# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
###### Abstract
On this basis, [PERSON] and [PERSON] and other scholars formulated the braking energy recovery strategy based on the multi-objective optimization method and the statistical method of incremental energy analysis [13; 14], respectively, while [PERSON] designed the braking energy recovery strategy based on adaptive fuzzy control algorithm [15]. All these studies on early brake energy recovery control strategies can effectively recover the energy generated during vehicle braking, but the energy recovery efficiency is low and cannot effectively extend the vehicle range. In recent years, with the continuous development of the automotive industry and the gradual maturation of technology, how to optimize the distribution of braking force between the front and rear axles of pure electric vehicles and the distribution of electric and mechanical-hydraulic braking forces have become two key issues to improve the braking force energy recovery rate [16]. [PERSON] et al. combined the current vehicle braking intensity and front and rear axle braking force distribution strategies to establish a braking energy recovery control strategy and optimized the control strategy through the PSO (Particle Swarm Optimization) algorithm [17]. [PERSON] et al. proposed an improved braking force recovery strategy based on an ideal braking force distribution curve for the regenerative braking system of pure electric vehicles [18]. Compared with other previous research, this control strategy considers braking stability more and covers a wider range of braking situations of the vehicle. With continuous research, fuzzy control is widely used due to its wide applicability, robustness, and simple structure. [PERSON] established a fuzzy braking energy recovery strategy based on a front-wheel drive for a pure electric vehicle using vehicle braking intensity, driving speed, and battery SOC (State of Charge) as inputs and regenerative braking force ratio coefficient as output [19]. [PERSON] et al. used vehicle speed, braking intensity, battery SOC, and battery temperature as fuzzy control inputs and regenerative braking distribution percentage as output fuzzy regenerative braking control strategy [20]. The ideal braking force distribution curve and ECE (Economic Commission for Europe) regulations were used as boundaries to determine the range of braking force distribution for pure electric vehicles. Moreover, [PERSON] et al. proposed a composite control strategy based on the original fuzzy control by combining the I curve, the ECE regulation (M curve) curve, and the f-line group [21]. [PERSON] et al. designed a fuzzy control-based braking energy recovery control strategy under the high-speed driving conditions of new energy vehicles [14]. In this paper, a multi-mode switching brake energy recovery control strategy is proposed by analyzing the process of the driver operating the accelerator pedal and brake pedal during vehicle braking. The main contributions of this paper are as follows: (1) Based on the braking process of the vehicle, this paper further optimizes the existing multi-mode braking energy recovery control strategy and proposes a multi-mode braking energy recovery control strategy including single-pedal mode, conventional braking mode, and coasting mode. (2) A fuzzy controller is established to calculate the front and rear axle brake force distribution coefficients and the motor braking ratio in the single-pedal mode and conventional braking mode, respectively. The affiliation functions of the input and output variables of the fuzzy controller are optimized by genetic algorithm (GA) in conventional braking mode. (3) Simulation analysis is conducted based on two different cyclic conditions, NEDC and CLTC-P, respectively, which verifies that the multi-mode braking energy recovery control strategy proposed in this paper can effectively improve the braking energy recovery efficiency of the vehicle and increase the vehicle range.