<p>The advancement of intelligent driving technology has rendered distributed drive electric vehicles a suitable platform for the coordinated control of path tracking and stability. This is due to their inherent benefits of independent torque control and profound integration of longitudinal and lateral dynamics. A hierarchical path tracking and stability coordinated control strategy is introduced for distributed drive electric vehicles in this study. To begin with, the tire model, vehicle dynamics model, and path tracking error model are established to establish a basis for algorithm development. The control strategy comprises three layers: the upper layer utilizes the ALQR-based path tracking algorithm with dynamically adjusted prediction time by the adaptive prediction time module to enhance tracking accuracy; the middle layer features a direct yaw moment controller designed using sliding mode control (SMC); and the stability is improved by calculating an additional yaw moment. The lower layer optimally distributes driving torque by solving a constrained quadratic optimization (QP) problem. It dynamically adjusts the control weight in conjunction with the tire force method to achieve coordinated optimization of path tracking and stability. The effectiveness of the strategy in enhancing path tracking accuracy and handling stability on high and low adhesion roads is demonstrated through CarSim/Simulink co-simulation verification. The hardware-in-the-loop test platform is developed to validate the algorithm’s adaptability and robustness in challenging conditions.</p>