Title: A review on estimation of vehicle tyre-road friction

Authors: Zipeng Huang; Xiaobin Fan

Addresses: School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China

Abstract: The tyre-road friction coefficient (TRFC) is not only related to pavement conditions, but also affected by factors such as tyre material, tyre 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 summarises 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.

Keywords: vehicle state; tyre-road friction; Kalman filter; particle filter; Luenberger observer; nonlinear observer; tyre model; distributed drive; intelligent tyres; neural network.

DOI: 10.1504/IJHVS.2024.136242

International Journal of Heavy Vehicle Systems, 2024 Vol.31 No.1, pp.49 - 86

Received: 27 Mar 2023
Accepted: 28 Mar 2023
Published online: 24 Jan 2024 *

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