Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted. The results show that the D4QN algorithm can achieve both the approximate global optimal results, which differs from the result of dynamic programming by 0.05% under the training cycle, and establish the positive mapping relationship between state variables and action variables of the hybrid powertrain with excellent generalization ability, which can be applied to HCU and control the hybrid electric vehicle effectively in real time. Compared with the original rule-based energy management strategy for the hybrid electric vehicle, the fuel economy under WLTC cycle is improved by 9.64% after the application of D4QN energy management strategy. The proposed strategy can realize a termination of SOC (State of charge) within 50±10% under different initial SOC states for a battery of 1.8kWh, achieving the goal of SOC robustness. In addition, the proposed strategy has strong adaptability to the unknown cycles; the fuel economy of the vehicle after the application of D4QN energy management strategy in the untrained NEDC and CLTC-P cycles is improved by more than 10% compared to the WLTC cycle.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Energy Management Based on D4QN Reinforcement Learning for a Series-Parallel Multi-Speed Hybrid Electric Vehicle


    Additional title:

    Sae Technical Papers


    Contributors:
    Zhuang, Caihua (author) / Lou, Diming (author) / Fang, Liang (author) / Zhao, Yinghua (author)

    Conference:

    SAE 2023 Vehicle Powertrain Diversification Technology Forum ; 2023



    Publication date :

    2023-10-30




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





    Hybrid electric vehicle energy management method based on reinforcement learning

    LOU DIMING / ZHAO YINGHUA | European Patent Office | 2021

    Free access

    Hybrid electric vehicle adaptive energy management framework based on model reinforcement learning

    SHI CUIDUO / ZHAO KEGANG / LIANG ZHIHAO | European Patent Office | 2023

    Free access