Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning

Shu Su, Hang Zhao, Hongzhi Zhang, Xiangning Lin, Feipeng Yang, Zhengtian Li
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引用次数: 14

Abstract

With the popularization of intelligent navigation system on electric vehicles, it's possible to obtain real-time distribution of electric vehicles in a given region. Based on traffic flow model and M/M/s queuing theory, this paper presents a mathematical model for the prediction of charging load at charging station. To get the charging distribution generated in the driving process, an optimal path planning model based on the Dijkstra algorithm is proposed. Besides, for the sake of formulating the dynamic spatial charging demand distribution map of the traffic network region, the Monte Carlo sampling method is adopted. The simulation results demonstrate the effectiveness of the proposed models in analyzing the charging demand distribution.
基于交通流模型和最优路径规划的电动汽车充电需求预测
随着智能导航系统在电动汽车上的普及,可以实时获取给定区域内电动汽车的分布情况。基于交通流模型和M/M/s排队理论,建立了充电站充电负荷预测的数学模型。为了得到行驶过程中产生的充电分布,提出了一种基于Dijkstra算法的最优路径规划模型。此外,为了制定交通网络区域的动态空间收费需求分布图,采用蒙特卡罗采样方法。仿真结果验证了所提模型在分析充电需求分布方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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