Estimations of vehicle driving state and road friction coefficient based on High-degree cubature Kalman filter of distributed drive electric vehicles

It is necessary to improve the estimated accuracy of vehicle driving state and road friction coefficient for distributed drive electric vehicles. This paper proposed an estimation algorithm of vehicle driving state and road friction coefficient based on high-degree cubature Kalman filter. First, a 3 degrees-of-freedom nonlinear vehicle dynamics model with Dugoff's tyre friction model is established; Then, a high-degree cubature Kalman filter-based algorithm for estimating vehicle driving state was designed, and the relationship between vehicle state data and road friction coefficient was used to estimate the road friction coefficient; Finally, a distributed drive electric vehicles model was built with Carsim/Simulink software for validating the estimation algorithm. The results show that the estimation accuracy of the algorithm based on high-degree cubature Kalman filter is higher than that of the algorithm based on traditional cubature Kalman filter.