Hybrid electric vehicles: A review of energy management strategies based on model predictive control. (10th December 2022)

• Record Type: Journal Article
• Authors: Lü, Xueqin; Li, Siwei; He, XiangHuan; Xie, Chengzhi; He, Songjie; Xu, Yuzhe; Fang, Jian; Zhang, Min; Yang, Xingwu
• Is Part Of: Journal of energy storage. Volume 56:Part B(2022)
• Publication Date: 2022-12-10
• Subjects: Hybrid electric vehicles -- Energy management -- Model predictive control -- Energy storage system; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.106112
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: At present, hybrid electric vehicles are regarded as an effective way to solve global environmental pollution and energy shortage. Energy management strategy is the core technology of hybrid electric vehicles, which directly determines the fuel economy, driving performance, and life of the vehicle. However, the uncertainty and disturbance of real working conditions greatly increase the difficulty of energy management algorithm design. For this reason, many researchers have conducted in-depth research in this field. Among them, the model predictive control strategy has the characteristics of real-time prediction and rolling optimization, and its application in different systems has proved that it has a good control effect and robustness. This paper comprehensively summarizes the advantages and applicable scenarios of different prediction methods and solution methods in the energy management strategy based on model predictive control of hybrid electric vehicles. The specific application and actual performance of the energy management strategy based on model predictive control in different systems are emphatically analyzed. Finally, the development of energy management strategy is prospected from the perspective of MPC-EMS. Based on the in-depth discussion, this research hopes to provide guidance and design ideas for researchers and promote the development of energy management strategies. Highlights: The operation mechanism of MPC-EMS is analyzed in detail. The characteristics and applicable scenarios of MPC-EMS with different prediction and solution methods are summarized. The efforts of MPC-EMS in multi-objective optimization of two types of HEVs are analyzed. The improvement and development direction of MPC-EMS are discussed.