reducing the upfront cost of purchase. Our estimation results show that
individuals have lower WTP for SEVs despite the existence of sufficientbattery-switching stations. Under the scenario that battery-switching infrastructures
represents 50% of that of current gasoline stations, individuals are indifferent between
GVs and SEVs, which have a 521 thousand yen lower price than GVs. However, weshould remember that the future of batteries might be different from now. For
example, the estimated cost of Li-ion battery packs for EV manufacturers declines byapproximately 14% annually (Nykvist and Nilsson, 2015).
The bankruptcy of Better Place and retreat of Tesla Motors from battery-switchingsystems lend support to the difficulty of developing a network of battery-switchingstations in a profitable manner. Maintaining the system is costly, as it is required tohold more batteries than the number of EVs on the road (Avci et al., 2015). On theother hand, there is an ancillary benefit of battery-switching systems such as avoiding
high peak demand and the ability to coordinate discharging back to the power gridthrough vehicle-to-grid technology (Widrick et al., 2016). The consideration of thesecosts and benefits is necessary for judging the economic rationality of adopting
battery-switching systems for EVs.

Acknowledgement
This study was supported by a grant from the Policy Studies of the Environment andEconomy (years 2009–2011) and S15 of the Ministry of Environment, Japan, and
JSPS KAKENHI Grant Numbers JP16H03006, JP26241033, and JP26000001.

13

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15

Table 1: The attributes and levels of the choice experiments
Attributes
Fuel type
Body type
Manufacturer
Cruising range(km)
Refueling rate
(minutes)
Fuel availability(% of the existing
gasoline stations)
Carbon dioxide(% reduction of apresent averagecar)
Purchase price(including tax)

GV
HEV/REV/SEV
GV
HEV/REV/SEV
GV
HEV
REV/SEV
GV/HEV/SEV
REV
GV/HEV
REV/SEV

LevelsGV
Base 1aBase 1aBase 2aBase 2a
800
800
100
5
10
100%
10%

GV
HEV
REV/SEV

0%
20%
50%

HEV

REV

SEV

900
150

1000
200

1200
300

15

30

25%

50%

75%

30%
60%

40%
70%

50%
80%

Base 1b
Base 2b

GV
HEV
REV
SEV
GV
HEV
REV
SEV

Base 3
Base 3+10%
Base 3+20%
Base 3+30%
Base 3+50%
Base 3+20%
Base 3+40%
Base 3+60%
Base 3+80%
Base 3−10%
Base 3−5%Base 3+5%
Base 3+10%
Annual fuel costBase 4
Base 4−50%
Base 4−40%
Base 4−30%
Base 4−20%
Base 4−80%
Base 4−60%
Base 4−40%
Base 4−20%
60,000+80,000+100,000+
150,000+(Base4−50%) (Base4−80%) (Base4−80%) (Base4−80%)
Note: Base 1, Base 2, Base 3, and Base 4 are specified by respondents and differ between respondents.

16

Table 2: Summary statistics of respondents’ characteristics
Females
AgeHousehold income (104 yen)

Mean

SD

0.49
44.622650.4160.5
13.220397.456

Note: N denotes the number of respondents.

17

Min

Max

0
20
50

1
69
2250

N

2408
2408
2165

Table 3: Estimation results
Explanatory variables

Coeff.

Std. Err.

0.805***

0.081

1.192

**

0.480

1.278

***

0.482

REV with a battery rechargeable in 30 minutes

1.172

**

0.483

SEV

0.722

ASCs
HEV
REV with a battery rechargeable in 10 minutes
REV with a battery rechargeable in 15 minutes

0.486

Vehicle attributes
0.428 ***

0.120

***

0.006

***

0.003

**

0.035

***

0.001

-0.833***0.031-0.576***0.034-0.043

*

0.023

**

0.001

Coupe

-0.113 *

0.058

Sedan

-0.109

***

0.037

0.158

**

0.070

-0.313***0.074-0.123***0.040

Toyota0.274 ***

0.036

Honda

0.263

***

0.040

Nissan

0.208

***

0.043

Mitsubishi-0.052

0.083

**

0.050

Range [100 km]
2

Range [10,000 km]-0.019Station [%]

0.010

2

Station [%/1000]-0.082Emission reduction [%]0.004

Price [million yen]
Annual cost [yen](REV+SEV)*Range

2

SEV*Station

0.003

Body type

SUV
Minivan
Wagon
Manufacturer

Foreign

0.124

2

Psuedo-R

0.0696

Log Likelihood-20647.048

Observation

21672

Number of respondents

2408

18

Figure 1: Example of a choice set
Vehicle 1

Vehicle 2

Vehicle 3

Fuel type

Gasoline

Battery-Recharging EV

Battery-Switching EV

Body type

SUV, Pickup

SUV, Pickup

SUV, Pickup

Manufacturer

ToyotaToyotaBMW

Cruising range

800 km200 km200 kmRefueling rate

5 minutes

15 minutes

15 minutes

Fuel availability

All

75% of existing service

50% of existing service

stations

stations

stations

Carbon dioxide

Present level

60% less

70% less

Purchase price5 million yen

7 million yen

5.5 million yen

Annual fuel cost

100 thousand yen

80 thousand yen

170 thousand yen

existing

serviceChoose one vehicle

19

Figure 2: WTP to build the infrastructure of REVs and SEVs
0.7
REV

0.6

WTP (million yen)

SEV
0.5
0.4
0.3
0.2
0.1
0
0

20

40
60
% of Gas Stations

20

80

100

Figure 3: WTP for cruising range
3.5
REV/SEV

3

WTP (million yen)

HEV
2.5
2
1.5
1
0.5
0
0

200

400

600

Cruising Range (km)

21

800

1000

1200