随着电动汽车增多，公共充电网络日趋繁忙，充电站车辆到达率大于服务率的过载现象时有发生，充电排队等待时间显著增长。为提高网络服务质量(quality of service, QoS)、节约充电等待时间，文中提出考虑动态充电需求和过载的公共充电网络规划方法，建立了网络规划混合整数多目标优化问题模型，用非支配排序遗传算法生成最优网络规划方案；设计了容忍过载的充电网络服务质量分析方法，基于变分不等式、元胞传输模型和蒙特卡洛法求充电等待时间。在通用性能测试数据集PEMS-BAY上的仿真结果表明，在典型场景中，文中方法比已有同类方法可显著提高网络服务质量，节约平均等待时间27.6%，节约最长等待时间29.5%,在过载更严重、充电更扎堆的场景中，性能优势更显著。
英文摘要:
With the rapid growth of electric vehicles, public charging network becomes increasingly busy and suffers more from overloaded (arrival rates exceed service rates) charging nodes, resulting in excessively long queuing waiting time. To improve network quality of service (QoS) and save charging waiting time, this paper proposed a planning method for electric vehicle public charging network with dynamic demand and overloaded charging nodes, formulated mixed integer multi-objective optimization problem of network planning to generate the optimal planning schemes by a non-dominated sorting genetic algorithm, designed an overload-tolerant QoS analysis method based on variational inequality, cell transmission model, and Monte Carlo method to find charging waiting time. Simulation results on PEMS-BAY dataset indicate that, in typical scenarios, the proposed method significantly improves QoS compared with previous methods, saves 27.6% average waiting time and 29.5% longest waiting time, and performs even better in scenarios with heavily overloaded charging nodes.