This study addresses the location problem of electric vehicle charging stations considering drivers’ range anxiety and path deviation. The problem is to determine the optimal locations of EV charging stations in a network under a limited budget that minimize the accumulated range anxiety of concerned travelers over the entire trips. A compact mixed-integer nonlinear programming model is first developed for the problem without resorting to the path and detailed charging pattern pre-generation. After examining the convexity of the model, we propose an efficient outer-approximation method to obtain the ε-optimal solution to the model. The model is then extended to incorporate the charging impedance, e.g., the charging time and cost. Numerical experiments in a 25-node benchmark network and a real-life Texas highway network demonstrate the efficacy of the proposed models and solution method and analyze the impact of the battery capacity, path deviation tolerance, budget and the subset of OD pairs on the optimal solution and the performance of the system.

这项研究考虑了驾驶员的范围焦虑和路径偏差，解决了电动汽车充电站的位置问题。问题是要在有限的预算下确定网络中EV充电站的最佳位置，以使有关旅行者在整个行程中的累积范围焦虑最小化。首先针对该问题开发了紧凑的混合整数非线性规划模型，而无需求助于路径和详细的充电模式预生成。在检查了模型的凸性之后，我们提出了一种有效的外部逼近方法来获得模型的ε最优解。然后扩展模型以合并充电阻抗，例如充电时间和成本。