Electrified vehicles are important for reducing emissions and will dominate road transportation in the future. An energy management strategy (EMS) is critical for hybrid electric vehicles (HEVs) to manage the use of on-board energy resources. This article proposes a novel double-layer EMS based on hierarchical rewarding deep deterministic policy gradient (HR-DDPG) for a heavy-duty HEV to improve energy efficiency adaptively to various scenarios. The proposed hierarchical rewarding structure with two reward functions can guide the DDPG agent to explore the optimal policy more efficiently and deeply and is well-suited to achieve targeted adjustment according to the vehicle operating modes to cope with rapidly changing scenarios. The optimality and adaptability of the new strategy are evaluated by standard and real-world driving data. The results show that the proposed strategy leads to an improvement of 8.11% in energy efficiency compared with the conventional DDPG strategy, and its energy-saving performance can reach up to 93.87% of the capacity of a dynamic programming (DP)-based EMS on the comprehensive driving condition II.


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    Title :

    Hierarchical Rewarding Deep Deterministic Policy Gradient Strategy for Energy Management of Hybrid Electric Vehicles


    Contributors:
    Wang, Jinhai (author) / Du, Changqing (author) / Yan, Fuwu (author) / Zhou, Quan (author) / Xu, Hongming (author)


    Publication date :

    2024-03-01


    Size :

    8390601 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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