Ensuring the safe and fast charging of lithium-ion battery (LIB) is a pivotal technology that plays a key role in advancing the wide application of electric vehicles (EVs). Currently, the majority of model-based charging methods are developed for deterministic models, lacking consideration for strategy failure and battery safety issues caused by model or data uncertainty. Learning-based charging methods can address this issue due to their strong adaptability. However, training appropriate strategies requires a mass of iterative interaction. In this article, a model predictive control (MPC)-guided deep reinforcement learning (DRL) charging scheme is proposed to address the control challenges resulting from model uncertainty or additional disturbances. By integrating the advantages of both MPC and DRL, the scheme can not only solve the problem of performance degradation caused by uncertainty in model-based methods, but also reduce the search space of DRL to improve the sample efficiency of learning-based methods. The proposed strategy is compared with state-of-the-art standalone MPC and DRL controllers. Results show that the MPC-based controller inevitably violates constraints, while controllers under DRL framework successfully reduce the voltage violation rate from 34.28% to 0%. Compared to the standalone DRL controller, the proposed strategy converges approximately 60% faster. The average charging time is reduced by 1.96, 2.23, and 0.36 min after 500, 1000, and 1500 training episodes, respectively. Additionally, the proposed strategy ensures a safer training process.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MPC-Guided Deep Reinforcement Learning for Optimal Charging of Lithium-Ion Battery With Uncertainty


    Contributors:
    Zhu, Zhipeng (author) / Dong, Guangzhong (author) / Lou, Yunjiang (author) / Sun, Li (author) / Yu, Jincheng (author) / Wu, Liangcai (author) / Wei, Jingwen (author)


    Publication date :

    2025-02-01


    Size :

    2459702 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    REINFORCEMENT LEARNING FOR CONTINUED LEARNING OF OPTIMAL BATTERY CHARGING

    KLEIN REINHARDT / RAVI NIKHIL / KROENER CHRISTOPH et al. | European Patent Office | 2024

    Free access

    Multiobjective Battery Charging Strategy Based on Deep Reinforcement Learning

    Xiong, Zheng / Luo, Biao / Wang, Bing-Chuan et al. | IEEE | 2024


    Heavy truck battery swap station two-stage battery charging optimization method based on deep reinforcement learning

    ZHANG YU / YIN LIXIN / LIU SHIWEI et al. | European Patent Office | 2024

    Free access

    Deep Reinforcement Learning-Based Local Path Planning with Memory-Guided

    Wang, Xu / Xu, Xiaobin / Lin, Shiyao et al. | Springer Verlag | 2025