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Journal article

Gaussian process regression for forecasting battery state of health

Abstract:

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostic...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jpowsour.2017.05.004

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
St Hilda's College
Role:
Author
Publisher:
Elsevier Publisher's website
Journal:
Journal of Power Sources Journal website
Volume:
357
Pages:
209–219
Publication date:
2017-05-10
Acceptance date:
2017-05-02
DOI:
ISSN:
0378-7753
Source identifiers:
697123
Keywords:
Pubs id:
pubs:697123
UUID:
uuid:46a15fa2-6e51-47e6-afe4-c0223e254569
Local pid:
pubs:697123
Deposit date:
2017-05-30

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