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Transactions of FAMENA, Vol. 47 No. 4, 2023.

Original scientific paper

https://doi.org/10.21278/TOF.474045522

Research on Design Optimization and Simulation of Regenerative Braking Control Strategy for Pure Electric Vehicle Based on EMB Systems

Shuwen Zhou ; College of Mechanical Engineering and Automation, Northeastern University, Shenyang Liaoning, China
Jinshuang Liu orcid id orcid.org/0000-0002-7230-1158 ; College of Mechanical Engineering and Automation, Northeastern University, Shenyang Liaoning, China *
Zhaolun Wang ; College of Mechanical Engineering and Automation, Northeastern University, Shenyang Liaoning, China
Shaohua Sun ; College of Mechanical Engineering and Automation, Northeastern University, Shenyang Liaoning, China

* Corresponding author.

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Abstract

The benefits of electromechanical braking (EMB) systems are short response time, high braking efficiency, ease of assembly and easy integration with other electronic control systems. Therefore, a model of an EMB system is developed based on which the braking stability, braking efficiency, and the regenerative braking energy recovery in electric vehicles are investigated. Electric vehicles can effectively increase their driving range by using a rational regenerative braking control strategy. Firstly, a fuzzy regenerative braking control strategy is developed for comparison, and an optimized regenerative braking control strategy is designed based on the NSGA-II algorithm. The technique for order preference by similarity to ideal solution (TOPSIS) is used to comprehensively evaluate the Pareto optimal solution set and to select an optimal solution for the optimization problem. Secondly, a Takagi-Sugeno fuzzy neural network is trained with the optimized discrete data, and then the braking force distribution controller is obtained. Simulink and AVL CRUISE are used to simulate the control strategy. The simulation results for variable intensity braking conditions and cyclic conditions NEDC, FTP75, and CLTC-P show that the optimized control strategy outperforms the fuzzy control strategy in braking stability and braking energy recovery.

Keywords

electromechanical brake; pure electric vehicle; regenerative braking control strategy; genetic algorithm; fuzzy neural network

Hrčak ID:

308925

URI

https://hrcak.srce.hr/308925

Publication date:

28.9.2023.

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