Singular value decomposition fifth-order cubature Kalman filter for vehicle state estimation
To address the limited estimation accuracy of high-dimensional vehicle nonlinear model with third-order filtering, a fifth-order Cubature Kalman Filter vehicle state estimator based on singular value decomposition ( SVD-FCKF) is proposed for electric vehicles. Firstly, based on the Dugoff tire model, a high-dimensional nonlinear seven-degree-of-freedom vehicle dynamics model is built. Secondly, CKF is extended to the fifth order according to the third-order sphere-radial volume rule, so that it has the fifth-order Taylor series expansion precision, and the singular value decomposition is employed to replace the traditional Cholesky decomposition to improve the robustness of the estimator. Finally, Carsim and Matlab/Simulink co-simulation platform are used to verify SVD-FCKF. Our results show the improved SVD-FCKF estimator effectively improves the estimation accuracy and stability of longitudinal speed, lateral speed, centroid sideslip angle and four-wheel speed of electric vehicles, and has strong adaptability to multiple working conditions. And the overall estimation is superior to that of CKF estimator. Our research may provide some theoretical support for the study of electric vehicles' active safety.
vehicle dynamics modelstate estimationsingular value decompositionfifth-order cubature Kalman filter
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