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Journal of Engineering
Volume 2018, Article ID 4143794, 11 pages
Research Article

Neural Network Identification of a Racing Car Tire Model

1School of Automotive Engineering, Harbin Institute of Technology, Weihai, Shandong 264209, China
2State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China

Correspondence should be addressed to Yiqun Liu; moc.361@wen.qyl and Liang Ding; nc.ude.tih@gnidgnail

Received 3 August 2017; Accepted 16 April 2018; Published 29 May 2018

Academic Editor: Kamran Iqbal

Copyright © 2018 Jianfeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to meet the demands of small race car dynamics simulation, a new method of parameter identification in the Magic Formula tire model is presented in this work, based on an analysis of the Magic Formula tire model structure. A high-precision tire model used for vehicle dynamics simulation is established via this method. It is difficult for students to build a high-precision tire model because of the complexity of widely used tire models such as Magic Formula and UniTire. At a pure side slip condition, building a lateral force model is an example, which illustrate the utilization of a multilayer feed-forward neural network to build an intelligent tire model conveniently. In order to fully understand the difference between the two models, a two-degrees-of-freedom (2 DOF) vehicle model is established. The advantages, disadvantages, and applicable scope of the two tire models are discussed after comparing the simulation results of the 2 DOF model with the Magic Formula and intelligent tire model.