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Heavy-duty truck battery failure 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.ORCID iD: 0000-0003-0808-052X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
2016 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2016, Vol. 49, no 11, 562-569 p.Conference paper (Refereed)
Abstract [en]

Predicting lead-acid battery failure is important for heavy-duty trucks to avoid unplanned stops by the road. There are large amount of data from trucks in operation, however, data is not closely related to battery health which makes battery prognostic challenging. A new method for identifying important variables for battery failure prognosis using random survival forests is proposed. Important variables are identified and the results of the proposed method are compared to existing variable selection methods. This approach is applied to generate a prognosis model for lead-acid battery failure in trucks and the results are analyzed. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2016. Vol. 49, no 11, 562-569 p.
Keyword [en]
Battery failure prognosis; Random survival forests; Variable selection
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-132240DOI: 10.1016/j.ifacol.2016.08.082ISI: 000383464400082OAI: oai:DiVA.org:liu-132240DiVA: diva2:1039384
Conference
8th IFAC Symposium on Advances in Automotive Control (AAC)
Available from: 2016-10-24 Created: 2016-10-21 Last updated: 2016-10-24

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Voronov, SergiiJung, DanielFrisk, Erik
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