An MPC Strategy for Anti-Pitch/Roll in Electric Vehicle Active Suspension Systems With Wheelbase Preview Information on Uneven Roads

Abstract

On uneven roads, active suspension can effectively reduce road vibrations and shocks compared with passive suspension. In this context, a wheelbase preview-based model predictive control (PMPC) approach for anti-pitch/roll in electric vehicle active suspension systems (ASS) is proposed to enhance vehicle ride comfort and stability. First, a 7-DOF vehicle model and a road excitation model were established. This road excitation model can more comprehensively reflect the vehicle’s vibration, pitch, and roll states under different uneven roads. Next, considering the wheelbase preview information, an augmented prediction model that external disturbances are augmented as state variables is designed to improve the prediction accuracy. This augmented prediction model reduces the number of external disturbances while using feedforward information of the disturbances to provide a more accurate prediction of the vehicle’s motion states. Then, based on the modified predictive model, we propose an model predictive control (MPC) method for electric vehicle ASS that considers disturbance feedforward information. The suppression of the vehicle’s vertical acceleration, pitch acceleration, and roll acceleration is set as the objective function, with disturbance feedforward information used as the predicted output. Meanwhile, the suspension dynamic deflection, tire dynamic load, and actuator constraints are considered. Finally, the proposed controller is tested via co-simulations with CarSim and MATLAB/Simulink, and a hardware-in-the-loop (HIL) system. The results demonstrate that the controller effectively improves vehicle comfort and stability on various levels of uneven roads in both the time and frequency domains.