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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning*
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2017 (English)In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, 4727-4732 p.Article in journal (Refereed) Published
Abstract [en]

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, 4727-4732 p.
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-66196DOI: 10.1016/j.ifacol.2017.08.863Scopus ID: 2-s2.0-85031802665OAI: oai:DiVA.org:ltu-66196DiVA: diva2:1150694
Conference
20th IFAC World Congress, Toulouse, France, 9-14 July 2017
Note

Konferensartikel i tidskrift

Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2017-11-24Bibliographically approved

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