Integrated Motion Control System for Electric Vehicles With Fast Iteration Architecture Enhanced NMPC Strategy

In vehicle motion control, handling, stability, and anti-slip performance are critical for vehicle safety and driver experience. Most existing studies address these requirements using separate controllers, but vehicle dynamics are coupled, which can lead to conflicting control objectives, especially under extreme conditions. To address this, an integrated control architecture is proposed that combines longitudinal, lateral, yaw, and wheel rotation dynamics. A nonlinear model predictive control (NMPC) strategy is used to coordinate these dynamics and meet the control objectives. A fast iteration architecture is introduced for real-time optimization, with the original problem reformulated using Pontryagin’s minimum principle. The effectiveness of the proposed strategy is validated through real vehicle experiments. Compared with the subsystem coordination method, a better closed-loop control performance can be demonstrated with the experiment results under complex road conditions. The mean value of computation time can also be reduced by 7–34 times with the proposed fast iteration architecture under different prediction horizon conditions.