Hybrid Friction Estimation based on Intelligent Tires and Vehicle Dynamics

The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Gupta, Utkarsh

Chair dc:contributor.committeechair

• Taheri, Saied

Committee members dc:contributor.committeemember

• Sandu, Corina
• Lee, Hojong
• Ferris, John B.

• Intelligent Tires
• Vehicle Dynamics
• Tire-Road Interactions
• Road Surface
• Coefficient of Friction
• Brush Tire Model
• Tire Slip Estimation

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:38366

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/116110