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论文类型：期刊论文

发表时间：2016-04-02

发表刊物：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录刊物：SCIE、EI

卷号：30

期号：5

页面范围：2581-2592

ISSN号：1064-1246

关键字：Fuzzy recognition; intelligent learning; control strategy; hybrid electric vehicle

摘要：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.