Real time diagnostics and prognostics of UAV lithium-polymer batteries
N. Eleftheroglou (Structural Integrity & Composites)
D. Zarouchas (Structural Integrity & Composites)
Theodoros Loutas (University of Patras)
Sina Sharif Mansouri (Luleå University of Technology)
George Georgoulas (University of Patras)
Petros Karvelis (Luleå University of Technology)
George Nikolakopoulos (Luleå University of Technology)
Benedictus Rinze (Structural Integrity & Composites)
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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.
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