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

• Main Authors: Yue Hu, Weimin Li, Hui Xu, Guoqing Xu
• Format: Article
• Language: English
• Published: MDPI AG 2015-10-01
• Series: Energies
• Subjects: hybrid electric vehicle; fuzzy Q-learning (FQL) control strategy; Q*(x,u) estimator network (QEN); fuzzy parameters tuning (FPT)
• Online Access: http://www.mdpi.com/1996-1073/8/10/11167

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.