Parameter Identification and Sensitivity Analysis of Heavy-duty Vehicle Tire Model

被引：0
|
作者：
Huang T. [1 ]
Liu Z. [1 ]
Gao Q. [1 ]
Wang D. [1 ]
Ma D. [1 ]
机构：
[1] College of Missile Engineering, Rocket Force University of Engineering, Xi’an
来源：
Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2022年 / 49卷 / 08期
基金：
中国国家自然科学基金;
关键词：
hybrid optimization algorithm; parameter identification; sensitivity analysis; six component force test; tire model;
D O I：
10.16339/j.cnki.hdxbzkb.2022183
中图分类号：
学科分类号：
摘要：
In order to explore the influence of heavy-duty tire performance on multi-axle heavy vehicle driving characteristics, the model parameters of the GL073A heavy-duty meridian tire were identified based on a six-component tire test. Aiming at the characteristics of strong nonlinear changes caused by multiple parameters of MF (Magic Formula) model and large vertical load range of heavy radial tire, a hybrid optimization parameter identification method based on Particle Swarm Optimization (PSO) and Levenberg-Marquardt algorithm was proposed. The parameter identification and result verification of the tire model under longitudinal slip and sidesway conditions of the heavy-load radial tire were carried out. The results show that the parameter identification accuracy of the tire model can be improved based on the hybrid optimization algorithm, and the residual of the identification results can be controlled in a range of 5%. Based on the Sobol sensitivity analysis method, the influence of multi-characteristic parameters on the tire MF model was studied. The first-order sensitivity and total order sensitivity of each characteristic parameter were used as evaluation criteria to screen out the leading parameters affecting tire mechanical properties. The results show that based on Sobol sensitivity analysis, 13 leading parameters are selected from 58 characteristic parameters of the magic formula tire model. Compared with the results of direct hybrid optimization, the maximum increase of residual error is 0.138%, and the maximum increase of model convergence rate is 30.4%. © 2022 Hunan University. All rights reserved.
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页码：36 / 44
页数：8
相关论文
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