Data-Enabled Tire-Road Friction Estimation Based on Explainable Dynamics Mechanism Under Straight Stationary Driving Maneuvers

The tire-road friction coefficient (TRFC) is the critical parameter that significantly improves the control performance of distributed electric vehicles. Nonetheless, achieving precise TRFC estimation during straight stationary driving maneuvers, characterized by constant longitudinal speed (e.g., where the longitudinal acceleration is nearly zero) on a straight road, poses a particularly formidable challenge. In the paper, we propose a new learning strategy that leverages multi-domain fusion feature extraction in both the time domain and time-frequency domain to estimate the TRFC during straight stationary driving maneuvers. Specifically, the frequency response function of the in-wheel-motor-drive system first is inferred from the longitudinal dynamics model and single wheel dynamics model. Then, the input selection of learning strategy is determined through frequency response characteristics analysis and explainable dynamics mechanism. In addition, a parallel spatial-temporal convolutional neural network (PSTCNN) is built to extract features in both the time domain and in the time-frequency domain, respectively. Finally, the TRFC learning strategy is verified by experimental tests on different road surfaces. Our results demonstrate that the proposed methodology is capable of estimating the TRFC with a lower error than the traditional learning-based method and the classical slip-slope method.