- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 2.0MB)
-
- Publisher copy:
- 10.1016/j.jpowsour.2017.05.004
-
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- 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
- Pubs id:
-
pubs:697123
- URN:
-
uri:46a15fa2-6e51-47e6-afe4-c0223e254569
- UUID:
-
uuid:46a15fa2-6e51-47e6-afe4-c0223e254569
- Local pid:
- pubs:697123
- Copyright holder:
- Richardson et al.
- Copyright date:
- 2017
- Notes:
- © 2017 The Authors. Published by Elsevier B.V. Open Access funded by Engineering and Physical Sciences Research Council. Available under a Creative Commons license.
Journal article
Gaussian process regression for forecasting battery state of health
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