Journal article
A Primer on Bayesian Parameter Estimation and Model Selection for Battery Simulators
- Abstract:
- Highlights: Physics-based battery modelling allows for precise diagnostics and prediction. Bayesian optimisation techniques enable matching theory with field data. We introduce the Bayesian model selection algorithms SOBER and BASQ. 6 example applications, incl. impedance model selection and surrogate-based analysis.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 12.2MB, Terms of use)
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- Publisher copy:
- 10.1149/1945-7111/ae73f3
Authors
+ Horizon 2020 Framework Programme
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- Funder identifier:
- 10.13039/100010661
- Grant:
- 101103997
+ Deutsches Zentrum für Luft- und Raumfahrt
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- Funder identifier:
- 10.13039/501100002946
- Publisher:
- IOP Publishing
- Journal:
- Journal of The Electrochemical Society More from this journal
- Volume:
- 173
- Issue:
- 12
- Pages:
- 120509
- Article number:
- 120509
- Publication date:
- 2026-06-17
- Acceptance date:
- 2026-05-27
- DOI:
- EISSN:
-
1945-7111
- ISSN:
-
0013-4651
- Language:
-
English
- Keywords:
- Source identifiers:
-
4239924
- Deposit date:
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2026-06-17
- ARK identifier:
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Terms of use
- Copyright date:
- 2026
- Licence:
- CC Attribution (CC BY)
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