Keynote Abstract

The transition toward electrification and software-defined vehicles (SDVs), coupled with the expansion of mobility platforms beyond the traditional passenger car and SUV segments are transforming the tire development paradigm. To address the increasingly demanding requirements related to high loads, high torque, energy efficiency, and real-time data utilization, tire engineering must evolve from a conventional, testing-centered linear process into a closed-loop framework enabled by Connected Data and Digital Twins. This presentation explores this transformation through four key dimensions: performance optimization, virtual tire development, real-time data-driven Predictive Health Management (PHM), and vehicle control enhancement for diverse vehicle platforms.

Emerging vehicle platforms impose new requirements that differ substantially from those of conventional vehicles. In electrified architectures, purpose-built vehicles (PBVs) and other high-load applications, increased vehicle mass and instantaneous drive torque significantly accelerate tire wear. Simultaneously, demands for improved energy efficiency and extended driving range continue to grow. These factors become even more challenging when stringent regulatory requirements, including CO2 reduction, noise limits, and upcoming wear regulations are added. Consequently, tire development must achieve a delicate balance between wear durability, load-carrying capacity, rolling resistance, and exterior noise reduction.

Virtual tire development is emerging as a critical enabler for resolving these complex and often conflicting demands. By leveraging digital twin-based tire models and advanced simulation technologies, engineers can front-load design maturity and enhance the reliability of performance predictions through stronger correlations with physical test results. This approach significantly reduces prototype iterations, shortens development times, lowers costs, and mitigates the risk of late-stage design changes. Ultimately, virtual development elevates tire engineering from component-level assessment to a foundational element of overall vehicle program efficiency.

In the context of autonomous driving and SDV environments, tire-specific databases and real-time vehicle data create a new link between development and in-service operation. Real-time data facilitates accurate tire condition estimation, performance degradation forecasting, and maintenance optimization. This not only advances PHM but also enhances steering, braking, and traction control by integrating tire state information directly into vehicle control strategies. In this sense, the tire is evolving from a passive consumable into an intelligent system component that actively contributes to vehicle safety and performance.

This presentation proposes a closed-loop tire development framework that integrates the evolving requirements of diverse mobility platforms, design innovation via virtual engineering, and data-driven PHM in the SDV era. It demonstrates how next-generation tire development can become faster, more robust, more predictive, and more intelligent.