Imperial College London
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Dr Samuel J Cooper

Faculty of Engineering, Dyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

samuel.cooper Website

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Finegan:2019:10.1016/j.joule.2019.10.013,
author = {Finegan, DP and Cooper, SJ},
doi = {10.1016/j.joule.2019.10.013},
journal = {Joule},
pages = {2599--2601},
title = {Battery safety: data-driven prediction of failure},
url = {http://dx.doi.org/10.1016/j.joule.2019.10.013},
volume = {3},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Finegan,DP
AU  - Cooper,SJ
DO  - 10.1016/j.joule.2019.10.013
EP  - 2601
PY  - 2019///
SN  - 2542-4351
SP  - 2599
TI  - Battery safety: data-driven prediction of failure
T2  - Joule
UR  - http://dx.doi.org/10.1016/j.joule.2019.10.013
UR  - https://www.sciencedirect.com/science/article/pii/S254243511930529X?via%3Dihub
UR  - http://hdl.handle.net/10044/1/76247
VL  - 3
ER  -
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