State estimation of distributed electric vehicle based on robust adaptive UKF

The key to achieving active safety and automatic driving is accurate parameters of the vehicle state. The unscented Kalman filter (UKF) algorithm will seriously affect the vehicle state's estimation accuracy if the noise of measurement is high or the covariance of noise does not suit. A robust adaptive UKF algorithm based on fault detection mechanism is proposed to estimate the state of distributed drive electric vehicle. The algorithm uses the residual vector of observation noise to identify whether there is a fault in the system, determines whether the observation noise covariance and the process noise covariance need to be adjusted adaptively according to the statistical function, and updates the covariance based on the weight factors. A robust adaptive UKF estimator is designed to estimate three important state variables: longitudinal speed, lateral speed, and sideslip angle. The algorithm is eventually tested based on the co-simulation of CarSim and MATLAB/Simulink. The results showed that the proposed robust adaptive UKF algorithm can significantly reduce the estimation error of three state variables, and is better than the standard accuracy and robustness of the UKF algorithm. This lays an important foundation for advanced driving assistance systems and precise automatic driving movement control. © 2020, Science Press. All right reserved.