Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries



Published Sep 22, 2019
Nick Eleftheroglou Dimitrios Zarouchas Theodoros Loutas Sina Sharif Mansouri George Georgoulas Petros Karvelis George Nikolakopoulos Rinze Benedictus


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).
Abstract 517 | PDF Downloads 738



UAVs, diagnostics, prognostics, batteries, RUL, data-driven model

Technical Research Papers