Precise Model-Based Predictive Motion Control for Electric Vehicles Using Limited Offline Data and Real-Time Online Adaptation

The pronounced nonlinearity of vehicle dynamics under extreme conditions presents a substantial challenge for motion control. The complexity of nonlinear modeling, parameter inaccuracies, and the high computational cost associated with high-order nonlinear control systems significantly hinder improvements in control stability and performance. To address these challenges, this article proposes an innovative data-driven model predictive control (DMPC) approach. A data-driven modeling (DM) technique is first introduced to construct a linear model based on step response input/output (I/O) data, with real-time compensation for model deviations using online data to more accurately capture the vehicle’s lateral and longitudinal dynamics. The stability of the model estimation error is rigorously analyzed, and a data-driven vehicle model with theoretical guarantees is established. A hierarchical stability control framework based on DM is then developed. The upper-level controller evaluates vehicle maneuverability and lateral stability to determine the required additional yaw moment, while the lower-level controller assesses longitudinal stability and traction, and incorporates lateral–longitudinal coupling to optimize motor torque distribution across the wheels. DM is validated using more than 25 000 s of low-adhesion road data, demonstrating significantly higher modeling accuracy than traditional nonlinear models (NLMs). Compared with the NLM-based controller, DMPC improves vehicle handling and lateral stability by 28.98% and 66.92%, respectively, while reducing driver workload by 33.72% in lateral tests. In longitudinal tests, the strategy increases vehicle acceleration by 58.65% and reduces yaw fluctuation by 79.5%. Finally, the robustness of DMPC is evaluated through parameter sensitivity analysis and variations in tire pressure.