Adhesion state estimation based on improved tire brush model

On the basis of calculating the longitudinal force using the original brush model, we simplify the tire structure and consider the lateral force generated by the lateral elasticity of the tread. At the same time, the boundary conditions between the adhesion area and the slip zone in the contact area...

Full description

Bibliographic Details
Main Authors: Bei Shaoyi, Li Bo, Zhu Yanyan
Format: Article
Language:English
Published: SAGE Publishing 2018-01-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814017747706
id doaj-6293bad46f074739befb6d8557e5f8cf
record_format Article
spelling doaj-6293bad46f074739befb6d8557e5f8cf2020-11-25T02:50:41ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-01-011010.1177/1687814017747706Adhesion state estimation based on improved tire brush modelBei ShaoyiLi BoZhu YanyanOn the basis of calculating the longitudinal force using the original brush model, we simplify the tire structure and consider the lateral force generated by the lateral elasticity of the tread. At the same time, the boundary conditions between the adhesion area and the slip zone in the contact area of the tire are fully discussed. By establishing an improved tire brush model, the error caused by neglecting the sideslip characteristics is avoided, and the adaptability of the tire model is improved. A double nonlinear compensation method based on the lateral acceleration deviation and the yaw rate deviation is employed to estimate the road adhesion coefficient, which is closer to the actual attachment situation than the standard calculation. Based on this model, the vehicle stability coefficient k is defined and calculated to describe the stability of the vehicle during the driving process. The modeling results show that the value of k is always in the stable range of [0, 1]. Therefore, the vehicle that utilizes the improved tire brush model is always within the controllable range in the driving process, which verifies the effectiveness of the model.https://doi.org/10.1177/1687814017747706
collection DOAJ
language English
format Article
sources DOAJ
author Bei Shaoyi
Li Bo
Zhu Yanyan
spellingShingle Bei Shaoyi
Li Bo
Zhu Yanyan
Adhesion state estimation based on improved tire brush model
Advances in Mechanical Engineering
author_facet Bei Shaoyi
Li Bo
Zhu Yanyan
author_sort Bei Shaoyi
title Adhesion state estimation based on improved tire brush model
title_short Adhesion state estimation based on improved tire brush model
title_full Adhesion state estimation based on improved tire brush model
title_fullStr Adhesion state estimation based on improved tire brush model
title_full_unstemmed Adhesion state estimation based on improved tire brush model
title_sort adhesion state estimation based on improved tire brush model
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2018-01-01
description On the basis of calculating the longitudinal force using the original brush model, we simplify the tire structure and consider the lateral force generated by the lateral elasticity of the tread. At the same time, the boundary conditions between the adhesion area and the slip zone in the contact area of the tire are fully discussed. By establishing an improved tire brush model, the error caused by neglecting the sideslip characteristics is avoided, and the adaptability of the tire model is improved. A double nonlinear compensation method based on the lateral acceleration deviation and the yaw rate deviation is employed to estimate the road adhesion coefficient, which is closer to the actual attachment situation than the standard calculation. Based on this model, the vehicle stability coefficient k is defined and calculated to describe the stability of the vehicle during the driving process. The modeling results show that the value of k is always in the stable range of [0, 1]. Therefore, the vehicle that utilizes the improved tire brush model is always within the controllable range in the driving process, which verifies the effectiveness of the model.
url https://doi.org/10.1177/1687814017747706
work_keys_str_mv AT beishaoyi adhesionstateestimationbasedonimprovedtirebrushmodel
AT libo adhesionstateestimationbasedonimprovedtirebrushmodel
AT zhuyanyan adhesionstateestimationbasedonimprovedtirebrushmodel
_version_ 1724737175082237952