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One-shot battery degradation trajectory prediction with deep learning

Abstract:

The degradation of batteries is complex and dependent on several internal mechanisms. Variations arising from manufacturing uncertainties and real-world operating conditions make battery lifetime prediction challenging. Here, we introduce a deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction. We also predict the end-of-life point and the knee-point. The model correctly learns about intrinsic v...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jpowsour.2021.230024

Authors


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Role:
Author
ORCID:
0000-0002-2916-3968
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Role:
Author
ORCID:
0000-0002-6246-5896
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-0620-3955
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Role:
Author
ORCID:
0000-0002-4354-0459
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Publisher:
Elsevier Publisher's website
Journal:
Journal of Power Sources Journal website
Volume:
506
Article number:
230024
Publication date:
2021-06-10
Acceptance date:
2021-05-07
DOI:
ISSN:
0378-7753
Language:
English
Keywords:
Pubs id:
1184002
Local pid:
pubs:1184002
Deposit date:
2021-08-18

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