TY - GEN
T1 - Study on regenerative braking control strategy for extended range electric vehicles
AU - Li, Yongliang
AU - Zhao, Changlu
AU - Huang, Ying
AU - Wang, Xu
AU - Guo, Fen
AU - Yang, Long
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Aiming at the problem of regenerative braking energy recovery control for extended range electric vehicles, a front-rear braking force distribution strategy that maximizes braking energy recovery is proposed on the premise of ensuring vehicle braking stability and safety in this paper; then a regenerative braking energy recovery strategy based on fuzzy control is designed. In addition, the membership function of the fuzzy controller is optimized by particle swarm optimization with taking the braking energy recovery rate as the target. Finally, a quasi-static model of the whole vehicle simulation is established on the Simulink-Cruise joint simulation platform, and the simulation is performed under the NEDC, FTP72 and Ja1015 operating conditions. The simulation results show that the designed regenerative braking energy recovery control strategy has an energy recovery rate of 53.5%, 43.9% and 56.1% in the above three operating conditions, and the battery charging power does not exceed the maximum charging power in the extended range mode, proving a good control performance.
AB - Aiming at the problem of regenerative braking energy recovery control for extended range electric vehicles, a front-rear braking force distribution strategy that maximizes braking energy recovery is proposed on the premise of ensuring vehicle braking stability and safety in this paper; then a regenerative braking energy recovery strategy based on fuzzy control is designed. In addition, the membership function of the fuzzy controller is optimized by particle swarm optimization with taking the braking energy recovery rate as the target. Finally, a quasi-static model of the whole vehicle simulation is established on the Simulink-Cruise joint simulation platform, and the simulation is performed under the NEDC, FTP72 and Ja1015 operating conditions. The simulation results show that the designed regenerative braking energy recovery control strategy has an energy recovery rate of 53.5%, 43.9% and 56.1% in the above three operating conditions, and the battery charging power does not exceed the maximum charging power in the extended range mode, proving a good control performance.
KW - Cruise/Simulink co-simulation
KW - Energy Recovery
KW - Extended-range electric vehicle
KW - Fuzzy control
KW - Particle swarm
UR - http://www.scopus.com/inward/record.url?scp=85102016823&partnerID=8YFLogxK
U2 - 10.1109/VPPC49601.2020.9330885
DO - 10.1109/VPPC49601.2020.9330885
M3 - Conference contribution
AN - SCOPUS:85102016823
T3 - 2020 IEEE Vehicle Power and Propulsion Conference, VPPC 2020 - Proceedings
BT - 2020 IEEE Vehicle Power and Propulsion Conference, VPPC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE Vehicle Power and Propulsion Conference, VPPC 2020
Y2 - 18 November 2020 through 16 December 2020
ER -