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

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.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  Chao Huang,et al.  Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation , 2018, IEEE Access.

[3]  Alfonso Garcia-Cerezo,et al.  Kinematic and dynamic analysis of the McPherson suspension with a planar quarter-car model , 2013 .

[4]  Hermann Winner,et al.  Validation of vehicle dynamics simulation models – a review , 2014 .

[5]  Gi-Woo Kim,et al.  Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter , 2020 .

[6]  Jian Zhao,et al.  An Indirect TPMS Algorithm Based on Tire Resonance Frequency Estimated by AR Model , 2016 .

[7]  Jorge de Jesus Lozoya-Santos,et al.  Comparative Analysis in Indirect Tire Pressure Monitoring Systems in Vehicles , 2019, IFAC-PapersOnLine.

[8]  Seung-Bok Choi,et al.  Response time of magnetorheological dampers to current inputs in a semi-active suspension system: Modeling, control and sensitivity analysis , 2021 .

[9]  Richard W. Longman,et al.  On‐line identification of non‐linear hysteretic structural systems using a variable trace approach , 2001 .

[10]  Rolf Isermann,et al.  Indirect Vehicle Tire Pressure Monitoring with Wheel and Suspension Sensors , 2009 .

[11]  Zhihua Wang,et al.  Multiple Adaptive Fading Schmidt-Kalman Filter for Unknown Bias , 2014 .

[12]  Fredrik Gustafsson,et al.  Indirect Tire Pressure Monitoring using Sensor Fusion , 2002 .

[13]  M. Hoshiya,et al.  Structural Identification by Extended Kalman Filter , 1984 .

[14]  C. Loh,et al.  Time Domain Identification of Frames under Earthquake Loadings , 2000 .

[15]  Michael D. Gilchrist,et al.  Minimizing Distress on Flexible Pavements Using Variable Tire Pressure , 2001 .

[16]  L. L. Bashford,et al.  METHODS FOR MEASURING VERTICAL TIRE STIFFNESS , 2000 .

[17]  Zhenpo Wang,et al.  Longitudinal Vehicle Speed Estimation for Four-Wheel-Independently-Actuated Electric Vehicles Based on Multi-Sensor Fusion , 2020, IEEE Transactions on Vehicular Technology.

[18]  Li Zhou,et al.  An adaptive extended Kalman filter for structural damage identification , 2006 .

[19]  Gi-Woo Kim,et al.  Road roughness estimation based on discrete Kalman filter with unknown input , 2018, Vehicle System Dynamics.

[20]  Sachin C. Patwardhan,et al.  State and parameter estimation using extended Kitanidis Kalman filter , 2019, Journal of Process Control.

[21]  Xuemin Shen,et al.  Adaptive fading Kalman filter with an application , 1994, Autom..

[22]  Semiha Turkay,et al.  A study of random vibration characteristics of the quarter-car model , 2005 .

[23]  M. Kowalewski Monitoring and managing tire pressure , 2004, IEEE Potentials.

[24]  Paul Young,et al.  An insight into linear quarter car model accuracy , 2011 .

[25]  Paresh Date,et al.  Nonlinear Estimation , 2019 .

[26]  Chenglin Wen,et al.  Performance Analysis of the Kalman Filter With Mismatched Noise Covariances , 2016, IEEE Transactions on Automatic Control.

[27]  David G. Dorrell,et al.  A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation , 2020, IEEE Transactions on Industrial Informatics.