Abstract: Although the sales market of electric vehicles has been expanded in recent years, the low utilization rate of electric vehicle charging stations makes the revenue of electric vehicle charging service operators not optimistic. The big data analysis of the charging station deployment method can effectively improve the utilization of charging piles. In this paper, the principle of the main algorithm of evolutionary computation and swarm intelligence is elaborated. The multi-objective optimization mathematical model of charging station planning is studied. Then, the application of evolutionary computing and swarm intelligence in charging station planning is discussed. The improvement method of evolutionary computing and swarm intelligence is studied in charging station planning. And the possibilities of the applications of artificial neural network and fuzzy system in charging station planning are discussed. Finally, the development status and future trends are summarized.

Key words: electric vehicle, charging station planning, intelligence computing, multi-objective optimization

摘要： 虽然近年来电动汽车销售市场在不断扩大，但过低的充电桩利用率使得电动汽车充换电服务运营商的收益并不乐观。通过大数据分析充电站的部署方式可以有效提升充电桩利用率。阐述了演化计算和群体智能主要算法的原理，研究了充电站规划的多目标优化数学模型，论述了演化计算和群体智能在充电站规划中的应用，研究了演化计算和群体智能在充电站规划过程中的改进方式，讨论了人工神经网络和模糊系统应用于充电站规划的可能性，并对发展现状和未来趋势进行了总结与展望。

关键词: 电动汽车, 充电站规划, 计算智能, 多目标优化