A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations

Area of Science:

• * Vehicle Dynamics and Sensor Technology
• * Automotive Engineering
• * Material Science

Background:

• * Tires can function as active sensors, offering novel methods for data acquisition.
• * Intelligent Tires equipped with strain sensors provide valuable vehicle dynamics information.
• * Estimating tire forces solely from contact patch deformations is an ongoing challenge.

Purpose of the Study:

• * To refine a strain-based methodology for intelligent tires to estimate all tire forces.
• * To develop a system that estimates tire parameters using only contact patch deformation data.
• * To validate the proposed estimation method using simulation software.

Main Methods:

• * Development of an algorithm using indoor test rig data to identify strain features and correlate them with tire parameters.
• * Implementation of a fuzzy logic system to estimate tire parameters based on contact patch information.
• * Validation of the estimation algorithm using CarSim simulation software for complex maneuvers and varying velocities.

Main Results:

• * A strain-based algorithm was developed to extract relevant features from tire deformation data.
• * A fuzzy logic system successfully estimated tire parameters using contact patch information.
• * The developed method demonstrated reliability when compared against CarSim simulation results for various driving conditions.

Conclusions:

• * The intelligent tire approach effectively estimates tire forces and parameters from contact patch strain data.
• * This methodology offers a reliable way to monitor tire behavior during different maneuvers and speeds.
• * The study validates the potential of intelligent tires for real-time vehicle dynamics monitoring.