Enhancing Tire Predictive Maintenance with Next-Generation TPMS Sensors

<div class="section abstract"><div class="htmlview paragraph">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.</div></div>