Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. These power sources have conventionally been controlled by a rule-based algorithm or optimization-based control. Besides these conventional approaches, reinforcement learning-based control algorithms have actively been studied recently. To investigate the benefits of the reinforcement learning-based approach, a model-free control algorithm for an HEV is proposed in this article using a Twin Delayed Deep Deterministic policy gradient (TD3), which is an online, off-policy Deep Reinforcement Learning (DRL) algorithm. The effectiveness of the proposed algorithm is studied by applying the TD3 algorithm to a 48V mild HEV (MHEV) model and the optimal operating strategy is obtained for maximum fuel economy. The simulation results show that the proposed TD3-based algorithm improves the average fuel economy by 1.89% on standard driving cycles and 2.20% on real-world driving cycles when compared to the Deep Deterministic Policy Gradient (DDPG) algorithm.


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    Titel :

    Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning


    Weitere Titelangaben:

    Sae Int. J. Elec. Veh


    Beteiligte:
    Yoon, Hwan-Sik (Autor:in) / Yao, Zhengyu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    27.10.2021


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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