Real time diagnostics and prognostics of UAV lithium-polymer batteries

Nikos Eleftheroglou, Dimitrios Zarouchas, Theodoros Loutas, Sina Sharif Mansouri, George Georgoulas, Petros Karvelis, George Nikolakopoulos, Rinze Benedictus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019)
EditorsN. Scott Clements
Place of PublicationNY, USA
PublisherPHM Society
Number of pages7
Volume11 (1)
Publication statusPublished - 2019
EventPHM 2019: 11th Annual Conference of the Prognostics and Health Management Society - Scottsdale, United States
Duration: 21 Sep 201926 Sep 2019

Conference

ConferencePHM 2019: 11th Annual Conference of the Prognostics and Health Management Society
CountryUnited States
CityScottsdale
Period21/09/1926/09/19

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