考虑多车交互影响的电动汽车与快充站动态响应

Dynamic Response of Electric Vehicle and Fast Charging Stations Considering Multi-vehicle Interaction

针对现有电动汽车快速充电地点选择中难以考虑多车辆的交互影响,以及站–车互动中未能实现考虑多站间博弈的服务能力均衡等问题,该文提出一种计及多车交互影响的电动汽车与快充站动态响应策略。首先,提出站–车互动的充电引导框架,阐述了车、站、路、云平台之间的信息流动关系;其次,以路段传输模型为基础,计及动态路段行程时间的影响,建立了城市路网动态路径选择模型,并以满足车主在充电选择上的多元化需求为目标,建立充电导航模型;再次,为准确分析车辆之间的交互影响过程,提出考虑电动汽车动态演化的充电决策方法,反映了车主在行驶过程中不同位置的选站概率;在此基础上,将电动汽车充电选择与充电站动态服务费制定相结合,建立多主体主从博弈模型,分析了相互之间的决策影响;最后,利用算例分析了路径选择、动态充电决策和站–车互动过程。结果表明,该方法可提高电动汽车充电寻站的效率,并均衡了站间的充电负荷。

A dynamic response method for electric vehicles(EVs) and fast charging stations(FCSs) with multi-vehicle interaction effects was presented to solve the problems of multi-vehicle interaction in the selection of EV fast charging locations and the equalizing the service capability by multi-station game in the station-EVs interaction. First, the station-EVs interaction charging guide frame was proposed to describe the information flow relationship between EVs, FCSs, roads and cloud platforms. Then, based on the road segment transmission model, and taking into account the influence of dynamic link travel time, a dynamic path selection model for urban road network was established. A charging navigation model was established to meet the diversified needs of EV owners in charging options. Further, a charging decision method considering the dynamic evolution of EVs was proposed to accurately analyze the interaction process between vehicles, which reflects the probability of selecting stations at different positions during the driving process. On this basis, combining the charging choice of EVs with the dynamic service fee of CSs, a multi-agent stackelberg game model was established to analyze the influence of decision among multi-agent. Finally, a case study was used to analyze the path selection, dynamic charging decision and the station-EVs interaction strategy. The results show that the proposed method can improve the efficiency of EVCS and balance the charging load between CSs.