基于自适应变异粒子群算法的电动出租车充电引导

发布时间：2018-08-23 12:01

【摘要】：通过调研分析深圳市电动出租车的实际运营数据,提出充电站充电设备时间利用率的计算方法。针对区域内充电站之间设备时间利用率分布不均衡的问题,提出了基于充电站信息和车辆信息的电动出租车充电引导系统,建立了充电引导模型,并采用改进的自适应变异粒子群算法引导电动出租车的充电行为。根据深圳充电站的实际数据进行算例仿真,仿真结果表明,经过充电引导后的电动出租车能根据充电站内充电桩的规模均匀分布到相应的充电站,实现区域内充电站之间充电设备利用率的均衡分布,从而提高充电设备时间利用率。仿真结果验证了所提充电引导方法的可行性。
[Abstract]:Based on the investigation and analysis of the actual operation data of electric taxis in Shenzhen, the method of calculating the time utilization ratio of charging equipment in charging station is put forward. In order to solve the problem of uneven distribution of equipment time utilization among charging stations in the region, a charging and guiding system for electric taxis based on charging station information and vehicle information is proposed, and a charging guidance model is established. An improved adaptive mutation particle swarm optimization algorithm is used to guide the charging behavior of electric taxis. According to the actual data of the charging station in Shenzhen, the simulation results show that the electric taxi after charging guidance can distribute evenly to the corresponding charging station according to the scale of charging pile in the charging station. The balanced distribution of charge equipment utilization ratio among charging stations is realized, and the time utilization ratio of charging equipment is improved. The simulation results verify the feasibility of the proposed charging guidance method.
【作者单位】：国家能源主动配电网技术研发中心(北京交通大学);
【基金】：北京高等学校青年英才计划项目(YETP0570)~~
【分类号】：U469.72;TM910.6

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