摘要
整车结构耐久试验作为车辆开发的关键通过性指标之一,其对于车辆开发至关重要。为了提高耐久规范与用户实际使用的关联性,文章提出了一套关联实际用户使用的整车结构耐久试验规范开发方法。首先,基于用户大数据利用机器学习获取真实用户道路类型里程比例、用户驾驶风格和典型操作类工况;其次,通过用户载荷谱采集、强化编辑与外推获取目标分位数的用户载荷谱;然后,采集与筛选试验场工况载荷,通过多维度的关联技术,匹配试验场工况与用户目标载荷,编制形成结构耐久规范;最后,通过试运行和持续优化对耐久规范改进完善。
The vehicle structure durability test,as one of the key pass-through indicators in vehicle development,is crucial for the development of vehicles.In order to improve the relevance between durability specifications and actual user usage,this article proposes a set of methods for developing vehicle structure durability test specifications that are associated with actual user usage.Firstly,based on user big data,machine learning is used to obtain the real proportion of user road type mileage,user driving styles,and typical operating conditions.Secondly,target percentile user load spectra are obtained through user load spectrum collection,enhanced editing,and extrapolation.Then,proving ground condition loads are collected and screened,and through multi-dimensional correlation technology,the proving ground conditions are matched with the user target loads to compile the vehicle structural durability specifications.Finally,the durability specifications are improved and perfected through trial operation and continuous optimization.
作者
卢海波
钟志宏
陈春燕
LU Haibo;ZHONG Zhihong;CHEN Chunyan(GAC R&D Center,Guangzhou 511434,China)
出处
《汽车实用技术》
2024年第13期95-99,126,共6页
Automobile Applied Technology
关键词
结构耐久
大数据
用户使用
载荷谱
Structural durability
Big data
Customer usage
Load spectrum
