Electric vehicles are a sustainable substitution to conventional vehicles. This study introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

电动汽车是传统汽车的可持续替代。这项研究为城市快速充电基础设施引入了一个综合框架，以解决距离焦虑的问题。开发了介观仿真工具来生成行程轨迹，并基于各种行程属性来仿真充电行为。由此产生的充电需求是寻求充电站配置的混合整数非线性程序的关键输入。该模型将包括充电站和充电器安装成本以及充电，排队和绕行延迟在内的总系统成本降至最低。除了黄金分割法外，还使用分解技术解决了该问题，该技术结合了用于小型网络的商用求解器和用于大型网络的启发式算法。与隐式枚举方法相比，可以确定分解方法的解决方案质量和显着优势。此外，针对未来的市场份额和技术，探索了支持城市旅行所需的基础设施。