Forecast of Electric Vehicle Charging Demand Based on Traffic Flow Model and Optimal Path Planning

被引:0
|
作者
Su, Shu [1 ]
Zhao, Hang [1 ]
Zhang, Hongzhi [1 ]
Lin, Xiangning [1 ]
Yang, Feipeng [1 ]
Li, Zhengtian [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Adv Elect Engn & Technol, Wuhan, Hubei, Peoples R China
来源
2017 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM APPLICATION TO POWER SYSTEMS (ISAP) | 2017年
基金
中国国家自然科学基金;
关键词
electric vehicles; charging station; charging demand; traffic flow; path planning; spatial and temopral distribution;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:6
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