This study aims to seek the optimal deployment of fast-charging stations concerning the traffic flow equilibrium and various realistic considerations to promote Electric Vehicles (EVs) widespread adoption. A bi-level optimization framework has been developed in which the upper level aims to minimize the total system cost (i.e., capital cost, travel cost, and environmental cost). Meanwhile, the lower level captures travellers’ routing behaviours with stochastic demands and driving range limitation. A meta-heuristic approach has been proposed, combining the Cross-Entropy Method and the Method of Successive Average to solve the problem. Finally, numerical studies are conducted to demonstrate the proposed framework’s performance and provide insights into the impact of uncertain driving range and charging congestion on the planning decision and the system performance. Generally, both on-route congestion and charging congestion tend to be more serious when there are more EVs in the network; however, the system performance can be improved by increasing EVs’ driving range limitation and providing appropriate charging infrastructure.

本研究旨在寻求关于交通流量平衡和各种现实考虑的快速充电站的最佳部署，以促进电动汽车 (EV) 的广泛采用。已经开发了一个双层优化框架，其中上层旨在最小化总系统成本（即资本成本、旅行成本和环境成本）。同时，较低级别捕获具有随机需求和行驶里程限制的旅行者的路线行为。提出了一种元启发式方法，结合交叉熵法和连续平均法来解决该问题。最后，进行数值研究以证明所提出框架的性能，并深入了解不确定的行驶里程和充电拥堵对规划决策和系统性能的影响。一般来说，当网络中有更多的电动汽车时，路上拥堵和充电拥堵往往更严重；然而，系统性能可以通过增加电动汽车的行驶里程限制和提供适当的充电基础设施来提高。