Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm

With regard to the rear-drive in-wheel motor vehicle, this paper studies joint estimation method for vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom model a tire are established. driving estimator coefficient based on generalized high-order cubature Kalman filter (GHCKF) algorithm designed. The combines calculate parameters, provides parameters estimator, realizes real-time exponential fading memory adaptive is used update measurement noise variance, we upgrade GHCKF (AGHCKF), which estimates typical working conditions using double GHCKF/AGHCKF were simulated analyzed. Then, high-and low-speed experiments carried out. An integrated navigation system (INS), global positioning (GPS), kinematic (RTK) collect data compare them with estimated values verify feasibility vehicle-state–road-adhesion-coefficient estimator. We compared algorithm, improved AGHCKF (CKF) simulation experimental results show that CKF, GHCKF, algorithms can realize shows best effectiveness robustness three algorithms.