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

We estimate willingness-to-pay (WTP) for battery-switching electric vehicles (SEVs) by using a stated choice experiment. Our estimation results show that individuals have high WTP for SEVs, provided sufficient battery-switching stations exist. When battery-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, the estimation results suggest that vehicle drivers may recognize SEVs as vehicles for shorter drives such as daily shopping trips and thereby have lower marginal WTP with respect to cruising range.


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 sold worldwide 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 adoption of 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 time for fuel charging.
One solution to this problem might be investment in battery-switching systems for EVs. 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 the battery in the car and is charged per mile driven, it is also possible to reduce the upfront costs that prevent people from purchasing EVs. However, the notion of battery-switching has been unsuccessful so far. Better Place, a venture company that developed battery-switching services for EVs and attracted significant attention and capital, collapsed in May 2013 (Pearson and Stub, 2013). Tesla Motors presented the idea 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) by using a stated preference survey. Since demand for alternative fuel vehicles critically depends 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 3 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 the availability 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 the investment 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 stated preference 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 preferred alternative from a set of profiles that have different levels of car attributes. From the estimated coefficients of these attributes, one can calculate marginal WTP to improve particular 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 a one-mile improvement in the driving range of an EV in the past studies in the United States is 100 dollars per mile. Infrastructure development for EVs has been less focused upon by previous studies even though consumers' valuation of range may be sensitive 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 empirical strategy for the WTP estimation. Section 4 presents the estimation results and their implications. Section 5 concludes.

Vehicle attributes and their levels
The decision to purchase a car depends on various factors. In this study, we selected nine attributes based on our research objectives and the findings of previous studies.
Attributes connected with refueling, refueling rate, and fuel availability are important factors 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 the attributes can be summarized as follows: Fuel type: To investigate potential demand for SEVs relative to other vehicles that uses 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 alternative that 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 nine alternatives that they would consider in their next purchase decision; these two body types 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 body types are assumed to be unrelated to fuel types.

Manufacturer:
Respondents were asked to choose one automobile manufacturer 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, and

Mitsubishi Motors Corporation.
Cruising range: Cruising range is a critical attribute for choosing alternative fuel vehicles. Cruising ranges were set as 800 kilometers for GVs; between 800 kilometers and 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-switching stations 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 for EVs. 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-switching station.

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, his or her emission levels of carbon dioxide are reduced by 20%, 30%, 40%, or 50% from 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 of refueling 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 2016 3 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 purchase prices 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 the annual fuel costs from that of GVs. Respondents were instructed to assume that the annual fuel costs include the cost of replacing the batteries of REVs. The attributes of different 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 not indicated in choice set as being identical among the alternatives. Respondents were able to obtain information regarding the above vehicle attributes by clicking a link while answering the choice experiment questions.

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 eight choice 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 were constructed; 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 the alternative 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.

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: where (•) and are the observable and unobservable terms, respectively, is the attribute vector, and is the price of alternative i. The probability of choosing alternative i from the choice set C is represented by Assuming a Gumbel distribution for the difference in the unobservable error terms, the probability of choosing alternative i can be represented by the conditional logit model as: The parameters of the utility function were estimated by maximizing the following log-likelihood function: where is a dummy variable that takes one when respondent k chooses an alternative i, and zero otherwise. Marginal WTP to increase a level of each attribute is calculated by dividing the estimated coefficient of an attribute variable by the estimated coefficient of a price variable.

Main results
The estimation results of the conditional logit model are shown in Table 3. We 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 that marginal utility for a longer cruising range is diminishing. We also estimated the parameters 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 marginal WTP for the cruising range of REVs or SEVs is lower than that for HEVs. Note that the cruising ranges of HEVs are different to those of EVs. Mitsubishi and them.

Establishment of infrastructure
The estimated coefficients suggest that infrastructure development for EVs is positively evaluated by individuals; however, there is a maximum WTP value with regard 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 where is the coefficient for Station, 2 is that for Station squared, and is that for price. The WTP to expand battery-switching stations for SEVs is calculated by _ = ( where 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.