Battery Safety: Data-Driven Prediction of Failure

Donal P. Finegan, Samuel J. Cooper

Research output: Contribution to journalArticlepeer-review

36 Scopus Citations

Abstract

Accurate prediction of battery failure, both online and offline, facilitates design of safer battery systems through informed-engineering and on-line adaption to unfavorable scenarios. With the wide range of batteries available and frequently evolving pack designs, accurate prediction of cell behavior under different conditions is very challenging and extremely time consuming. In this issue of Joule, Li et al.1 used data from a previously reported finite-element model to train machine learning algorithms to predict whether a cell will undergo an internal short circuit when exposed to a selection of mechanical abuse conditions. The presented approach aims to alleviate, and yet is still limited by, a common challenge facing data-driven prediction methods: access to robust, plentiful, high-quality, and relevant experimental data.

Original languageAmerican English
Pages (from-to)2599-2601
Number of pages3
JournalJoule
Volume3
Issue number11
DOIs
StatePublished - 20 Nov 2019

Bibliographical note

Publisher Copyright:
© 2019

NREL Publication Number

  • NREL/JA-5400-75591

Keywords

  • accurate prediction
  • battery failures
  • battery safety
  • battery systems
  • cell behaviors
  • internal shorts
  • lithium ion batteries
  • prediction methods
  • prediction of failures

Fingerprint

Dive into the research topics of 'Battery Safety: Data-Driven Prediction of Failure'. Together they form a unique fingerprint.

Cite this