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