Observer-Based Torque Vector Control of a Four In-Wheel Motor-Driven Electric Vehicles Considering with Unbalanced Electric Magnetic Field 2022-01-0915

The accuracy and range of chassis control for a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in observer-based EVs control for improving road handling and ride comfort, is a challenging task for the IWM-driven vehicle system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely acquire movement state and chose the reasonable observer-based control algorithm for IWM-driven EVs become a hot topic in both academia and industry. Simultaneously, uncertainty is always existing, e.g., varying road excitation, variable system parameters or nonlinear structure. Meanwhile, the coupling effects between the non-ideal IWM actuator and vehicle are ignored under the assumption of an ideal actuator. To deal with the above mentioned, the paper presents an observer-based control approach, which combines Takagi-Sugeno (T-S) fuzzy observer and torque vector control algorithm, to further improve the chassis performance for IWM-driven EVs under the double lane change steering input. First, a nonlinear full-car model considering with the unbalanced electric magnetic force is established to describe vehicle lateral-vertical dynamics. Second, a fuzzy T-S observer is developed to estimate the state of slip angle and yaw rate in real time. The stability of the proposed observer is calculated using linear matrix inequalities (LMI). Then, a novel observer-based torque vector model predictive control (MPC) strategy is proposed to optimize the chassis performance of IWM-driven EVs. Finally, combing with a test rig platform, the proposed observer-based control algorithm is validated under the double lane change steering input. The research achievements develop a reasonable algorithm to apply to the improving chassis performance for a four IWM-driven EVs.