重庆理工大学学报2024，Vol.38Issue(5)：74-83,10.DOI:10.3969/j.issn.1674-8425(z).2024.03.008
奇异值分解五阶容积卡尔曼滤波汽车状态估计
Singular value decomposition fifth-order cubature Kalman filter for vehicle state estimation
摘要
Abstract
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.关键词
车辆动力学模型/状态估计/奇异值分解/五阶容积卡尔曼滤波Key words
vehicle dynamics model/state estimation/singular value decomposition/fifth-order cubature Kalman filter分类
交通工程引用本文复制引用
吴伟斌,黄靖凯,曾锦彬,李浩欣..奇异值分解五阶容积卡尔曼滤波汽车状态估计[J].重庆理工大学学报,2024,38(5):74-83,10.基金项目
广东省重点领域研发计划项目(2021B0101220003) （2021B0101220003）
广东省(深圳)数智农服产业园建设项目(FNXM012022020-1-03) （深圳）
