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

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

Structural Integrity & Composites

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

335 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019)
Editors	N. Scott Clements
Place of Publication	NY, USA
Publisher	PHM Society
Number of pages	7
Volume	11 (1)
Publication status	Published - 2019
Event	PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society - Scottsdale, United States Duration: 21 Sept 2019 → 26 Sept 2019

Conference

Conference	PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society
Country/Territory	United States
City	Scottsdale
Period	21/09/19 → 26/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

785-Document Upload-3291-1-10-20190922 (2)Final published version, 1.06 MBLicence: CC BY

http://www.phmpapers.org/index.php/phmconf/article/view/785

Cite this

Eleftheroglou, N., Zarouchas, D., Loutas, T., Mansouri, S. S., Georgoulas, G., Karvelis, P., Nikolakopoulos, G., & Benedictus, R. (2019). Real time diagnostics and prognostics of UAV lithium-polymer batteries. In N. Scott Clements (Ed.), Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019) (Vol. 11 (1)). Article 785 PHM Society. http://www.phmpapers.org/index.php/phmconf/article/view/785

@inproceedings{643589507297475999f333a8c57b0095,

title = "Real time diagnostics and prognostics of UAV lithium-polymer batteries",

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.",

author = "Nikos Eleftheroglou and Dimitrios Zarouchas and Theodoros Loutas and Mansouri, {Sina Sharif} and George Georgoulas and Petros Karvelis and George Nikolakopoulos and Rinze Benedictus",

year = "2019",

language = "English",

volume = "11 (1)",

editor = "{Scott Clements}, N.",

booktitle = "Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019)",

publisher = "PHM Society",

note = "PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society ; Conference date: 21-09-2019 Through 26-09-2019",

}

Eleftheroglou, N , Zarouchas, D, Loutas, T, Mansouri, SS, Georgoulas, G, Karvelis, P, Nikolakopoulos, G & Benedictus, R 2019, Real time diagnostics and prognostics of UAV lithium-polymer batteries. in N Scott Clements (ed.), Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019). vol. 11 (1), 785, PHM Society, NY, USA, PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society , Scottsdale, Arizona, United States, 21/09/19. <http://www.phmpapers.org/index.php/phmconf/article/view/785>

Real time diagnostics and prognostics of UAV lithium-polymer batteries. / Eleftheroglou, Nikos ; Zarouchas, Dimitrios; Loutas, Theodoros et al.
Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019). ed. / N. Scott Clements. Vol. 11 (1) NY, USA: PHM Society, 2019. 785.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Real time diagnostics and prognostics of UAV lithium-polymer batteries

AU - Eleftheroglou, Nikos

AU - Zarouchas, Dimitrios

AU - Loutas, Theodoros

AU - Mansouri, Sina Sharif

AU - Georgoulas, George

AU - Karvelis, Petros

AU - Nikolakopoulos, George

AU - Benedictus, Rinze

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - 11 (1)

BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019)

A2 - Scott Clements, N.

PB - PHM Society

CY - NY, USA

T2 - PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society

Y2 - 21 September 2019 through 26 September 2019

ER -

Real time diagnostics and prognostics of UAV lithium-polymer batteries

Abstract

Conference

UN SDGs

Access to Document

Fingerprint

Cite this