Can I read A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning on EtoBox?

A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning by Xu, Nan; Tang, Zepeng; Askari, Hassan; Zhou, Jianfeng; Khajepour, Amir is a scholarly article available to read on EtoBox.

What is A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning about?

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the slip ratio reference value can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, machine learning (ML) algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent t

Author

Xu, Nan; Tang, Zepeng; Askari, Hassan; Zhou, Jianfeng; Khajepour, Amir

Published

2021

Language

EN