The penetration rate of electronic vehicles (EVs) has been increasing rapidly in recent years, and the deployment of EV infrastructure has become an increasingly important topic in some solutions of the Internet of Things (IoT). A reasonable balance needs to be struck between the user experience and the deployment cost of charging stations and the number of charging piles. The deployment of EV’s charging station is a challenging problem due to the uneven distribution and mobility of EV. Fortunately, EVs move with a certain regularity in the urban environment. It makes the deployment strategy design of EV charging stations feasible. Therefore, we proposed a deployment strategy of EV charging station based on particle swarm optimization algorithm to determine the charging station localization and number of charging piles. This strategy is designed based on the nonuniform distribution of EV in a city scene map, at the same time, the distribution of EV at different times, which makes the strategy more reasonable. Extensive simulation results further demonstrated that the proposed strategy can significantly outperform the K-means algorithm in the urban environment.

中文翻译：

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电动汽车充电站定位与充电桩密度优化策略研究

近年来，电动汽车（EV）的普及率一直在快速增长，并且在某些物联网（IoT）解决方案中，电动汽车基础设施的部署已成为越来越重要的主题。需要在用户体验和充电站的部署成本以及充电桩数量之间取得合理的平衡。由于电动汽车的不均匀分布和机动性，电动汽车充电站的部署是一个具有挑战性的问题。幸运的是，电动汽车在城市环境中的行驶具有一定规律性。它使电动汽车充电站的部署策略设计可行。因此，我们提出了一种基于粒子群优化算法的电动汽车充电站部署策略，以确定充电站的位置和充电桩数量。该策略是基于城市场景图中EV的不均匀分布而设计的，同时基于EV在不同时间的分布，使得该策略更加合理。大量的仿真结果进一步表明，所提出的策略在城市环境中可以明显优于K-means算法。