Estimation of Vehicle Status and Parameters Based on Nonlinear Kalman Filtering

From the perspective of economy and engineering practicability, due to the existing mass-produced on-board sensor measurement accuracy is not high or difficult to measure at low cost, some car status parameters can not be obtained directly through the sensor, therefore, in order to accurately and real-time acquisition of vehicle motion state information, this paper is based on a nonlinear three-degree of freedom vehicle model, respectively, using the unscented Kalman filter algorithm and the extended Kalman filter algorithm to estimate longitudinal speed, centroid slip angle, swing angle velocity. Simulink and Carsim were used for co-simulation. The results show that the effect of the extended Kalman filter algorithm in estimating longitudinal speed and swing angle velocity is not much different from the unscented Kalman filter, and the unscented Kalman filtering algorithm could more accurately track the centroid slip angle of the vehicle.