Joint Observation of Vehicle States and Parameters Based on Unscented Kalman Filtering
In order to improve the control performance of distributed driving electric vehicles,and for the situation that some vehicle state parameters cannot be directly measured by sensors,this paper used unscented Kalman filters to design a nonlinear observer for vehicle state and parameter coupling,and estimated the vehicle state and actuator failure coefficients. The nonlinear vehicle dynamic model was established,so that the motor fault diagnosis problem was transformed into a real-time parameter estimation problem. The yaw speed and vehicle speed were estimated in real time by UKF (Unscented Kalman Filter). Finally,the Carsim/Simulink co-simulation was used to verify the problem. The simulation results show that the observer can accurately estimate the above related vehicle states and parameters,which verifies that the estimation algorithm has high real-time performance and accuracy.
distributed driving electric vehiclesparameter estimationUKFfault diagnosis
万方数据
维普
