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:2021:10.1016/j.joule.2020.11.018,
author = {Finegan, DP and Zhu, J and Feng, X and Keyser, M and Ulmefors, M and Li, W and Bazant, MZ and Cooper, SJ},
doi = {10.1016/j.joule.2020.11.018},
journal = {Joule},
pages = {316--329},
title = {The application of data-driven methods and physics-based learning for improving battery safety},
url = {http://dx.doi.org/10.1016/j.joule.2020.11.018},
volume = {5},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. Data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.
AU  - Finegan,DP
AU  - Zhu,J
AU  - Feng,X
AU  - Keyser,M
AU  - Ulmefors,M
AU  - Li,W
AU  - Bazant,MZ
AU  - Cooper,SJ
DO  - 10.1016/j.joule.2020.11.018
EP  - 329
PY  - 2021///
SN  - 2542-4351
SP  - 316
TI  - The application of data-driven methods and physics-based learning for improving battery safety
T2  - Joule
UR  - http://dx.doi.org/10.1016/j.joule.2020.11.018
UR  - https://www.sciencedirect.com/science/article/pii/S2542435120305626?via%3Dihub
UR  - http://hdl.handle.net/10044/1/86959
VL  - 5
ER  -
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