Data on Machine Learning regenerated Lithium-ion battery impedance.

导出 LinkOut

翻译打印收藏纠错

作者： Selcuk,Temiz ^[1] ; Hasan,Kurban ^[2] ; Salim,Erol ^[3] ; Mehmet M,Dalkilic ^[4]

作者单位： Department of Physics, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey. ^[1] Applied Data Science Department, San Jose State University, San Jose, CA, 95192, US. ^[2] Computer Science Department, Indiana University, Bloomington, 47405, IN, US. ^[3] Department of Chemical Engineering, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey. ^[4]

关键词 Co-modeling approach Electrochemical Impedance Spectroscopy (EIS) for Li-ion batteries Machine Learning (ML) on Li-ion batteries Regeration of impedance for Li-ion batteries

DOI 10.1016/j.dib.2022.108698

PMID 36426056

发布时间 2022-11-25

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Data in brief

2022年45卷

108698页

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

Data on Machine Learning regenerated Lithium-ion battery impedance.

Data in brief

相似文献

Data in brief

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Data on Machine Learning regenerated Lithium-ion battery impedance.

Data in brief

相似文献

Data in brief

相关期刊

检索历史清除