Comparative Study and Real-World Validation of Vertical Load Estimation Techniques for Intelligent Tire Systems.

Area of Science:

• Automotive Engineering
• Sensor Technology
• Vehicle Dynamics

Background:

• Accurate vertical load measurement is vital for vehicle stability, handling, and safety.
• Existing research often lacks comparative analysis of real-world sensor performance under dynamic driving conditions.

Purpose of the Study:

• To compare sensor performance for vertical load prediction using finite element modeling (FEM) and bench tests.
• To validate vertical load prediction algorithms under diverse real-road driving scenarios.
• To assess the product-level implementation of an Intelligent Tire Test Unit (ITTU).

Main Methods:

• Finite element modeling (FEM) to evaluate accelerometer and strain-based sensors.
• Quantitative comparison of triaxial Integrated Electronics Piezoelectric (IEPE) accelerometers and Polyvinylidene Fluoride (PVDF) sensors on a high-precision test bench.
• Development and validation of Support Vector Machine (SVM) and linear regression algorithms for vertical load prediction using contact length, vehicle speed, and tire pressure.
• Real-road validation across constant speed, acceleration, braking, and cornering maneuvers.
• Deployment of a self-designed Intelligent Tire Test Unit (ITTU).

Main Results:

• Accelerometers demonstrated superior stability and linearity compared to PVDF sensors.
• Vertical load prediction algorithms showed effectiveness under various driving conditions.
• The Intelligent Tire Test Unit (ITTU) confirmed its viability for real-world applications.

Conclusions:

• A validated framework for accurate vertical load estimation in intelligent tires has been established.
• The study provides practical insights for real-time tire parameter prediction in dynamic driving.
• Accelerometer-based intelligent tire technology offers enhanced accuracy and reliability for vehicle safety.