多因素下基于路网拓扑的电动汽车充电路径规划策略

Electric vehicles charging path planning method based on road network under multi parameters

摘要:

随着电动汽车产业的发展,电动汽车的充电需求也日益增加.为了满足电动汽车用户充电多样性需求并提高充电设施利用率,本文在考虑出行距离、充电电价以及充电站排队情况等三种影响因素下构建混合整数线性规划模型,提出了一种多因素下基于充电站路网拓扑结构的电动汽车充电路径规划方法,为用户规划充电路径与充电站选择.首先,该方法在能耗约束的前提下基于Dijkstra最短路径算法进行充电引导,为求解多目标最优引入信息熵的概念来确定各参数影响权重.其次，针对用户充电需求的差异性问题,提出了三种不同目标下的规划方法以降低用户充电成本.此外,本文构建了站点随机充电服务排队模型并进行敏感性分析以研究充电站服务能力对充电成本的影响.以某地区路网为算例进行仿真,结果表明本文提出的方法能够有效降低用户充电出行成本并合理规划出行路径,验证了所提模型的可行性和有效性,对充电选择和站点配置具有一定的决策参考意义.

Abstract:

With the development of the electric vehicle industry, the charging demand for electric vehicles is also increasing. To meet the diverse charging demand of electric vehicle users and improve the utilization rate of charging facilities, this paper proposes a mixed integer linear programming model considering three influencing parameters: travel distance, charging price and charging station queuing. It then presents a multi-factor electric vehicle charging path planning method based on the topology of road network to plan the charging path and station selection for users. Firstly, we uses Dijkstra algorithm for charging guidance under the premise of energy consumption constraint, and introduces the concept of information entropy to determine the weight of each parameter for solving the multi-objective optimization. Secondly, to address the variability of users′ charging demand, three planning methods with different objectives are proposed to reduce users′ charging costs. Additionally, this paper constructs a stochastic charging service queue model and conducts sensitivity analysis to investigate the impact of station service capacity on charging cost. Finally, the proposed method is applied to an actual road network for simulation, the results demonstrate that the proposed method can effectively reduce the users′ charging travel cost and reasonably plan the travel path and has certain decision-making significance for charging selection and station configuration.