Tire Thermal Model with State Observer Integration for Enhanced Real-Time Temperature Prediction

This paper focuses on the development of a tire thermal model for automotive applications, addressing the challenge of accurately predicting tire temperatures on different layers of the tire, under various driving conditions. The primary goal is to enhance the understanding of tire temperature behavior to improve safety, performance, and durability. The research utilizes a physics 1-D model for the tire, from which a system of differential equations, describing the interaction between different layers of the tire, is derived. Furthermore, a state observer is used to estimate tire temperatures, using Tire Pressure Monitoring System (TPMS) measurements to correct model predictions. In particular, the TPMS measurements are assumed to be sufficient to exclude the additional thermal contributions coming from the rims and disk brakes, which simplifies the model, making it more suitable for real-time applications. A calibration procedure is defined for deriving the model parameters, based on data collected in different driving maneuvers. For the model calibration and validation, the predicted tread surface temperatures have been compared with infrared sensors’ measurements. The final model demonstrates how temperature can differ across different tire layers. Furthermore, the use of a non-linear state observer is crucial to correct the physical model outputs. The study concludes that these methodologies can be further refined and extended to develop more comprehensive tire models, with future work focusing on automated tuning processes, exploration of alternative filtering techniques, and the application of global optimization algorithms to achieve even more precise and reliable results.