New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

被引：13
|
作者：
Lee, Dong-Hoon [1 ]
Yoon, Dal-Seong [1 ]
Kim, Gi-Woo [1 ]
机构：
[1] Inha Univ, Dept Mech Engn, Incheon 22212, South Korea
来源：
ELECTRONICS | 2021年 / 10卷 / 11期
关键词：
adaptive extended Kalman filter; sensor fusion; time-varying parameter estimation; tire stiffness; tire pressure monitoring system; unknown road roughness input; IDENTIFICATION; STATE; TIME;
D O I：
10.3390/electronics10111359
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new indirect tire pressure monitoring system (TPMS) based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested methodology is based on the explicit correlation between tire pressure and tire stiffness and is available in real time. AEKF-UI is used to simultaneously estimate the time-varying parameter (tire stiffness) of vehicle suspension systems and the road roughness using an unknown input estimator. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate tire stiffness (i.e., tire inflation pressure) variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm are verified through a laboratory-level experiment. This study offers a potential application for an alternative indirect TPMS and the estimation of unknown road roughness used for automotive controller design.
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收藏
页数：19
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