TY - JOUR
T1 - Braking/steering coordination control for in-wheel motor drive electric vehicles based on nonlinear model predictive control
AU - Zhu, Junjun
AU - Wang, Zhenpo
AU - Zhang, Lei
AU - Dorrell, David G.
N1 - Publisher Copyright:
© 2019
PY - 2019/12
Y1 - 2019/12
N2 - Braking and steering are common maneuvers performed by drivers during driving. This paper presents a hierarchy control strategy to coordinate the braking and steering performance for in-wheel motor drive electric vehicles (IWMD EVs). A particle swarm optimization-based nonlinear predictive control (PSO-NMPC) scheme is proposed to calculate the required longitudinal force, lateral force and yaw moment of the vehicle in the upper controller. In the lower controller, the PSO algorithm is again utilized to realize the required forces and yaw moment through optimal torque allocation and brake actuator regulation while maintaining vehicle stability and maximizing the regenerative braking recovery. A fault-tolerance mechanism is also incorporated to enhance the robustness of the proposed method. Finally, the effectiveness of the proposed scheme is examined under various braking execution scenarios through the Carmaker-Simulink co-simulation. The results show that the proposed scheme outperforms other state-of-the-art methods in all-round aspects.
AB - Braking and steering are common maneuvers performed by drivers during driving. This paper presents a hierarchy control strategy to coordinate the braking and steering performance for in-wheel motor drive electric vehicles (IWMD EVs). A particle swarm optimization-based nonlinear predictive control (PSO-NMPC) scheme is proposed to calculate the required longitudinal force, lateral force and yaw moment of the vehicle in the upper controller. In the lower controller, the PSO algorithm is again utilized to realize the required forces and yaw moment through optimal torque allocation and brake actuator regulation while maintaining vehicle stability and maximizing the regenerative braking recovery. A fault-tolerance mechanism is also incorporated to enhance the robustness of the proposed method. Finally, the effectiveness of the proposed scheme is examined under various braking execution scenarios through the Carmaker-Simulink co-simulation. The results show that the proposed scheme outperforms other state-of-the-art methods in all-round aspects.
KW - Braking intention tracking
KW - In-wheel motor drive electric vehicle
KW - Nonlinear model predictive control
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85070337073&partnerID=8YFLogxK
U2 - 10.1016/j.mechmachtheory.2019.103586
DO - 10.1016/j.mechmachtheory.2019.103586
M3 - Article
AN - SCOPUS:85070337073
SN - 0094-114X
VL - 142
JO - Mechanism and Machine Theory
JF - Mechanism and Machine Theory
M1 - 103586
ER -