Vehicle sideslip angle estimation based on SVD-UPF algorithm

Vehicle sideslip angle is the key parameter to evaluate the handling stability of the vehicle, and it is also one of the control targets of the vehicle stability, so the accurate estimation of the vehicle sideslip angle directly affects the safety of the vehicle. In order to improve the influence of particles degeneracy on the estimation of vehicle sideslip angle, and ensure the nonnegative qualitative of the covariance matrix and iterative stability of the unscented Kalman filter algorithm, in this paper, a vehicle sideslip angle estimation method based on singular value decomposition Unscented Kalman particle filtering algorithm (SVD-UPF) is proposed by using two-degrees-of-freedom nonlinear vehicle dynamics model, and the singular value decomposition Unscented Kalman filter algorithm is used to optimize the density distribution function. Using the hardware-in-the-loop simulation platform of distributed drive electric vehicle (HEV), the method of estimating the vehicle sideslip angle based on the unscented Kalman filter(UKF) and the SVD-UPF algorithm is compared and verified under the working condition of emergency double lane change, steering angle increase gradually, steering wheel angle stepping. The results show that the SVD-UPF estimator improves the accuracy of the unscented Kalman filter estimator and the effectiveness of the algorithm is verified.