The development of intelligent tire technology has attracted increasing attention from researchers to build different tire models to obtain the state parameters of the tire and to try to correlate these parameters with sensors. To address the challenge of characterizing the evolution of wear in traditional tire mechanics models, this study proposes a physical model that incorporates tire wear. The model is an improvement over the traditional flexible ring model, incorporating brush theory. By establishing the mechanical equilibrium equation of the tread unit, a tire dynamic equation incorporating wear state variables is constructed. The strain–displacement relationship is analyzed to determine the correlation between the strain field and the displacement field. The results show that the strain signals obtained from the physical model and the finite element model maintain a high degree of consistency, validating the reliability and effectiveness of the proposed model. In addition, the correlation between the tire wear and strain signal characteristics was successfully revealed by comparing the physical model and the finite element model. The proposed model provides a theoretical foundation for future research on intelligent tires, as well as a basis for related studies on tire wear, tire lifespan, and tire mechanical properties.

Tire force is a critical state parameter for vehicle dynamics control systems during vehicle operation. Compared with tire force estimation methods relying on vehicle dynamics or tire models, intelligent tire technology can provide real-time feedback regarding tire–road interactions to the vehicle control system. To address the demand for accurate tire force prediction in active safety control systems under various operating conditions, this paper proposes an intelligent tire force estimation method, integrating sensor-measured dynamic response parameters and machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and a finite element model was established using the parameterized modeling approach with the ABAQUS finite element simulation software. The validity of the finite element model was verified through indoor static contact and stiffness tests. To investigate the sensitive response areas and variables associated with tire force, the ground deformation area of the inner liner was refined along the transverse and circumferential directions. Variance-based global sensitivity analysis combined with dimensional reduction methods was used to evaluate the sensitivity of acceleration, strain, and displacement responses to variations in longitudinal and lateral forces. Based on the results of the global sensitivity analysis, the influence of longitudinal and lateral forces on sensitive response variables in their respective sensitive response areas was examined, and characteristic values of the corresponding response signal curves were analyzed and extracted. Three intelligent tire force estimation models with different sensor-targeting configurations were established using radial basis function (RBF) neural networks. The mean relative error (MRE) of intelligent tire force estimation for these models remained within 10%, with Model 3 demonstrating an MRE of less than 2% and estimation errors of 1.42% and 1.10% for longitudinal and lateral forces, respectively, indicating strong generalization performance. The results show that tire forces exhibit high sensitivity to acceleration and displacement responses in the crown and sidewall areas, providing methodological guidance for the targeted sensor configuration in intelligent tires. The intelligent tire force estimation method based on the RBF neural network effectively achieves accurate estimation, laying a theoretical foundation for the advancement of vehicle intelligence and technological innovation.