Adaptive Energy Management of Hybrid Electric Vehicles Based on Gate Control Mechanism of Hybrid Prediction Model

doi:10.3901/JME.260715

Abstract

Abstract: To improve the energy-saving effect of vehicles, predictive control has become a research hotspot in energy management strategies for hybrid electric vehicles. With the diversity of driving conditions, the accuracy of prediction models directly affects vehicle performance. To address this issue, an adaptive energy management framework based on a hybrid prediction model using gate control mechanism is proposed, which mainly integrates personalized driving pattern recognition, a hybrid prediction vehicle speed model with gate control mechanism, and model predictive control. To improve the accuracy of pattern recognition, a Bayesian optimized multi-layer perceptron neural network model integrating personalized features is proposed based on personalized mixed clustering method, which has improved by 2.69% compared to the recognition model based on basic vehicle features; Based on the results of personalized driving pattern recognition, a hybrid expert network with gate control mechanism is used for speed prediction to improve prediction accuracy. Furthermore, the impact of different vehicle speed prediction model accuracies on the adaptive energy management strategy of hybrid electric vehicles is analyzed, and the proposed method can achieve fuel consumption savings of 3.34%～8.65%.

Key words: hybrid electric vehicles, energy management strategies, hybrid prediction model, gate control mechanism

CLC Number:

U469

YANG Dongpo, GAO Jiahui, SHI Dehua, ZENG Xiaohua. Adaptive Energy Management of Hybrid Electric Vehicles Based on Gate Control Mechanism of Hybrid Prediction Model[J]. Journal of Mechanical Engineering, 2026, 62(14): 302-312.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260715

http://www.cjmenet.com.cn/EN/Y2026/V62/I14/302

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