Intelligent tires, as an emerging technology, have great potential for tire-road contact information identification and new vehicle active safety system design. In this article, a tire-road friction coefficient estimation method is proposed based on intelligent tires application with three-axis accelerometer. At first, a finite element tire model with an accelerometer is established using ABAQUS platform. Accelerometer body frame transformation is considered during the tire rotation. Subsequently, the contact patch length is determined according to the peak of the longitudinal acceleration profile. Meanwhile, tire lateral deflection is calculated from the tire lateral acceleration. By curve fitting the lateral deflection model with least square method, tire lateral force and the aligning moment are derived and then the friction coefficient is estimated via brush model. Finally, the effectiveness and accuracy of the proposed estimation method are verified through computer simulation conducted under different road surface conditions.

Tire-Road Friction Coefficient Estimation Method Design for Intelligent Tires Equipped with Three-Axis Accelerometer

Lenzo B.
2021

Abstract

Intelligent tires, as an emerging technology, have great potential for tire-road contact information identification and new vehicle active safety system design. In this article, a tire-road friction coefficient estimation method is proposed based on intelligent tires application with three-axis accelerometer. At first, a finite element tire model with an accelerometer is established using ABAQUS platform. Accelerometer body frame transformation is considered during the tire rotation. Subsequently, the contact patch length is determined according to the peak of the longitudinal acceleration profile. Meanwhile, tire lateral deflection is calculated from the tire lateral acceleration. By curve fitting the lateral deflection model with least square method, tire lateral force and the aligning moment are derived and then the friction coefficient is estimated via brush model. Finally, the effectiveness and accuracy of the proposed estimation method are verified through computer simulation conducted under different road surface conditions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3402865
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