Affiliations:
School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, P.R. China
Correspondence:
[*]
Corresponding author. Linhui Li, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, P.R. China. Tel.: +86 15524706097; Fax: +86 41184706475; E-mail: dlutlilinhui@126.com.
Abstract: This paper is concerned with the self-adaptive control problems for the parallel hybrid electric power systems based on the fuzzy relative membership classification theory and an adaptive control strategy optimization method for HEV dynamic systems is proposed. This optimized control strategy can adaptively adjust its control parameters based on the real-time driving cycle, effectively improving the fuel economy of the HEV. Firstly, four types of representative driving cycles are constructed based on actual vehicle operating data, using principal component analysis and cluster analysis to reflect the actual vehicle running conditions. Additionally, the optimal control parameters for each type of representative driving cycle are determined. Then, a fuzzy driving cycle recognition algorithm is proposed for online recognition of the actual driving cycle type. The optimal control parameters for the identified driving cycle type are then updated in the vehicle controller, to automatically realize control strategy optimization for different driving cycles. Finally, simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.
Keywords: Fuzzy recognition, intelligent learning, control strategy, hybrid electric vehicle
