Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries

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Published Sep 22, 2019
DOI https://doi.org/10.36001/phmconf.2019.v11i1.785
Nick Eleftheroglou
Delft University of Technology
Dimitrios Zarouchas
Delft University of Technology
Theodoros Loutas
University of Patras
Sina Sharif Mansouri
Lulea University of Technology
George Georgoulas
University of Patras
Petros Karvelis
Lulea University of Technology
George Nikolakopoulos
Lulea University of Technology
Rinze Benedictus
Delft University of Technology

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.

How to Cite

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. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.785
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