Real time diagnostics and prognostics of UAV lithium-polymer batteries

Real time diagnostics and prognostics of UAV lithium-polymer batteries

Eleftheroglou, N. (TU Delft Structural Integrity & Composites)
Zarouchas, D. (TU Delft Structural Integrity & Composites)
Loutas, Theodoros (University of Patras)
Mansouri, Sina Sharif (Luleå Univ. of Technology)
Georgoulas, George (University of Patras)
Karvelis, Petros (Luleå Univ. of Technology)
Nikolakopoulos, George (Luleå Univ. of Technology)
Benedictus, R. (TU Delft Structural Integrity & Composites)

Scott Clements, N. (editor)

2019

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.

http://resolver.tudelft.nl/uuid:64358950-7297-4759-99f3-33a8c57b0095

PHM Society, NY, USA

Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2019), 11 (1)

PHM 2019: 11th Annual Conference of the Prognostics and Health Management Society, 2019-09-21 → 2019-09-26, Scottsdale, United States

Institutional Repository

conference paper

© 2019 N. Eleftheroglou, D. Zarouchas, Theodoros Loutas, Sina Sharif Mansouri, George Georgoulas, Petros Karvelis, George Nikolakopoulos, R. Benedictus