Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-5752
Main Title: A hierarchical estimator development for estimation of tire-road friction coefficient
Author(s): Zhang, Xudong
Göhlich, Dietmar
Type: Article
Language Code: en
Abstract: The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.
URI: http://depositonce.tu-berlin.de/handle/11303/6187
http://dx.doi.org/10.14279/depositonce-5752
Issue Date: 8-Feb-2017
Date Available: 23-Feb-2017
DDC Class: DDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): roads
algorithms
covariance
friction
wheels
acceleration
torque
steering
Sponsor/Funder: DFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
Journal Title: PLoS ONE
Publisher: PLoS
Publisher Place: San Francisco
Volume: 12
Issue: 2
Article Number: e0171085
Publisher DOI: 10.1371/journal.pone.0171085
EISSN: 1932-6203
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 5 Verkehrs- und Maschinensysteme » Institut für Konstruktion, Mikro- und Medizintechnik » Fachgebiet Methoden der Produktentwicklung und Mechatronik » Publications

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