Shu Su, Hang Zhao, Hongzhi Zhang, Xiangning Lin, Feipeng Yang, Zhengtian Li
{"title":"Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning","authors":"Shu Su, Hang Zhao, Hongzhi Zhang, Xiangning Lin, Feipeng Yang, Zhengtian Li","doi":"10.1109/ISAP.2017.8071382","DOIUrl":null,"url":null,"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.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAP.2017.8071382","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.