Discovery of Interpretable Physical Models for High-Maneuverability Trajectory Tracking Control of Electric Vehicles via Transient Response Analysis

Steady-state tire models fail to capture elastic hysteresis effects, leading to significant modeling errors under transient conditions. In trajectory tracking tasks, such errors may lead to controller overresponse or underresponse, seriously compromising vehicle safety and stability. To address this challenge, this study proposes a data-driven framework for discovering interpretable, physics-informed tire dynamic models under transient conditions. First, transient data are extracted using the Latin hypercube sampling, targeting the relatively scarce data in transient scenarios. Next, the sampling process in candidate library sampling-sparse identification of nonlinear dynamics is enhanced by incorporating physics-informed priors during candidate feature expansion, thereby improving model interpretability. High-frequency feature selection is then performed on the nonzero sparse coefficients to construct the candidate function library and identify the transient tire model. Finally, the identified model is integrated into a nonlinear model predictive controller for trajectory tracking and validated via both real vehicle experiments and hardware-in-the-loop simulations. The experimental results demonstrate significant improvements in tire force prediction accuracy, trajectory prediction, and tracking performance with the proposed transient tire model, compared to steady-state models. These improvements are observed under transient conditions, including slalom maneuvers and emergency double-lane changes on the ice-snow road.