Transfer learning prediction on lithium-ion battery heat release under thermal runaway condition  

作　　者：Changmin Shi Di Zhu Liwen Zhang Siyuan Song Brian W.Sheldon 

机构地区：[1]School of Engineering,Brown University,Providence,RI 02912,USA [2]Mechanical Engineering,North Carolina State University,Raleigh,NC 27606,USA [3]Department of Mechanical,Aerospace&Biomedical Engineering,UT Space Institute,University of Tennessee,Knoxville,TN 37388,USA

出　　处：《Nano Research Energy》2024年第4期9-12,共4页纳米能源研究(英文)

摘　　要：Accurately predicting the variability of thermal runaway(TR)behavior in lithium-ion(Li-ion)batteries is critical for designing safe and reliable energy storage systems.Unfortunately,traditional calorimetry-based experiments to measure heat release during TR are time-consuming and expensive.Herein,we highlight an exciting transfer learning approach that leverages mass ejection data and metadata from cells to predict heat output variability during TR events.This approach significantly reduces the effort and time to assess thermal risks associated with Li-ion batteries.

关 键 词：transfer learning machine learning Li-ion battery thermal runway heat release 

分 类 号：TM9[电气工程—电力电子与电力传动]