Electric vehicle entire-trip navigation and charging reservation method based on a high-speed communication network

被引:8
|
作者
Huang, Yulong [1 ,3 ]
Liu, Mingbo [2 ]
Zhang, Yongjun [2 ]
机构
[1] Jinan Univ, Int Energy Coll, Zhuhai 519070, Peoples R China
[2] South China Univ Technol, Sch Elect Power Engn, Guangzhou 510640, Peoples R China
[3] Adm Bldg 620,206 Qianshan St, Zhuhai 519070, Peoples R China
关键词
Electric vehicles; Entire -trip charging navigation; High-speed communication network; EV-based decentralized optimization; STRATEGY; TRANSPORTATION; COORDINATION; SYSTEM;
D O I
10.1016/j.ijepes.2023.109070
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
Having a large number of electric vehicles (EVs) on the road will lead to vehicle congestion, long waiting times at charging stations (CSs), and even the inability to find an available charging position. Main technical challenges in mobile EVs charging may include charging information exchanges between EVs and CSs, EV traveling route and CS selection, and EV charging planning. To solve the above problems, this study provides an optimization so-lution for entire-trip charging and navigation of EVs, which balances charging resources, protects the travel time of EV drivers, and prioritizes renewable energy. The communication scheme of an EV charging network is designed, including real-time communication between the CSs, EV drivers, road condition monitoring center, power grid company, and the charging control center distributed in every parking lot. CSs in each road segment sort vehicles arriving at stations, allocate charging time slots, provide charging start times, and adjust those times for EVs with reservations that have not yet arrived. EV drivers can plan vehicle charging on their smart devices. The optimization of electric navigation can reduce the travel time or cost for drivers, ensure safe electricity usage, calculate vehicle routes, select CSs, and provide charging time intervals. The effectiveness of the method is verified on a regional road network in China, and the results show that it can balance the charging resource distribution of a CS, guarantee EV state of charge level, greatly reduce travel cost or travel time, provide precise waiting time, and prioritize renewable energy.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Research on Integrated Navigation Method of High-speed Spinning Flying Body Based on EKF
    Dong Yiping
    Liu Ning
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 3739 - 3744
  • [22] Research on Integrated Navigation Method of High-speed Spinning Flying Body Based on EKF
    Yiping, Dong
    Ning, Liu
    Chinese Control Conference, CCC, 2021, 2021-July : 3739 - 3744
  • [23] A Novel Newton Raphson-Based Method for Integrating Electric Vehicle Charging Stations to Distribution Network
    Nurmuhammed, Mustafa
    Akdag, Ozan
    Karadag, Teoman
    ELECTRICA, 2023, 23 (02): : 310 - 317
  • [24] Research on electric vehicle charging load prediction method based on spectral clustering and deep learning network
    Fang, Xin
    Xie, Yang
    Wang, Beibei
    Xu, Ruilin
    Mei, Fei
    Zheng, Jianyong
    FRONTIERS IN ENERGY RESEARCH, 2024, 12
  • [25] Nonlinear equalizer based on neural network in high-speed optical fiber communication systems
    Zhao, Han-qi
    Li, Na
    Wu, Bin
    Wu, Gui-long
    Chen, Yi-tong
    Feng, Xiao-fang
    He, Pei-li
    Li, Wei
    CHINESE OPTICS, 2025, 18 (01)
  • [26] Track Geometry State Assessment Method for High-speed Railway Based on Vehicle Response
    Ma S.
    Liu X.
    Zhang Y.
    Chen Z.
    Zhao D.
    Tiedao Xuebao/Journal of the China Railway Society, 2023, 45 (11): : 117 - 127
  • [27] Method of sharing virtual traditional crafting system based on high-speed network
    Ishida, Tomojwki
    Miyakawa, Akihiro
    Ohhashi, Yuji
    Shibata, Yoshitaka
    20TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS, VOL 2, PROCEEDINGS, 2006, : 665 - +
  • [28] An Identification Method of High-speed Railway Sign Based on Convolutional Neural Network
    Meng L.
    Sun X.-Y.
    Zhao B.
    Li N.
    1600, Science Press (46): : 518 - 530
  • [29] Neural Network-based Symbol Detection in High-speed OFDM Underwater Acoustic Communication
    Chen, Zhipeng
    He, Zhiqiang
    Niu, Kai
    Rong, Yue
    2018 10TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), 2018,
  • [30] A Hardware-in-the-loop Simulation Method of the Network Performance of High-speed Railway Mobile Communication System
    Lin, Kaifeng
    Zhong, Zhangdui
    Xiong, Lei
    Lin, Siyu
    2011 6TH INTERNATIONAL ICST CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM), 2011, : 1221 - 1225