My Account Log in

1 option

Enhancing Tire Predictive Maintenance with Next-Generation TPMS Sensors Goodyear SA

SAE Technical Papers (1906-current) Available online

View online
Format:
Book
Conference/Event
Author/Creator:
Sharma, Sparsh, author.
Contributor:
Son, Roman
Conference Name:
WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2025
Summary:
This paper presents findings on the use of data from next-generation Tire Pressure Monitoring Systems (TPMS), for estimating key tire states such as leak rates, load, and location, which are crucial for tire-predictive maintenance applications. Next-generation TPMS sensors provide a cost-effective and energy-efficient solution suitable for large-scale deployments. Unlike traditional TPMS, which primarily monitor tire pressure, the next-generation TPMS used in this study includes an additional capability to measure the tire's centerline footprint length (FPL). This feature offers significant added value by providing comprehensive insights into tire wear, load, and auto-location. These enhanced functionalities enable more effective tire management and predictive maintenance. This study collected vehicle and tire data from a passenger car hatchback equipped with next-generation TPMS sensors mounted on the inner liner of the tire. The data was analyzed to propose vehicle-tire physics-inspired algorithms that can be solved using Recursive Least Squares (RLS), which are computationally light and memory-efficient, making them suitable for both embedded and cloud-native environments. The results demonstrate the proposed algorithms' accuracy in estimating tire leak rates, load, and auto-location. The findings suggest that next-generation TPMS sensors with footprint measurement capabilities are preferable for large-scale deployments in commercial fleet operations and passenger vehicles, offering customers a cost-effective alternative for tire predictive maintenance applications
Notes:
Vendor supplied data
Publisher Number:
2025-01-8759
Access Restriction:
Restricted for use by site license

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Library Catalog Using Articles+ Library Account