Real-time hierarchical yaw stability control of electric vehicles considering road-induced variations in tire loads

To improve vehicle lateral stability under extreme driving conditions, this study proposes a real-time hierarchical direct yaw moment control strategy that accounts for road-induced fluctuations in tire dynamic loads. In the upper layer, a sliding mode controller is designed to track the desired yaw rate and sideslip angle. In the lower layer, tire torque allocation is optimized based on time-varying vertical load information, with the objective of minimizing the tire adhesion utilization ratio. A 14-degree-of-freedom vehicle-road coupled dynamics model based on random road excitation is established to investigate the influence of road grade and vehicle speed on tire vertical load. A co-simulation model of the whole vehicle is built based on the CarSim-Simulink and Hardware-in-the-Loop (HIL) platform. The effectiveness of the proposed strategy is evaluated and validated under three typical driving maneuvers: steering step input, double lane-change, and sinusoidal steering input. The results show that with the increase of vehicle speed and the decrease of road grade, the amplitude of tire vertical dynamic load fluctuation increases from 5.52% to 24.8%. Compared with the uncontrolled case, the proposed strategy improves the control performance of the sideslip angle and yaw rate by more than 40% under both the double lane-change and steering step input maneuvers at a vehicle speed of 72 km/h. Under the sine condition, when the vehicle travels at a speed of 90 km/h on A/D grade roads, the control accuracy of the center of gravity sideslip angle and yaw rate is improved by more than 30%, which verifies the effectiveness of the proposed strategy in improving lateral stability.