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Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
Faculty of Aerospace Engineering, TU Delft, the Netherlands.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Department of Mechanical Engineering & Aeronautics, University of Patras, Greece.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 254, article id 113677Article in journal (Refereed) Published
Abstract [en]

In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 254, article id 113677
Keywords [en]
Remaining useful life, Data-driven prognostics, UAVs, Li-Po batteries, End of discharge, Machine learning
National Category
Robotics Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-75673DOI: 10.1016/j.apenergy.2019.113677Scopus ID: 2-s2.0-85070739542OAI: oai:DiVA.org:ltu-75673DiVA, id: diva2:1345203
Note

Validerad;2019;Nivå 2;2019-08-27 (johcin)

Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-08-27Bibliographically approved

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Mansouri, Sina SharifKarvelis, PetrosNikolakopoulos, George

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