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Energies 2015, 8(10), 11167-11186; doi:10.3390/en81011167

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

1,2,3
,
1,3,* , 3
and
1
1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
3
Jining Institutes of Advanced Technology, Chinese Academy of Sciences, Jining 272000, China
*
Author to whom correspondence should be addressed.
Academic Editor: K. T. Chau
Received: 30 June 2015 / Revised: 17 July 2015 / Accepted: 12 August 2015 / Published: 9 October 2015
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
View Full-Text   |   Download PDF [913 KB, uploaded 9 October 2015]   |  

Abstract

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. View Full-Text
Keywords: hybrid electric vehicle; fuzzy Q-learning (FQL) control strategy; Q*(x,u) estimator network (QEN); fuzzy parameters tuning (FPT) hybrid electric vehicle; fuzzy Q-learning (FQL) control strategy; Q*(x,u) estimator network (QEN); fuzzy parameters tuning (FPT)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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