Intelligent Tire Model for Improving Ride and Dynamics of Vehicles 2019-01-0175

For virtual simulation of the vehicle attributes such as handling, durability, and ride, the accurate representation of pneumatic tire behavior is crucial. With the advancement in autonomous vehicles as well as in the development of driver assisted systems, there is an increased need for an intelligent tire (a tire that can identify the state of a vehicle). Integrating sensors into the inner liner of a tire has been proved to be the most promising way in extracting the real-time tire patch-road interface data that serves as a critical zone in developing control algorithms for an automobile. Researchers at Kettering University are developing an intelligent tire model (KU-iTire) that can predict the subsequent braking-traction requirement. This model is used to avoid slip condition at the interface by implementing artificial intelligence to process the acceleration signals perceived from an accelerometer installed in the inner liner on the tire. Abaqus® FEA has been used to replicate the test data and to study the non-linear behavior executed by a rolling tire. The strain variation at the contact under the three major situations (acceleration, braking, and free rolling) have been studied. This data is used to develop a robust algorithm which could provide real-time contact patch situation to the vehicle GPU in order to generate the optimum traction or braking force required thus avoiding the slip situation.