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Bayesian hierarchical model-based prognostics for lithium-ion batteries
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
NASA Ames Research Center, Intelligent Systems Division, Moffett Field, CA.
2018 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 172, 25-35 p.Article in journal (Refereed) Published
Abstract [en]

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 172, 25-35 p.
National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Matemathical Statistics
Identifiers
URN: urn:nbn:se:ltu:diva-66884DOI: 10.1016/j.ress.2017.11.020Scopus ID: 2-s2.0-85037540599OAI: oai:DiVA.org:ltu-66884DiVA: diva2:1162190
Note

Validerad;2018;Nivå 2;2017-12-14 (andbra)

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2017-12-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
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Output format
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