A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy

Yinhang Wang, Liqing Zhou, Liang Chu, Di Zhao (), Zhiqi Guo and Zewei Jiang
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Yinhang Wang: College of Automotive Engineering, Jilin University, Changchun 130025, China
Liqing Zhou: College of Automotive Engineering, Jilin University, Changchun 130025, China
Liang Chu: College of Automotive Engineering, Jilin University, Changchun 130025, China
Di Zhao: College of Automotive Engineering, Jilin University, Changchun 130025, China
Zhiqi Guo: College of Automotive Engineering, Jilin University, Changchun 130025, China
Zewei Jiang: College of Automotive Engineering, Jilin University, Changchun 130025, China

Energies, 2024, vol. 17, issue 9, 1-31

Abstract: Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

Keywords: commercial vehicles; state observation; braking force control; braking source distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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