Control strategy of electric vehicle regenerative braking integrating fuzzy control and PSO

Introduction The long charging cycle, limited energy storage system, and short range of traditional batteries have constrained the further development of electric vehicles. Methods Given this, the paper constructs a regenerative braking control strategy for electric vehicles based on hierarchical fuzzy control, and optimizes it using an improved Particle Swarm Optimization (PSO) algorithm. The study aims to improve the energy recovery efficiency of electric vehicles while ensuring the safety and stability of vehicle braking by reasonably allocating motor and hydraulic braking forces. Results The results showed that the improved PSO exhibited faster convergence speed and higher accuracy in the optimization process, with the smallest difference in optimal solutions and the lowest loss function value of 10 −5 . In terms of regenerative braking control effect of electric vehicles, the control strategy built on improved PSO achieved an energy recovery rate of 16.8% and increased the contribution of driving range by 35 km. Its braking response time has been shortened to 0.71 s, the braking stability index has reached 95, and the energy consumption rate has been reduced to 150 Wh/km. Discussion The proposed hierarchical fuzzy control strategy based on improved PSO provides an efficient and stable solution for the design and optimization of regenerative braking systems in electric vehicles. This optimization scheme can enhance the energy utilization efficiency and endurance of electric vehicles, which is of great significance for promoting the development of electric vehicle technology.