Autonomous drift control strategy for distributed drive electric vehicle based on online optimization considering tire slip coupling and saturation

Autonomous vehicle control seeks to push the handling envelope and expand the repertoire of maneuvers under extreme conditions – especially during emergency obstacle-avoidance events. To this end, a drifting algorithm based on nonlinear model predictive control (NMPC) for the distributed drive electric vehicle (DDEV) is proposed. The non-equilibrium drifting characteristics are incorporated into the control objectives and the motion control inputs are optimized online using NMPC to balance multiple objectives. Its capabilities are evaluated in three demanding scenarios – figure-8 drifting, high-speed lane changes, and tight U-turns – using MATLAB/Simulink co-simulation and dSPACE hardware-in-the-loop (HIL) testing. The simulation results demonstrate significant improvements in active safety and stability, while enabling agile dynamic drifting maneuvers for rapid obstacle avoidance.