Vehicle State Estimation by Integrating the Recursive Least Squares Method with a Variable Forgetting Factor with an Adaptive Iterative Extended Kalman Filter

Abstract

The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined method of recursive least squares with a variable forgetting factor and adaptive iterative extended Kalman filtering is proposed for estimation. Based on the established three-degrees-of-freedom nonlinear model of the vehicle, the variable forgetting factor recursive least squares method is used to identify the tire cornering stiffness and serves as an input for vehicle state estimation. An innovative algorithm is used to optimise the uncertain noise covariance in the iterative extended Kalman filter (IEKF) process. Finally, with the help of the joint simulation of CarSim2019 and Matlab/Simulink R2022a, a distributed drive electric vehicle state parameter estimation model is established, and a simulation analysis of typical working conditions is carried out. Furthermore, an experiment is conducted with the pix moving vehicle and the integrated navigation system. The simulation and experimental results show that, compared to the traditional extended Kalman filter algorithm, the proposed algorithm improves the estimation accuracy of the yaw rate, sideslip angle, and longitudinal speed by 58.17%, 57.2%, and 76.47%, respectively, which shows that the algorithm has a higher estimation accuracy and a stronger applicability to provide accurate state information for vehicle handling and stability control.