基于自适应无迹卡尔曼滤波的分布式驱动电动汽车车辆状态参数估计

A vehicle state parameter estimation algorithm based on adaptive unscented Kalman filter (AUKF) was proposed to estimate vehicle state parameters accurately. Taking a nonlinear three freedom vehicle model as object, the fuzzy control algorithm and the unscented Kalman filter algorithm were combined to realize the adaptive adjustment of the system measurement noise. The sensor information about steering wheel angle, longitudinal acceleration and lateral acceleration were synthesized to realize the estimation of side slip angle and yaw rate. CarSim and Matlab/Simulink were used to establish the distributed driving electric vehicle model and the effectiveness of the algorithm was verified by simulation. The results show that the adaptive unscented Kalman filter is more effective and accurate than the unscented Kalman filter to estimate the parameters of the vehicle. In the double lane conditions, side slip angle estimation accuracy is improved by 6.7%, and the yaw rate estimation accuracy is improved 4.8%.