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Data-driven Lead-Acide Battery Prognostics Using Random Survival Forests
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2014 (English)In: PMH 2014. Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA / [ed] Mathew J. Daigle and Anibal Bregon, PMH Society , 2014, 92-101 p.Conference paper (Refereed)
Abstract [en]

Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is a case-study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data-driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet management data including 291 variables from 33 603 vehicles from 5 different European markets. The data is a mix of numerical values such as temperatures and pressures, together with histograms and categorical data such as battery mount point. Implementation aspects are discussed such as how to include histogram data and how to reduce the computational complexity by reducing the number of variables. Finally, battery lifetime predictions are computed and evaluated on recorded data from Scania's fleet-management system.

Place, publisher, year, edition, pages
PMH Society , 2014. 92-101 p.
Series
Proceedings, PHM Society, ISSN 2325-0178
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137776ISBN: 978-1-936263-17-2 (print)OAI: oai:DiVA.org:liu-137776DiVA: diva2:1101809
Conference
Proceedings of the Annual Conference of The Prognostics and Health Management Society. Fort Worth, Texas, USA, September 29 - October 2
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2017-06-01Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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