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
相关论文
共 50 条
  • [1] Planning of electric vehicle charging station based on real time traffic flow
    Yin, Xiaoman
    Zhao, Xiangyang
    2016 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2016,
  • [2] Location Planning of Charging Station for Electric Vehicle Based on Urban Traffic Flow
    Liu Guang
    Zeng Chengbi
    2016 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED), 2016,
  • [3] Vehicle Path Planning Based on Stability and Macroscopic Traffic Flow Model
    Li L.
    Pei Y.-L.
    Yin L.
    Zhou L.
    Pei, Yu-Long (peiyulong@nefu.edu.cn), 1600, Chang'an University (33): : 71 - 80
  • [4] Optimal path planning for electric vehicle travel time based on Dijkstra
    Yang, Jiajia
    Cai, Bin
    Li, Xiaohui
    Ge, Rongxianng
    2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 721 - 726
  • [5] Probabilistic Electric Vehicle Charging Demand Forecast Based on Deep Learning and Machine Theory of Mind
    Hu, Tianyu
    Liu, Kailong
    Ma, Huimin
    2021 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC), 2021, : 795 - 799
  • [6] Electric vehicle path planning and charging navigation strategies considering the impact of traffic accidents
    Huang, Bo
    Hu, Bo
    Xie, Kaigui
    Shao, Changzheng
    Lin, Chengrong
    Huang, Wei
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2024, 52 (19): : 47 - 59
  • [7] Integrated Electric Vehicle Charging Path Planning Considering Traffic Network and Power Grid
    Hao, Jiajie
    Hou, Hui
    Zhang, Yubao
    Wang, Yu
    Cai, Baike
    Liu, Chao
    2022 4TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM (AEEES 2022), 2022, : 379 - 383
  • [8] Electric vehicle charging load prediction and charging station planning considering traffic flow capture
    Sun L.
    Shen C.
    Zhu T.
    Yang G.
    Yang M.
    Sun Y.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2024, 44 (07): : 263 - 270
  • [9] An Electricity Demand-Based Planning of Electric Vehicle Charging Infrastructure
    DAI Yongxia
    LIU Min
    WuhanUniversityJournalofNaturalSciences, 2017, 22 (05) : 449 - 454
  • [10] Spatial and Temporal Model of Electric Vehicle Charging Demand
    Bae, Sungwoo
    Kwasinski, Alexis
    IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (01) : 394 - 403