{"title":"基于Kriging响应面法的电动汽车电池组结构响应预测与质量优化研究","authors":"Deepak Sreedhar Kanakandath, Sankha Subhra Jana, Arunkumar Ramakrishnan","doi":"10.37285/ajmt.1.2.8","DOIUrl":null,"url":null,"abstract":"Structural response of battery packs in electric vehicles when subjected to road loads is an important factor that decides its performance and life during normal operation. In this paper a kriging response surface model is built using a Design of Experiment (DOE) run dataset to predict structural response and global modal frequency metrics of the battery pack. Using this Response Surface Model (RSM), we can rapidly optimize the battery pack design with respect to structural response and achieve significant mass reduction. This method reduces turnaround times for design optimization in early stages of battery pack design.","PeriodicalId":294802,"journal":{"name":"ARAI Journal of Mobility Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Using Kriging Response Surface Method for EV Battery Pack Structural Response Prediction and Mass Optimization\",\"authors\":\"Deepak Sreedhar Kanakandath, Sankha Subhra Jana, Arunkumar Ramakrishnan\",\"doi\":\"10.37285/ajmt.1.2.8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structural response of battery packs in electric vehicles when subjected to road loads is an important factor that decides its performance and life during normal operation. In this paper a kriging response surface model is built using a Design of Experiment (DOE) run dataset to predict structural response and global modal frequency metrics of the battery pack. Using this Response Surface Model (RSM), we can rapidly optimize the battery pack design with respect to structural response and achieve significant mass reduction. This method reduces turnaround times for design optimization in early stages of battery pack design.\",\"PeriodicalId\":294802,\"journal\":{\"name\":\"ARAI Journal of Mobility Technology\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ARAI Journal of Mobility Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37285/ajmt.1.2.8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ARAI Journal of Mobility Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37285/ajmt.1.2.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On Using Kriging Response Surface Method for EV Battery Pack Structural Response Prediction and Mass Optimization
Structural response of battery packs in electric vehicles when subjected to road loads is an important factor that decides its performance and life during normal operation. In this paper a kriging response surface model is built using a Design of Experiment (DOE) run dataset to predict structural response and global modal frequency metrics of the battery pack. Using this Response Surface Model (RSM), we can rapidly optimize the battery pack design with respect to structural response and achieve significant mass reduction. This method reduces turnaround times for design optimization in early stages of battery pack design.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
