双电机后驱车辆操纵稳定性分层混杂模型预测控制

In order to improve the stability of the vehicle under complicated working conditions, especially on low-adhesive road, a driving stability control strategy combining direct yaw control and active steering control is proposed for the dual-motor driven electric vehicle. A hierarchical control structure is used in the control strategy, including an upper controller for calculating the vehicle's additional yaw moment and active steering angle, and a lower controller for distributing the driving torque. The upper controller adopts the model predictive control of the multi-input and multi-output system, and solves the target additional yaw moment and active steering angle; The hybrid model predictive control is adopted in lower controller, which simplified the nonlinear characteristics of the tire into a segmented hybrid system. The slip condition of the wheel is considered while the driving torque is distributed, thereby the steering stability of the vehicle under complicated working conditions is improved. The hardware-in-the-loop simulation platform based on the dSPACE real-time simulation system is used to perform the semi-physical simulation. The simulation results show that compared with the quadratic programming (QP) torque distribution algorithm, the average relative error under high adhesion road conditions is reduced by 13.64%, the root mean square error accuracy is improved by 42.86%, and the relative deviation of the maximum deviation error is reduced by 7.49%; under low adhesion road conditions, the algorithm can effectively prevent vehicle instability and improve steering stability.