A spoke strain-based method to estimate tire condition parameters for intelligent tires

Abstract

Previous research on intelligent tires has shown that strain gauges are suitable for measuring the dynamic characteristics of tires. However, the process of mounting these gauges onto the tire tread and connecting the necessary wiring is relatively challenging. Furthermore, the high-speed rotation of the tire can generate significant heat and noise, which can adversely affect signal acquisition. With the aim of developing an innovative intelligent tire system, we endeavored to install strain gauges onto the spokes. Through analyzing the spokes' deformation characteristics in various driving conditions and directions (radial, longitudinal, and lateral), we determined the strain-sensitive areas and characteristic points on the spokes. Estimation models for tire forces, slip ratio, and slip angle were constructed successfully by considering the relationship between radial force, longitudinal force, lateral force, and strain on the spokes. Our simulation experiments validated these models. Unlike previous models for intelligent tires, the proposed estimation model achieves high accuracy without requiring complex algorithms to estimate tire condition parameters. Furthermore, incorporating strain gauges onto the spokes removes numerous superfluous complexities. The addition of this feature broadens the study scope of intelligent tires and presents novel insights for potential future advancements.