电动汽车充电引导策略研究

Research on electric vehicle charging guidance strategy

随着大规模电动汽车的普及,产生一系列诸如:行驶里程限制、电网负荷过大和充电排队拥挤等问题。为了解决这些问题,大量学者对充电引导进行研究。近年来该领域所侧重的研究方向将电动汽车充电引导策略分为用户最优和系统最优两个策略进行分类阐述。通过分析对比两种引导策略可以得出,用户最优更偏向于充电行为的引导,系统最优更偏向于充电行为的调度。根据城市出行现状,认为用户最优的研究更符合现在的市场需求,系统最优的研究则更符合未来城市大规模并发充电综合调度的情形。通过探讨大量文献所提供不同角度和方法的充电引导策略,可以发现现有研究的数据利用率和协调调度的智能化程度较低。在人工智能时代,面对智慧城市和智慧出行的要求,提出一种智能化充电引导的框架,充分分析用户在充电业务逻辑中的行为,结合大数据、云计算和人工智能技术,为电动汽车充电引导的效率和智能化水平做进一步提升。

With the popularity of electric vehicles,a series of problems such as limitation of driving distance,excessive load on power grid and crowded charging queue have emerged.In order to solve these problems,a large number of scholars have studied charging guidance.The research directions focused on in recent years are summarized in this paper.The electric vehicle charging guidance strategy is divided into two major types of user optimization and system optimization.By analyzing and comparing the two guidance strategies,it can be seen that the user optimization is more biased towards the guidance of charging behavior,and the system optimization is more biased towards the scheduling of charging behavior.According to the current travel situation of urban people,it is believed that the user optimization research is more in line with the current market demand,and the system optimization research is more in line with the situation of large⁃scale concurrent charging and comprehensive dispatching in the future cities.By discussing the charging guidance strategies in different angles and methods provided by a large number of literatures,it can be found that the data utilization and intelligent coordination scheduling of the existing research are low.In the era of artificial intelligence,as for the requirements of smart city and smart travel,a framework of intelligent charging guidance is proposed in this paper,and the user′s behaviors in the charging business logic are fully analyzed,in which big data,cloud computing and artificial intelligence technology are combined.Anyway,the efficiency and intelligence level of electric vehicle charging guidance have been improved further because of this research.