Abstract
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.
Highlights
The Formula Society of Automotive Engineers (FSAE) race car’s higher speed and greater cornering acceleration make tire characteristics significantly influence the vehicle’s performance [1,2,3]
In 1954, Fiala conducted an analysis and performed various experiments pertaining to cornering characteristics and proposed a dimensionless formula of tire characteristics based on a simplified analytical tire model [5]
The Magic Formula tire model and neural network intelligent tire model depend on the test data of the tire
Summary
The Formula Society of Automotive Engineers (FSAE) race car’s higher speed and greater cornering acceleration make tire characteristics significantly influence the vehicle’s performance [1,2,3]. In 1987, Pacejka and Bakker established a semiempirical tire model that can accurately calculate the lateral force and aligning moment, which is one of the findings of the cooperation project between TU Delft and Volvo [11]. A multiple-step parameter identification method is proposed, based on the analysis of the characteristics of the Magic Formula tire model structure. According to this method, all coefficients of Pacejka 2002 [12] have been obtained after fitting all the Hoosier 18x6R25B tire data at 14psi. The same pure cornering condition test data is fitted using a three-layer feed-forward neural network In this manner, a simplified intelligent tire model is built.
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