As an effective reinforcement learning (RL) algorithm, Q-learning has been applied to energy management strategy of hybrid electric vehicle (HEV) in recent years. In the existing literatures, the values of three hyperparameters based on Q-learning are all given in advance, which are respectively exploratory rate ε, discount factor γ and learning rate α. However, different values of hyperparameters will influence on fuel economy of the vehicle and offline computation speed. In this paper, it is proposed that the method of optimization on hyperparameters adapting to driving cycle. Firstly, the mathematical model between three hyperparameters and iteration times is established based on inherent regularity of hyperparameters influencing on vehicle performance respectively. Secondly, it is determined that the optimal changing index k_index of iteration number based on Q-learning corresponding to typical driving cycles. Finally, the simulation model of Yubei District in Chongqing is constructed based on the method of Kullback-Leibler (KL) driving cycle identification. The simulation results indicate that equivalent fuel consumption of the proposed strategy is reduced by 0.4 % and the offline operation time is reduced by 6 s. It can be concluded that the proposed strategy can not only improve fuel economy of the vehicle, but also accelerate the computation speed.


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    Title :

    Hyperparameters of Q-Learning Algorithm Adapting to the Driving Cycle Based on KL Driving Cycle Recognition


    Additional title:

    Int.J Automot. Technol.


    Contributors:
    Yin, Yanli (author) / Huang, Xuejiang (author) / Pan, Xiaoliang (author) / Zhan, Sen (author) / Ma, Yongjuan (author) / Zhang, Xinxin (author)

    Published in:

    Publication date :

    2022-08-01


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Driving cycle recognition for hybrid electric vehicle

    Xing, Jie / Han Xuefeng, / Ye Hui, et al. | IEEE | 2014


    Urban bus driving cycle

    van de Weijer,C.J.T. / Hendriksen,P. / Verbeek,R.P. et al. | Automotive engineering | 1993


    Urban bus driving cycle

    Van de Weijer, C. J. T. / Hendriksen, P. / Verbeek, R. P. et al. | British Library Conference Proceedings | 1993


    On Vehicle Driving Cycle Simulation

    Jacobson, Bengt | SAE Technical Papers | 1995