Life prediction is a major focus for a commercial fuel cell stack, especially applied in fuel cell electric vehicles (FCEV). This paper proposes a data driven fuel cell lifetime prediction model using particle swarm optimized back-propagation neural network (PSO-BPNN). For the prediction model PSO-BP, PSO algorithm is used to determine the optimal hyper parameters of BP neural network. In this paper, total voltage of fuel cell stack is employed to represent the health index of fuel cell. Then the proposed prediction model is validated by the aging data from PEMFC stack in FCEV at the actual road condition. The experimental results indicate that PSO-BP model can predict the voltage degradation of PEMFC stack at actual road condition precisely and has a higher prediction accuracy than BP model.


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    Titel :

    A Data Driven Fuel Cell Life-Prediction Model for a Fuel Cell Electric City Bus


    Weitere Titelangaben:

    Sae Int. J. Adv. and Curr. Prac. in Mobility


    Beteiligte:
    Hao, Dong (Autor:in) / Zheng, Lu (Autor:in) / Li, Wenqi (Autor:in) / Hou, Yongping (Autor:in)

    Kongress:

    SAE WCX Digital Summit ; 2021



    Erscheinungsdatum :

    2021-04-06


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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