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Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

机译:基于自适应粒子群算法的电动出租车充电指导

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摘要

Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly.
机译:电动出租车在电动汽车的应用中起着重要作用。分析了中国深圳电动出租车的实际运行数据,并针对充电站设备的时间不平衡,提出了基于充电站信息和车辆信息的电动出租车充电指导系统。建立了电动出租车收费指导模型,并通过自适应突变粒子群优化算法根据出租车和充电站的位置来指导充电。仿真基于深圳充电站的实际数据,结果表明,在充电指导后,电动出租车可以根据充电站中的充电桩数量均匀地分配到合适的充电站。该区域充电站之间将实现均匀分布,提高充电设备的利用率,从而证明了该充电指导方法是可行的。充电设备利用率的提高可以大大节省公共充电基础设施的资源。

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