Abstract: In the process of  vigorous development of new energy vehicles in the country, the charging problem has always hindered the development of electric vehicles, and the planning and construction of charging infrastructure, especially fast charging stations, is particularly important. The key to the large-scale development of electric vehicles（EV） is to establish a complete charging infrastructure based on users’ charging preferences, reduce users’ mileage anxiety, and completely solve the problem of inconvenient charging. Based on the background research of considering all aspects of social factors and determining a certain number of candidate nodes, a dual-objective planning model is proposed. Under the constraints of demand, distance, capacity, the relationship between the total cost of constructing charging stations and charging coverage area are analyzed to find the optimal charging station construction plan, and taking area B of the city  A as an example, the multi-objective particle swarm algorithm is used to solve, find the best node and number of charging stations. Different algorithms are used to solve the problem, and the results are analyzed and compared, which shows that MOPSO has more practical significance in solving bi-objective problems.

Key words: charging station, selection preference, location and capacity, dual objective function, multi-object particle swarm optimization（MOPSO）

摘要： 在国家大力发展新能源汽车的过程中，充电问题一直阻碍着电动汽车的发展，充电基础设施尤其是快速充电站的规划和建设尤为重要。大规模发展电动汽车（electric vehicle，EV）的关键是根据用户的充电选择偏好，建立完善的充电基础设施，减少用户的里程焦虑，彻底解决充电不方便的问题。在考虑了各方面社会因素并确定一定数量的候选节点背景研究的基础上，提出了一种双目标规划模型，在满足需求、距离、容量等约束条件下，分析了建设充电站总成本和充电覆盖范围之间的关系，寻找最优的充电站建设方案，并以A城市B区为例，通过多目标粒子群算法进行求解，求出充电站的最佳节点和数量。用不同算法进行求解，通过对结果进行分析比较，表明多目标粒子群算法（MOPSO）在求解双目标问题时更具有实际意义。

关键词: 充电站, 选择偏好, 选址定容, 双目标函数, 多目标粒子群算法