This article proposes a novel energy management strategy (EMS) for a fuel cell electric vehicle (FCEV). The strategy combines the offline optimization and online algorithms to guarantee optimal control, real-time performance, and better robustness in an unknown route. In particular, dynamic programming (DP) is applied in a database with multiple driving cycles to extract the theoretically optimal power split between the battery and fuel cell with a priori knowledge of the driving conditions. The analysis of the obtained results is then used to extract the rules to embed them in a real-time capable fuzzy controller. In this sense, at the expense of certain calibration effort in the offline phase with the DP results, the proposed strategy allows on-board applicability with suboptimal results. The proposed strategy has been tested in several actual driving cycles, and the results show energy savings between 8.48% and 10.71% in comparison to rule-based strategy and energy penalties between 1.04% and 3.37% when compared with the theoretical optimum obtained by DP. In addition, a sensitivity analysis shows that the proposed strategy can be adapted to different vehicle configurations. As the battery capacity increases, the performance can be further improved by 0.15% and 1.66% in conservative and aggressive driving styles, respectively.


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    Title :

    Real-Time Energy Management Strategy of a Fuel Cell Electric Vehicle With Global Optimal Learning


    Contributors:
    Hou, Shengyan (author) / Yin, Hai (author) / Pla, Benjamin (author) / Gao, Jinwu (author) / Chen, Hong (author)


    Publication date :

    2023-12-01


    Size :

    3520488 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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