This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver’s demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver’s driving style and promote fuel economy.


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    Title :

    Reinforcement Learning-Based Predictive Control for Autonomous Electrified Vehicles


    Contributors:
    Liu, Teng (author) / Yang, Chao (author) / Hu, Chuanzheng (author) / Wang, Hong (author) / Li, Li (author) / Cao, Dongpu (author) / Wang, Fei-Yue (author)


    Publication date :

    2018-06-01


    Size :

    1902881 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    REINFORCEMENT LEARNING-BASED PREDICTIVE CONTROL FOR AUTONOMOUS ELECTRIFIED VEHICLES

    Liu, Teng / Yang, Chao / Hu, Chuanzheng et al. | British Library Conference Proceedings | 2018





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