Estimation of tire-road friction coefficient with adaptive tire stiffness based on RC-SCKF

Tire-road friction coefficient is of great significance for vehicle active safety systems, advanced driving assistance systems, and even future autonomous vehicles. Most of the dynamics-based methods for estimating the tire-road friction coefficient neglect the characteristic that the tire stiffness changes with the driving conditions, resulting in a limited number of driving conditions for which the friction coefficient can be effectively estimated. In order to improve the accuracy of the tire-road friction coefficient estimation results, an estimation method is proposed in which the tire stiffness can change adaptively according to the vertical load and the vehicle dynamics response. By analyzing the change in length of the tire-road contact patch, the relationship between vertical load and tire stiffness is established. By adding tire stiffness to the system state, the relationship between tire stiffness and vehicle dynamics response is established. A square-root cubature Kalman filter algorithm with rapid convergence by automatically adjusting the measurement noise covariance matrix is developed to enhance the real-time performance of the estimation method. Finally, the superiority of the method is verified under different test scenarios based on CarSim and MATLAB/Simulink co-simulation platform. The results also show that this method is able to make an accurate identification when tire damage happens.