Steering stability and energy efficient oriented optimization control for distributed drive electric mining trucks

Distributed drive electric mining trucks operate under conditions fundamentally different from passenger vehicles, characterized by heavy and variable payloads, prolonged low-speed haulage, long wheelbases, and wide steering radii. These factors cause significant parameter variations and stringent dynamic constraints, rendering conventional stability-efficiency coordination strategies insufficient. While model predictive control has been extensively studied for passenger vehicles, limited research addresses its application to heavy mining trucks, particularly for the joint optimization of lateral stability and energy efficiency under steering conditions. This study proposes a hierarchical optimize control framework tailored to 4WID/4WIS electric mining trucks. The upper-layer employs a stability-region-embedded adaptive model predictive control with online adaptive weight scheduling and a Lyapunov-based compensation term to generate corrective yaw moments under model-induced mismatches. The lower-layer distributes drive torques through a hybrid offline computer-online optimization, jointly considering tire adhesion utilization and motor efficiency under yaw moment demands. Both co-simulation in Matlab/Simulink-TruckSim and Hardware-in-the-loop experiments under representative open-pit scenarios demonstrate that, the proposed strategy reduces sideslip angle by at least 3.36 %, and decreases energy consumption by at least 1.18 % compared with baseline controllers. These results confirm the effectiveness and real-time feasibility of the proposed strategy, demonstrating its potential for practical deployment in mine haulage transportation field.