A review on estimation of vehicle tyre-road friction
The tire-road friction coefficient (TRFC) is not only related to pavement conditions, but also affected by factors such as tire material, tire pressure and ambient temperature; in addition, there are problems such as sensor measurement noise, signal transmission hysteresis, parameter uncertainty or time denaturation in the actual vehicle system. These problems make the real-time robust estimation of the friction coefficient and its stability analysis more complicated, Therefore, the identification of TRFC has always been a key topic and difficult issue in research. This paper provides a comprehensive technical review of the currently widely used TRFC estimation method. First, various filters and observers and their improved versions to solve different problems are introduced. Then the model-based estimation algorithm is comprehensively expounded. The paper summarizes the research results of sensor-based and neural network-based methods, analyses the new method brought about by the structural characteristics of distributed drive electric vehicles to estimate the friction coefficient, and looks forward to the future development direction.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/1744232X
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Supplemental Notes:
- Copyright © 2024 Inderscience Enterprises Ltd.
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Authors:
- Huang, Zipeng
- Fan, Xiaobin
- Publication Date: 2024
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 49-86
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Serial:
- International Journal of Heavy Vehicle Systems
- Volume: 31
- Issue Number: 1
- Publisher: Inderscience Enterprises Limited
- ISSN: 1744-232X
- EISSN: 1741-5152
- Serial URL: http://www.inderscience.com/jhome.php?jcode=IJHVS
Subject/Index Terms
- TRT Terms: Electric vehicles; Mathematical methods; Rolling contact; Rolling friction; Roughness; Surface course (Pavements)
- Subject Areas: Highways; Pavements; Vehicles and Equipment;
Filing Info
- Accession Number: 01912877
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 22 2024 5:04PM