As a new type of vehicle power system with zero emission and renewable energy, fuel cell has high research value. In this paper, a fuel cell energy management strategy based on deep reinforcement learning is proposed for high-speed traffic scenarios. A bus working condition with real high-speed traffic scenarios is obtained through NGSIM's real high-speed road segment data set screening. The energy management strategy effectively reduces the hydrogen consumption by 4.21% and the power fluctuation by 17.2%, which effectively improves the durability of fuel cells.


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    Titel :

    Fuel cell bus energy management based on deep reinforcement learning in NGSIM high-speed traffic scenario


    Beteiligte:
    Shen, Yang (Autor:in) / Yi, Fengyan (Autor:in) / Fan, Yi (Autor:in) / Chen, Weiqi (Autor:in) / Zhou, Jiaming (Autor:in) / Peng, Jiankun (Autor:in)


    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    4827589 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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