Vehicle Lateral Motion State Estimation Based on Adaptive Cubature Kalman Filter

Abstract Accurate and reliable vehicle state information is very significant to the vehicle lateral stability control, while it is hard to get information such as body sideslip angle and lateral tire forces due to lack the nonlinearity of the tire. This paper presents a new combined method (adaptive cubature Kalman filter(ACKF) and adaptive proportion integral observer(APIO)) to estimate body sideslip angle, yaw rate and lateral tire force for vehicle system. Firstly, based on a four-wheel vehicle dynamics model, (ACKF) is used to estimate body sideslip angle and yaw rate with considering the nonlinear lateral tire force stage. Due to system nonlinearities and un-modeled dynamics, APIO is used to improve the estimated body sideslip angle by utilizing the estimated yaw rate and vehicle lateral speed. Then, ACKF is used to estimate the front and rear lateral tire forces based on the one-order tire dynamics model. By utilizing the partition coefficient calculated by the vertical force model, the front and rear lateral tire forces are further distributed to left and right wheels. For comparison, estimation model based on extended Kalman filter(EKF) is built and investigated. Simulation using Matlab/Simulink-CarSim and car test verifies the effectiveness of the proposed method.