Genetic Algorithm Optimization of Regenerative Energy Recovery Technology for Pure Electric Vehicle

In order to improve the driving distance of pure electric vehicles, an improved regenerative energy recovery technology is proposed for different cyclic working conditions. Firstly, the vehicle dynamics model and motor model are analyzed to determine the factors that affect the energy recovery efficiency of the vehicle regenerative braking system. Secondly, according to their influencing factors, the regenerative energy recovery strategy based on fuzzy control uses genetic algorithm to improve its parameters. Finally, the regenerative energy recovery strategy is modeled based on MATLAB and embedded in Carsim for experiments. The results show that the control strategy proposed in this paper improves the braking energy recovery efficiency by 11.6% and 8.3% under the two operating conditions of NEDC and WLTC respectively, and improves the endurance of pure electric vehicles.