Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Abstract

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

“…Jiang et al [71] reported that their energy recovery efficiency reached 55%. In the NEDC, Jiang et al [71] achieved an energy recovery efficiency of 55.3%, followed by Salari et al [80], Wager et al [81], Xiao et al [86], Mondal et al [84], Yin et al [70], and Xu et al [92]. Although our energy recovery efficiency is not as high as that of Jiang et al [71], it is better than others.…”

Research on regenerative braking control of electric vehicles based on game theory optimization

“…The output of the controller is commonly the ratio coefficient between regenerative braking torque and total braking torque. 4 , 10 – 13 He et al 14 introduced a neural network (NN) controller for composite braking systems aiming to enhance energy efficiency and braking stability concurrently. Simulation results revealed a 3.04% enhancement in the final SOC with the NN controller compared to the parallel braking approach, indicating superior energy efficiency.…”