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

被引：27
作者：
Hu, Yue [1 ,2 ,3 ]
Li, Weimin [1 ,3 ]
Xu, Hui [3 ]
Xu, Guoqing [1 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[2] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen 518055, Peoples R China
[3] Chinese Acad Sci, Jining Inst Adv Technol, Jining 272000, Peoples R China
来源：
ENERGIES | 2015年 / 8卷 / 10期
基金：
中国国家自然科学基金;
关键词：
hybrid electric vehicle; fuzzy Q-learning (FQL) control strategy; Q*(x; u) estimator network (QEN); fuzzy parameters tuning (FPT); ENERGY MANAGEMENT;
D O I：
10.3390/en81011167
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
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.
引用
收藏
页码：11167 / 11186
页数：20
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