多信息融合下电动汽车充电路径规划

由于电动汽车用户难以找到充电时间与充电地点之间的平衡点,不能准确把握何时何地进行充电行为。文中首先选取能够准确反映实际道路中用户独特驾驶特性的行驶工况特征参数,采用工况识别法构建电动汽车在实时动态路况下的剩余电量估算模型,判断其出行过程中何时有充电需求;当有充电需求时,通过预测充电站的抵达车辆数,建立电动汽车排队等待时间模型,为用户规划有效充电时段,作为选择充电地点的依据;考虑到充电时间与充电地点的耦合关系,从用户角度出发,在电池剩余电量的约束下,构建用户出行距离、出行时间及充电成本三者权值之和最优为目标的电动汽车充电路径模型,并将其应用于实际交通路网区域中,采用蚁群算法对其进行仿真验证。

Electric vehicle charging path planning under multi-information fusion

Since electric vehicle users have difficulty finding a balance between charging time and charging location, it is impossible to accurately grasp when and where to perform charging behavior. This paper first selects the driving condition characteristic pa-rameters that can accurately reflect the unique driving characteristics of users in the actual road, and uses the working condition identification method to construct the remaining electricity estimation model of electric vehicles under real-time dynamic road conditions to judge when there is charging demand in the travel process. When there is charging demand, the electric vehicle waiting time model is established by predicting the number of arriving vehicles of the charging station, and the effective charging period is planned for the user as the basis for selecting the charging location; considering the coupling relationship between the charging time and the charging location, From the user"s point of view, under the constraint of the remaining battery power, the electric vehicle charging path model with the best combination of the user"s travel distance, travel time and charging cost is con-structed and applied to the actual traffic network in Beijing. In the region, the ant colony algorithm is used to verify the simulation.