# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
###### Abstract  
To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. battery electric vehicles; multi-mode; braking energy recovery; control strategy; fuzzy control; genetic algorithm +
Footnote †: journal: Applied sciences  
generated during the braking process and extends the service life of the components at the same time. Therefore, how to maximize the recovery of braking energy while ensuring safe and stable braking is the key issue of braking energy recovery technology [4]. However, the braking energy recovery control strategy commonly used in current pure electric vehicles generally exists as a single mode of operation, and due to harsh working conditions and other issues, cannot make full use of the regenerative braking torque generated by the pure electric vehicle motor, which leads to low braking energy recovery efficiency, in addition to not being able to effectively improve the range of the vehicle, leaving a large space for optimization. Therefore, in this paper, a brake energy recovery control strategy that can effectively improve the braking energy recovery control efficiency of pure electric vehicles and increase the range of the vehicle is investigated, and a multi-mode brake energy control strategy with the accelerator pedal and brake pedal opening as the switching conditions is proposed. Since fuzzy control has a strong adaptability to environmental changes, which can make the control system maintain good performance even when the characteristics of the controlled object are changed or perturbed [5; 6], fuzzy control is used to calculate the power of the motor mechanism in different modes. And the genetic algorithm is used to optimize the maximum range of the fuzzy control's affiliation function variables to enhance the global search capability and adaptivity of the control strategy [7; 8] to recover the braking energy with higher efficiency. The research on braking energy recovery first started in 1999. [PERSON] and other scholars proposed three typical basic braking force distribution control strategies for hub motor-driven pure electric vehicles [9; 10; 11]. In 2001, [PERSON] and other scholars further investigated the braking energy recovery control strategies based on neural network theory based on the use of switched reluctance motors for pure electric vehicles [12].