An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning
AbstractIn 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. View Full-Text
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Hu, Y.; Li, W.; Xu, H.; Xu, G. An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning. Energies 2015, 8, 11167-11186.
Hu Y, Li W, Xu H, Xu G. An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning. Energies. 2015; 8(10):11167-11186.Chicago/Turabian Style
Hu, Yue; Li, Weimin; Xu, Hui; Xu, Guoqing. 2015. "An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning." Energies 8, no. 10: 11167-11186.