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.
Highlights
Hybrid electric vehicles (HEV), which combine the advantages of the fuel vehicle and pure electric vehicle, is the future of the road vehicle
In order to make a control strategy adaptive to different driving cycles and convenient for practical application, we propose an approach to tune fuzzy controllers based on fuzzy Q-learning (FQL)
In order to know the effectiveness of the FQL algorithm, simulation experiments were done in ADVISOR
Summary
Hybrid electric vehicles (HEV), which combine the advantages of the fuel vehicle and pure electric vehicle, is the future of the road vehicle. According to the literature [5,6,7,8,9,10], to optimize the operation of the HEV drivetrain, some model-based global optimization methods have been employed in control strategy design, such as dynamic programming (DP), sequential quadratic programming (SQP), genetic algorithms (GA), and so on These algorithms can manage to determine the optimal power split between the engine and the motor for a particular driving cycle. A dynamic model based on a predictive future, control action based on online rolling optimization, and feedback correction of the model error are the core features of the algorithm This control strategy has the advantages of good control effect and strong robustness. The decrease of computational load makes FQL algorithm more convenient for practical applications
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