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Abstract
Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.
Highlights
A Neural Network Fuzzy Energy ManagementState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
Hybrid electric vehicles (HEVs) combine the advantages of traditional fuel vehicles with battery electric vehicles (BEVs)
HEV was proposed based on the driving cycle model and a dynamic programming algorithm [18], while an fuzzy energy management strategy (F-energy management strategy (EMS)) based on driving cycle recognition (DCR) was proposed in Reference [19] to improve the fuel economy of a parallel HEVs (PHEVs), which consisted of DCR and a fuzzy torque distribution controller
Summary
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China. Received: 6 December 2019; Accepted: 15 January 2020; Published: 19 January 2020
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