Study on the Grey Predictive Extension Control of Yaw Stability of Electric Vehicle Based on the Minimum Energy Consumption

Abstract

According to the characteristics that the driving torque of each wheel of a four-wheel hub motor drive electric vehicle can be independently controlled, the stability control of the electric vehicle could be realized by controlling the output torque of wheel hub motor (i.e., adjusting the wheel driving force or brake force) to generate additional yaw moment. The hierarchical control strategy is applied for the vehicle stability control. The upper layer is a yaw moment controller, which includes two fuzzy controllers based on yaw rate and sideslip angle, respectively, and an extension combination controller. The lower layer is a driving force distribution controller, which utilizes the pseudo inverse algorithm to optimize the driving torque allocation of each wheel. Its control modes are divided into stability control, minimum energy consumption control and combination control. The gray control model is used to preprocess the actual yaw rate and sideslip angle. According to the electric vehicle driving state, the control domain is divided into three domains, i.e., classic domain, extension domain and non-domain. And in different domains, different control modes are employed to ensure the vehicle's stability and reduce the energy consumption. The vehicle dynamics model is established in Matlab/Simulink. The simulations of stability control and minimum energy consumption control have been carried out in double lane change condition. The simulation results show that the proposed control strategy can effectively guarantee the vehicle's stability and minimize the energy consumption. Finally, the control strategy was verified on a wheel hub motor test bench based on Carsim and LabVIEW.