To develop an energy management strategy (EMS) that adapts to the state-of-health (SOH) of the fuel cell stack (FCS) to keep the output of the powertrain of fuel cell hybrid electric vehicles (FCHEVs) optimal all the time, a degradation adaptive EMS is proposed. The proposed EMS is based on a hierarchical framework, where the upper layer involves a degradation rate model to estimate the SOH of the FCS online and acquire performance characteristics at different SOH levels. In the lower layer, an EMS using the Rule-DDPG algorithm is developed. The results prove that the proposed method outperforms existing mainstream EMSs and aligns closely with the dynamic programming approach regarding economic and durability indicators. Compared to nonadaptive EMSs across various driving cycles, the proposed degradation adaptive EMS can consistently achieve the lowest energy consumption and optimal durability performance, tailored to the current SOH of the powertrain. In addition, a comparison between offline simulation and hard-in-the-loop testing validates the computational efficiency and real-time applicability of the proposed strategy in practical environments. Specifically, the calculation time for each sample during the WLTC cycle is shown to be less than 213.214 ms, with an average calculation time of 168.637 ms and a minimum calculation time of 150.618 ms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Degradation Adaptive Energy Management Strategy for FCHEV Based on the Rule-DDPG Method: Tailored to the Current SOH of the Powertrain


    Contributors:
    Tang, Xingwang (author) / Shi, Lei (author) / Zhang, Yujia (author) / Li, Bingbing (author) / Xu, Sichuan (author) / Song, Ziyou (author)


    Publication date :

    2025-02-01


    Size :

    6096291 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    FCHEV energy management control method based on multi-target dynamic programming neural network

    LIANG BOWEN / GAO YUAN / LIU JING et al. | European Patent Office | 2023

    Free access

    Impact of Hilly Road Profile on Optimal Energy Management Strategy for FCHEV with Various Battery Sizes

    Han, J. / Park, Y. / Kum, D. et al. | British Library Conference Proceedings | 2013



    Autopilot Strategy Based on Improved DDPG Algorithm

    Zuo, Xiaochao / Li, Xiaoning / Tian, Zhewen | SAE | 2019