Aiming at the range anxiety caused by battery capacity limitations on electric vehicles (EVs), a set of mutual assistance trip system was established, which used the mutual assistance information on EV drivers as a supplement to decision-making. The charging navigation and route selection optimization was carried out significantly, improving driver charging experience. The optimization model of the central control terminal aims at minimizing the total travel time costs of drivers, integrating real-time dynamic road conditions information, charging service information and EV mutual assistance information. It utilizes information entropy theory to quantify mutual assistance information on mutual assistance information risk factor θi, thus adjusts the weight of travel costs. The model is solved by the genetic algorithm based on the priority coding method. The results of the calculation example show that the mutual assistance trip system can significantly reduce the total travel costs of EV drivers. At the same time, the increase in mutual assistance information makes θi larger, which means the more reliable the corresponding travel cost ti, the greater the impact on the total time costs.


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    Titel :

    Route Planning and Charging Navigation Strategy for Electric Vehicles Under the Mutual Assistance Trip System


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Zeng, Zhaohui (Autor:in) / Wang, Jiangfeng (Autor:in) / Luo, Dongyu (Autor:in) / Yang, Guojun (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021



    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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