This paper presents the design and application of a model predictive control-based energy management for a fuel cell hybrid electric vehicle. To estimate the upcoming vehicle speed within each receding horizon, a speed-forecast method is proposed using the layer recurrent neural network (LRNN). Then, the power-allocating decisions are derived via minimizing the multicriteria cost function by considering the predicted speed sequence. It has been verified that the LRNN predictor has a higher accuracy versus the benchmark methods. Software-in-the-Loop testing results have indicated that the proposed control strategy can improve fuel economy and fuel cell durability versus a rule-based benchmark, with an acceptable online computational burden.


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    Titel :

    Real-time Predictive Energy Management for Fuel Cell Electric Vehicles


    Beteiligte:
    Zhou, Yang (Autor:in) / Ravey, Alexandre (Autor:in) / Pera, Marie-Cecile (Autor:in)


    Erscheinungsdatum :

    21.06.2021


    Format / Umfang :

    6628521 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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