Sökning: id:"swepub:oai:DiVA.org:ltu-75673" >
Intelligent data-dr...
Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
-
- Eleftheroglou, Nick (författare)
- Faculty of Aerospace Engineering, TU Delft, the Netherlands
-
- Mansouri, Sina Sharif (författare)
- Luleå tekniska universitet,Signaler och system
-
- Loutas, Theodoros (författare)
- Department of Mechanical Engineering & Aeronautics, University of Patras, Greece
-
visa fler...
-
- Karvelis, Petros (författare)
- Luleå tekniska universitet,Signaler och system
-
- Georgoulas, George (författare)
- Department of Mechanical Engineering & Aeronautics, University of Patras, Greece
-
- Nikolakopoulos, George (författare)
- Luleå tekniska universitet,Signaler och system
-
- Zarouchas, Dimitrios (författare)
- Faculty of Aerospace Engineering, TU Delft, the Netherlands
-
visa färre...
-
(creator_code:org_t)
- Elsevier, 2019
- 2019
- Engelska.
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 254
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Remaining useful life
- Data-driven prognostics
- UAVs
- Li-Po batteries
- End of discharge
- Machine learning
- Reglerteknik
- Control Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas