Development and simulation of a novel mathematical model for an intelligent tire system toward predictive maintenance

An intelligent tire system autonomously collects and transmits key data on tire and road conditions. As vehicle performance depends on tire dynamics governed by tire-road interactions, accurate tire modeling is essential for optimizing overall performance. Tire deformations significantly influence longitudinal and lateral forces, emphasizing the need for accurate predictive models. The growth of autonomous vehicles (AVs), electric vehicles (EVs), and shared mobility has intensified research on tire-vehicle interactions. EVs, in particular, place special demands on tires due to their heavier battery packs, immediate torque output, and regenerative braking, which can increase tire wear by 20-30% compared to traditional vehicles. Smart tire models have been widely explored to improve vehicle control systems, as embedded sensors provide valuable real-time data on tire behavior under actual driving conditions. These models seek to link measured parameters with physical states to improve prediction accuracy. This paper introduces a physical-analytical approach to describe in-plane tire dynamics by connecting deformations to the forces involved. Tire parameters are determined by aligning radial deformation data from a flexible ring model with strain-based simulations. The proposed mathematical model, based on carcass strain and displacement data, provides a solid theoretical foundation for future intelligent tire research and aids further studies on tire wear, lifespan, mechanical performance, and predictive maintenance. This work offers a theoretical foundation for intelligent tire systems, solutions that significantly reduce computational effort compared to numerical methods, and a potential direct indicator for wear estimation algorithms.