Hybrid Data-Mechanism Modeling for Tire Response Dynamics in Estimating Tire-Road Friction Coefficient

Advanced control and safety systems are crucial for electric vehicles, and the accurate estimation of the tire-road friction coefficient (TRFC) is crucial for developing effective safety control strategies. The hybrid data-mechanism model (HDMM), introduced in this paper, addresses the performance challenges posed by the inaccuracies of physical models and the limited interpretability of data-driven models in tire force estimation for TRFC estimation. Tire dynamics often exhibit transient responses, while mechanism-based models(MBM) typically reflect steady-state characteristics. Neglecting transient characteristics leads to a decrease in model accuracy. A neural network is used to learn the transient response characteristics of tire dynamics. These characteristics are then integrated with the steady-state tire forces from MBM to estimate the lateral and vertical forces acting on the wheel. The estimated tire forces serve as virtual measurements to calibrate parameters in the TRFC estimator, based on the Unscented Kalman Filter (UKF). During real-world vehicle tests, the proposed method reduced the Mean Error (ME) in lateral and vertical forces by 1271.85 N and 996.7 N, respectively, compared to the estimated tire forces from MBM. Additionally, the estimated TRFC converged to the reference value approximately 40ms earlier than the result from the MBM, with an estimated deviation within 0.1.