Enhancing Tire-Mounted Sensor Data Use for Load Estimation and Auto-Location

ABSTRACT Intelligent tires that are equipped with tire-mounted sensors have emerged as a transformative force in the automotive industry. These sensors provide real-time data on tire health, road conditions, and vehicle performance; however, their efficacy is constrained by battery-life limitations. To address this challenge, a range of strategies has been proposed in the literature to enhance the reliability and accuracy of intelligent tire systems. These strategies encompass edge computing, event-driven sensing, selective sampling, sensor fusion, and adaptive algorithms. By synergistically integrating these approaches, the output of intelligent tire systems across diverse applications can be optimized. We introduce a novel load estimation methodology that leverages a combination of these strategies. Initially, we present static tire load estimation algorithms alongside tire auto-location techniques. Subsequently, we propose a fusion strategy that unifies both algorithms, thereby yielding simultaneous results for static tire load and tire auto-location. Furthermore, we extend this approach to incorporate vehicle inertial measurement unit data that enables dynamic load estimation for each tire. Remarkably, this extension obviates the need for ongoing inputs from tire-mounted sensors during extended periods of driving. Validation results underscore the effectiveness, reliability, and accuracy of our proposed methodology, positioning it as a promising advancement in the field of intelligent tire systems.