Neural Network Identification of a Racing Car Tire Model

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...

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Bibliographic Details
Published in:Journal of engineering (Cairo, Egypt) Egypt), 2018-01, Vol.2018 (2018), p.1-11
Main Authors: Sun, Tianyao, Gao, Haibo, Li, Jun, Ding, Liang, Liu, Yiqun, Wang, Jianfeng, Liang, Yuhan
Format: Article
Language:English
Subjects:
Computer simulation
Consortia
Degrees of freedom
Dynamic structural analysis
Multilayers
Neural networks
Parameter identification
Pressure distribution
Race cars
Racing
Roads & highways
Simulation
Tires
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Summary: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.
ISSN:2314-4904
2314-4912
2314-4912
DOI:10.1155/2018/4143794