基于胎内周向应变的非道路轮胎垂向载荷反演优化算法研究  

Inverse Optimization Algorithm for Vertical Load of Non-road Tire Based on In-tire Circumferential Strain

作　　者：王亚东[1] 宋寅东 王彦民 张剑[4] 何志祝 李臻 WANG Yadong;SONG Yindong;WANG Yanmin;ZHANG Jian;HE Zhizhu;LI Zhen(College of Engineering,China Agricultural University,Beijing 100083,China;Modern Agricultural Equipment Co.,Ltd.,Beijing 100083,China;Shijiazhuang Zhongxing Machinery Manufacture Co.,Ltd.,Shijiazhuang 051530,China;Technology Center,Guizhou Tyre Co.,Ltd.,Guiyang 550201,China)

机构地区：[1]中国农业大学工学院,北京100083 [2]现代农装科技股份有限公司,北京100083 [3]石家庄中兴机械制造股份有限公司,石家庄051530 [4]贵州轮胎股份有限公司技术中心,贵阳550201

出　　处：《农业机械学报》2025年第1期463-473,共11页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2022YFD2000300);国家自然科学基金项目(52175259);拼多多-中国农业大学研究基金项目(PC2023B01005)。

摘　　要：非道路轮胎具有结构尺寸大、工况恶劣多变、载荷波动明显等典型特征,其垂向载荷显著影响车辆的纵向、垂向、侧向动力学特性。针对非道路轮胎垂向载荷获取困难、传统物理模型推演精度不足的问题,提出了一种基于应变信息与机器学习技术的垂向载荷反演算法。以R-1型人字花纹非道路轮胎为研究对象,设计了由大量程柔性应变传感器、低功耗数据采集及无线传输模块组成的轮胎应变信息采集系统。以胎压、速度、载荷等参数为变量,在转鼓试验台上开展了多种典型工况测试,分析了轮胎接地点的应变变化规律。在此基础上,构建了面向轮胎垂向载荷估计的深度神经网络模型,并基于AdamW优化器与网格搜索法开展了算法参数优化。试验结果表明,基于AdamW优化器的深度神经网络模型对非道路轮胎垂向载荷预测表现出较高的精度,各工况下最大平均相对误差由4.10%降至0.30%。此外,针对模型泛化能力的测试结果显示,深度神经网络模型平均归一化均方根误差较SVR模型降低55.91%,泛化性能优越。研究表明,所提出基于AdamW优化器的深度神经网络模型可对以应变信息为输入的非道路轮胎垂向载荷进行准确反演,为非道路车辆的动力学控制系统提供可靠的轮胎力学关键参数获取依据。Non-road tires have typical characteristics such as large structural size,harsh and changeable working conditions,and obvious load fluctuations.Its vertical load significantly affects the longitudinal,vertical and lateral dynamic characteristics of the vehicle.Aiming at the problem of difficulty in obtaining the vertical load of non-road tires and the insufficient accuracy of traditional physical model deductions,a vertical load inversion algorithm was proposed based on strain information and machine learning technology.Taking the R-1 herringbone pattern non-road tire as the research object,a tire strain information collection system consisting of a large-range flexible strain sensor,low-power data collection and wireless transmission module was designed.Using parameters such as tire pressure,speed,load as variables,a variety of typical working condition tests were carried out on the drum test bench,and the strain change pattern of the tire contact point was analyzed.On this basis,a deep neural network model for tire-oriented vertical load estimates was built.The algorithm parameter optimization was carried out based on the AdamW optimizer and grid search method.The test results showed that the deep neural network model based on AdamW optimizer showed a high accuracy on the prediction of the non-road tire vertical load prediction.Under the trial conditions,the maximum average relative error was reduced from 4.10%to 0.30%.Test results for the generalization capacity of models showed that the average naturalization of deep neural network models was reduced by 55.91%compared with the SVR model,and the generalization performance was superior.Studies showed that the deep neural network model proposed based on the AdamW optimizer had accurate reaction to the non-road tire vertical load.This method provided the basis for the acquisition of reliable key parameters of tire mechanics for the dynamic control system of non-road vehicles.

关 键 词：非道路轮胎 周向应变 载荷估计 智能轮胎 机器学习 

分 类 号：U481[交通运输工程—载运工具运用工程]