Abstract
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.
Highlights
Tire pressure can significantly affect vehicle stability and fuel consumption
The National Highway Transport Safety Administration (NHTSA) started investigation on the implementation of tire pressure warning systems in vehicles and stipulated that since 2008, all passenger cars and trucks manufactured in or imported into the United States are required to be equipped with tire pressure monitoring system (TPMS)
The vertical tire stiffness kt is the unknown parameter, which is modeled as the state variable for random walk model [17]. This state-space model can be represented as a global structure x. = f (x, u∗, w) y = h(x, u∗, v) where x is the state variable vector defined in (3); w is the process noise; v is the measurement noise; f is the system function; u* is the unknown input for the system; h is the output function; y is the measurable output; x..b and x..w are the sprung and unsprung mass accelerations, respectively. These acceleration signals can be acquired from the controller area network (CAN) bus, which is the central nervous system of a modern vehicle, upon which the majority of intra-vehicular communication takes place [18,19]
Summary
Tire pressure can significantly affect vehicle stability and fuel consumption. the electronic tire pressure monitoring system (TPMS) used to monitor tire inflation pressure (nominally 32–35 psi), is rapidly becoming an essential electronic system in most vehicles. Many researchers have attempted to improve the performance of TPMS, with a primary focus on the use of robust pressure sensor, antenna design and lower power consumption [3]. Where x is the state variable vector defined in (3); w is the process noise; v is the measurement noise; f is the system function; u* is the unknown input for the system; h is the output function; y is the measurable output; x..b and x..w are the sprung and unsprung mass accelerations, respectively These acceleration signals can be acquired from the controller area network (CAN) bus, which is the central nervous system of a modern vehicle, upon which the majority of intra-vehicular communication takes place [18,19]
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