Tire rolling resistance significantly influences vehicle fuel efficiency and environmental impact. Traditional methods for predicting rolling resistance rely on physical testing or physics-based simulation, which can be time-consuming. This study explores machine learning techniques, to predict tire rolling resistance with high accuracy and efficiency. Traditional machine learning models were used for predicting rolling resistance by leveraging large datasets comprising tire design parameters, material properties and operational conditions. The results demonstrate that AI-based approaches not only reduce the need for extensive physical testing but also enable real-time prediction and optimization during the tire design process.

• Rolling resistance
• Machine learning
• AI