An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning

Energies

Yue Hu²  Weimin Li²  Hui Xu¹  Guoqing Xu² 

[1] Jining Institutes of Advanced Technology, Chinese Academy of Sciences, Jining 272000, China; E-Mail:;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; E-Mails:

关键词: hybrid electric vehicle;    fuzzy Q-learning (FQL) control strategy;    Q*(x;    u) estimator network (QEN);    fuzzy parameters tuning (FPT);   

DOI  :  10.3390/en81011167

来源: mdpi

【 摘 要 】

In order to realize the online learning of a hybrid electric vehicle (HEV) control strategy, a fuzzy Q-learning (FQL) method is proposed in this paper. FQL control strategies consists of two parts: The optimal action-value function Q*(x,u) estimator network (QEN) and the fuzzy parameters tuning (FPT). A back propagation (BP) neural network is applied to estimate Q*(x,u) as QEN. For the fuzzy controller, we choose a Sugeno-type fuzzy inference system (FIS) and the parameters of the FIS are tuned online based on Q*(x,u). The action exploration modifier (AEM) is introduced to guarantee all actions are tried. The main advantage of a FQL control strategy is that it does not rely on prior information related to future driving conditions and can self-tune the parameters of the fuzzy controller online. The FQL control strategy has been applied to a HEV and simulation tests have been done. Simulation results indicate that the parameters of the fuzzy controller are tuned online and that a FQL control strategy achieves good performance in fuel economy.

【 授权许可】

CC BY   
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