Real Time Estimation of Vehicle Quality and Road Slope Based on Adaptive Extended Kalman Filter

被引：0
作者：
Ren Z. [1 ]
Shen L. [1 ]
Huang W. [2 ]
Liu X. [1 ]
机构：
[1] School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou
[2] Fujian Special Equipment Inspection Institute, Fuzhou
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2020年 / 40卷 / 04期
关键词：
Adaptive extended Kalman filter(AEKF); Forgetting factor; Longitudinal dynamics model; Road gradient; Vehicle quality;
D O I：
10.16450/j.cnki.issn.1004-6801.2020.04.019
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the statistical characteristics of the external noise in the actual driving process of the vehicle cannot be known, based on the longitudinal dynamics model of the vehicle, the adaptive extended Kalman filter (AEKF) vehicle quality and road gradient estimate algorithm is proposed. Taking the dynamic estimation of the mass and slope of the vehicle system as the research object, the rotation mass conversion coefficient is introduced, the state space model of the vehicle longitudinal dynamic system is established, and the gear matching at different times and the handling of special driving conditions are considered. The system state equation is discretized to obtain the system state equation and the system measurement equation. Then, the noise statistical estimator with forgetting factor is introduced on the basis of the extended Kalman filter (EKF).The on-line estimation and correction of noise statistics are performed based on the real-time updating of the state equation and the measurement equation by adaptive extended Kalman filter, so as to solve the problem of time-varying noise of the system. The comparative analysis of the estimated and measured results of this algorithm and the EKF algorithm shows that the proposed algorithm can effectively filter and estimate the vehicle mass and gradient signals in the vehicle longitudinal dynamics model, and gradually converge and approach the measured value in a short time, so that it can be reasonably and effectively detect the status information of the vehicle during driving. © 2020, Editorial Department of JVMD. All right reserved.
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页码：758 / 764
页数：6
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