State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

作     者：Xiaofei Pei Xu Hu Wei Liu Zhenfu Chen Bo Yang 

作者机构：School of Automotive EngineeringWuhan University of TechnologyWuhan 430070China Wanxiang Qianchao CorporationHangzhou 311215China 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2018年第1卷第3期

页      面：281-289页

核心收录：

学科分类：07[理学] 0802[工学-机械工程] 0701[理学-数学] 0823[工学-交通运输工程] 070101[理学-基础数学] 

基　　金：the 111 Project(Grant No.B17034) National Natural Science Foundation of the People’s Republic of China(Grant No.51505354) 

主　　题：Vehicle dynamic State estimation EKF UKF Vehicle test 

摘      要：An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability *** different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability ***,the overall structure of the state estimation with four inputs and four outputs is *** determining tire-cornering stiffness using a recursive leastsquares(RLS)method,the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover ***,the specific steps of real-time state estimation using the extended Kalman filter(EKF)and unscented Kalman filter(UKF)are both *** a co-simulation,we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly,and the UKF improves the effect of state estimation compared with *** addition,the UKF is verified against data from vehicle *** results show the proposed method is reliable and practical in estimating vehicle states.