Road Adhesion Coefficient Estimatior Using Adaptive UKF with Model Parameter Perturbation and Unknown Time-Varying Noise

Distributed driving electric vehicles (DDEV) has become one of the research hotspots in automobile industry due to its fully decoupled chassis configuration. Accurate and timely estimation of road adhesion coefficient (RAC) is the premise of vehicle active safety, which will greatly improve the driving comfort and handling stability of DDEV. In this research, an adaptive unscented Kalman filter (AUKF) is proposed for RAC estimation to deal with issues of model parameter perturbation and unknown time-varying noise. Fading coefficient matrix is used to enhance the utilization of new observation data. Meanwhile, Sage-Husa noise estimator is adopted to refresh system noise dynamically. Results indicate that AUKF method has a higher precision, faster convergence and better stability than EKF and UKF. The proposed AUKF method also shows strong robustness to different pavement coefficients and has fortissimo generalization ability to multiple driving scenarios.