Abstract: According to the statistical analysis of the China Association of Automobile Manufacturers (CAAM), in March 2022, the production and sales of new energy vehicles in China reached 465,000 and 484,000 units, respectively, with an increase by 25.4% and 43.9% in the chain and an increase by 1.1 times in both year-on-year, and the market share reached 21.7% and continued to maintain the momentum of rapid growth. However, the problems with hard-charging, long charging time, and frequent queues often make users of new energy vehicles face the dilemma of being unwilling to charge and having no time to do so. To solve these problems, a new service model has emerged, namely the valet charging service model. Users can make an appointment through the Valet Charging App, and the charging staff will arrive at the user’s location within a specified time, drive the user’s vehicle to a nearby charging station, complete the charge and return to deliver the vehicle to the user. The research on the valet charging service model is of great importance to alleviate the pressure of urban charging, reduce the charging and waiting anxiety of users, and explore the potential users of new energy vehicles.
   In recent years, the problem with optimizing the distribution of the electric vehicle (EV) charging stations has become a hot topic of research for domestic and foreign scholars. However, there are still few studies on the location problem with charging stations under the valet charging service mode. LI et al.(2021) considered the stochastic nature of user charging demand and modeled the problem using the Two-stage Stochastic Mixed-Integer Program (TSMIP). Based on their work, the rate of users choosing valet charging service is calculated by integrating the charging time cost and the valet charging service price, and the TSMIP model is developed by combining this parameter to minimize the construction cost of the charging infrastructure and the travel cost of EV users to the charging station while maximizing the profit of the valet charging business. The Sample Average Approximation (SAA) and Epsilon constraint method are used to solve the multi-objective TSMIP model to explore the impact of the location strategy charging station on the operation of the valet charging service.
   Based on the results of numerical experiments, we find that while maximizing the expected profit of the valet charging business with a fixed number of charging stations, the location of charging stations will gradually spread around the region, increasing the cost of charging travel for EV users. Therefore, decision-makers should take into account the actual situation and the interests of both enterprises and users in choosing the location strategy of charging facilities. Moreover, the upper bound of charging cost per unit of time has a more significant impact on the expected profit of the valet charging business than the lower bound of cost per unit time of charging for users. Therefore, when conducting market research in a region, companies should thoroughly investigate and obtain information about the upper bound of cost per unit of time. This parameter can be initially estimated using the service price ratio to the region’s minimum charging time and then adjusted according to the actual situation.
   Valet charging is mainly included in the after-sales services of large new energy vehicle companies. However, given the rapid growth of new energy vehicle users and charging demand in recent years, the development of valet charging mode as a stand-alone business has broad prospects. This paper proposes the following directions for future research on issues related to the valet charging service model: the problem with scheduling optimization and route optimization for charging staff and the problem with optimal pricing for valet charging service.

Key words: electric vehicles, charging-station location problem, valet charging, two-stage stochastic programming, sample average approximation, Epsilon constraint method

摘要： 当新能源汽车用户自行前往充电站充电,将会产生时间成本和等待焦虑。而代客充电服务能够有效解决这些问题,但是其服务成本受到充电站布局与社区用户分布等因素的影响。为探究充电站布局对代客充电服务模式的影响,本文建立基于充电时间的等待焦虑程度量化模型,引入代客充电服务比率量化用户的服务选择行为,同时考虑用户需求的随机性,以最小化充电站建设成本与用户前往充电站充电的行驶成本、最大化代客充电业务利润为优化目标,建立多目标两阶段随机混合整数规划模型,并使用Epsilon约束法与抽样平均近似方法进行求解。本文在对模型及其参数进行数值分析的基础上,以合肥市瑶海区为背景设计案例研究,发现在充电站建设数目一定的前提下,通过改变充电站布局可以提升代客充电业务利润,但同时也会导致用户充电行驶成本的升高。

关键词: 电动汽车, 充电站布局, 代客充电, 随机规划, 抽样平均近似, Epsilon约束法