Coordinated strategy for vehicle tracking accuracy and stability based on parameter optimization of integrated control system

Abstract

To enhance the path tracking accuracy and driving stability of distributed drive electric vehicles (DDEVs) under complex driving conditions, a coordinated strategy based on parameter optimization of the integrated control system is proposed in this paper. First, after analyzing the dynamic characteristics of DDEVs, a vehicle dynamics model incorporating a seven-degree-of-freedom(7DOF) system and a Magic Formula tire model is established. Then, a coordinated strategy is designed by a hierarchical control structure, which includes a path tracking layer, a stability adjustment layer, and a torque distribution layer. In the path tracking layer, the Model Predictive Control (MPC) algorithm is designed with soft constraints on tire slip angles, ensuring accurate tracking while preventing excessive lateral slip and instability. In the stability adjustment layer, a Linear Quadratic Regulator (LQR) computes the desired yaw moment, with its weighting matrices dynamically adjusted based on a real-time stability factor derived from phase plane analysis. A genetic algorithm is employed offline to optimize the weight adjustment parameters, enabling adaptive coordination between path tracking accuracy and yaw stability. In the torque distribution layer, a controller is developed to allocate torques to individual wheels by minimizing tire adhesion utilization, further optimizing the power distribution. Finally, the proposed strategy is validated through hardware-in-the-loop (HIL) experiments under double lane change scenarios. On low adhesion road surfaces, the proposed method reduces the maximum lateral tracking error by 55.9% and the maximum side slip angle by 86.3%, while maintaining the vehicle's state within the defined stability boundaries. These results confirm the effectiveness and practical applicability of the proposed strategy.