重载车辆轮胎模型参数辨识与灵敏度分析

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

为了探究重载轮胎性能对多轴重型车辆行驶特性的影响,基于轮胎六分力测试试验,对GL073A型重载子午轮胎的力学模型参数进行辨识研究.针对魔术公式轮胎模型参数多,重载子午轮胎垂向载荷范围大所导致的强非线性变化的特点,提出一种基于粒子群算法和Levenberg-Marquardt算法的混合优化参数辨识方法,以轮胎六分力测试试验数据为基础,对该型重载子午轮胎的纵滑和侧偏工况的轮胎模型进行参数辨识和结果验证.结果表明,基于混合优化算法能够提高轮胎模型的参数辨识精度,辨识结果残差控制在5%以内.基于Sobol灵敏度分析方法研究了多特征参数对魔术公式轮胎模型的影响程度,以各特征参数的一阶灵敏度和总阶灵敏度作为评价标准筛选影响轮胎力学性能的主导参数.结果表明,基于Sobol灵敏度分析,从魔术公式轮胎模型的58个特征参数中选择13个主导参数,采用Sobol灵敏度分析所得出的主导参数进行辨识的结果与直接采用混合优化进行辨识的结果相比,残差最大增幅为0.138%,模型收敛速度最大增幅为30.4%.

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 identifica⁃tion method based on Particle Swarm Optimization(PSO)and Levenberg-Marquardt algorithm was proposed.The pa⁃rameter 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 con⁃trolled in a range of 5%.Based on the Sobol sensitivity analysis method,the influence of multi-characteristic param⁃eters on the tire MF model was studied.The first-order sensitivity and total order sensitivity of each characteristic pa⁃rameter 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 param⁃eters 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%.