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Combined with the characteristics of the distributed-drive electric vehicle and direct yaw moment control, a double-layer structure direct yaw moment controller is designed. The upper additional yaw moment controller is constructed based on model predictive control. Aiming at minimizing the utilization rate of tire adhesion and constrained by the working characteristics of motor system and brake system, a quadratic programming active set was designed to optimize the distribution of additional yaw moments. The road surface adhesion coefficient has a great impact on the reliability of direct yaw moment control, for which joint observer of vehicle state parameters and road surface parameters is designed by using unscented Kalman filter algorithm, which correlates vehicle state observer and road surface parameter observer to form closed-loop feedback correction. The results show that compared to the “feedforward + feedback” control, the vehicle’s error of yaw rate and sideslip angle by the model predictive control is smaller, which can improve the vehicle stability effectively. In addition, according to the results of the docking road simulation test, the joint observer of vehicle state and road surface parameters can improve the adaptability of the vehicle stability controller to the road conditions with variable adhesion coefficients.

Research on Direct Yaw Moment Control Strategy of Distributed-Drive Electric Vehicle Based on Joint Observer