Intelligent Tire Prototype in Longitudinal Slip Operating Conditions.

Area of Science:

• * Mechanical Engineering
• * Automotive Engineering
• * Sensor Technology

Background:

• * Autonomous vehicles require advanced sensors for real-time monitoring of tire-road conditions and forces.
• * The tire contact patch is critical for vehicle dynamics (acceleration, braking, steering).
• * Road irregularities cause contact patch fluctuations, potentially leading to hazardous driving situations.

Purpose of the Study:

• * To develop and validate a sensor-integrated tire for real-time tire-to-road contact data.
• * To investigate strain variations within the tire contact patch under various driving and loading conditions.
• * To correlate finite element analysis (FEA) results with experimental data from an intelligent tire prototype.

Main Methods:

• * Development of a tire model using Abaqus/CAE and analysis with Abaqus/Explicit for nonlinear behavior.
• * Simulation of strain variations in acceleration, braking, and free-rolling scenarios under different vertical loads.
• * Experimental validation using the KU-iTire prototype under similar operating and loading conditions.

Main Results:

• * Observed correlation between FEA and experimental testing for strain shape across free-rolling, acceleration, and braking conditions.
• * Identified a relationship between peak longitudinal strain and vertical load during free-rolling.
• * Demonstrated the effectiveness of the intelligent tire in providing reliable contact patch strain data.

Conclusions:

• * FEA simulations accurately predict tire contact patch strain variations compared to experimental data.
• * The KU-iTire prototype successfully captures real-time strain data, validating the simulation model.
• * This research provides a foundation for enhanced tire monitoring systems in autonomous vehicles.