Nonlinear Robust Fusion Estimation of Centroid Sideslip Angle and Tire Lateral Forces for Four-wheel-drive Electric Vehicles

Abstract

It was rather difficult to measure the centroid side slip angle and the tire lateral forces of the four-wheel-drive electric vehicles directly. Considering the unmodeled dynamic characteristics of the systems, model parameter perturbations, system process noises and measurement noises, a joint estimation method was proposed based on FFRLS and RCKF. Based on applying FFRLS to estimate the mass of the vehicle in real time and the estimation error minimization in the background of the maximum value embedded into the standard cubature Kalman filter to realize RCKF. The improved strategy of the joint estimation algorithm was proposed, which effectively improved the anti-interference ability of the filter to the model parameter perturbations and the unmodeled noises under composite conditions. It may realize the accurate estimation of the centroid sideslip angle and tire lateral forces. By CarSim/Simulink, the accuracy, robustness and anti-interference of the algorithm were verified in different conditions. Through the actual vehicle platform of four-wheel-drive electric vehicle, the validity of the algorithm was verified. Research show that the results of the proposed method are more accurate than that of RCKF and the standard Cubature Kalman filter. The problems of joint estimation of centroid side slip angle and tire lateral forces of four-wheel-drive electric vehicle are solved under composite conditions.