Abstract
Drones are gradually becoming an integral part of several applications like package delivery, military reconnaissance, and automated inspection systems. Drones may utilize one source of energy, which is usually a battery. However, drones operating on fossil fuels and large capacity fuel cells also exist. This paper introduces a novel optimization framework to decide on the optimal source or mix of sources to be installed on drones to minimize their running cost. The proposed approach considers three sources: batteries, fuel cells, and super-capacitors, the characteristics of which are embedded in the optimal selection approach. In addition, a drone aerodynamic model is incorporated, which is composed of four actions: hovering, ascending, descending, and moving forward. The proposed approach selects the optimal source(s) from a defined database of sources to power the drone according to the user-specified trip profile. The offline simulation of various case studies shows that the proposed framework enables the selection of appropriate power source(s) to sufficiently support the drone flight while simultaneously minimizing its operational cost. The present work focuses on energy sources, but future extensions can allow automated selection of all parts required to assemble a drone, such that longest flight time is achieved at least cost. The proposed approach also quickly allows ascertaining if a desired flight time is achievable given a flight profile and a database of energy sources/parts.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All required data used for this work are available in the Appendix. Other data required for the optimization can be generated from the equations given in this paper.
References
Alyassi R, Khonji M, Chau SC-K, Elbassioni K, Tseng C-M, Karapetyan A (2017) Autonomous recharging and flight mission planning for battery-operated autonomous drones. http://arxiv.org/abs/1703.10049
Anderson J, Kalra N, Stanley K, Sorenson P, Samaras C, Oluwatola O (2016) Autonomous vehicle technology: a guide for policymakers. RAND Corpor 2016:58
ANSGear-Paintball|Airsoft|Guns|Equipment, ANSGear (2020) https://www.ansgear.com/Ninja_Paintball_Tanks_s/. Accessed 01 Jan 2020
Apostoloff N, Zelinsky A (2003) Robust vision based lane tracking using multiple cues and particle filtering. In: IEEE IV2003 intelligent vehicles symposium. Proceedings (Cat. No.03TH8683), 2003, pp 558–563. https://doi.org/10.1109/IVS.2003.1212973
Arbanas B et al (2016) Aerial-ground robotic system for autonomous delivery tasks. In: Proceedings—IEEE international conference on robotics and automation, vol 2016, pp. 5463–5468. https://doi.org/10.1109/ICRA.2016.7487759
Austin R (2010) Unmanned aircraft systems: UAVs design, development and deployment. Wiley, Hoboken, pp 169–171
Baluta S (2019) How much power is needed to hover ? Starlino Electronics, Starlino.com, http://www.starlino.com/power2thrust.html. Accessed 09 Jun 2019
Bernard J, Delprat S, Büchi FN, Guerra TM (2009) Fuel-cell hybrid powertrain: toward minimization of hydrogen consumption. IEEE Trans Veh Technol 58(7):3168–3176. https://doi.org/10.1109/TVT.2009.2014684
Berntorp K, Inani P, Quirynen R, Di Cairano S (2019) Motion planning of autonomous road vehicles by particle filtering: implementation and validation. Am Control Conf (ACC) 2019:1382–1387. https://doi.org/10.23919/ACC.2019.8815309
Boukoberine MN, Zhou Z, Benbouzid M (2019) Power supply architectures for drones—a review. In: IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, pp 5826–5831. https://doi.org/10.1109/IECON.2019.8927702
Chen M, Rincón-Mora GA (2006) Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Trans Energy Convers 21(2):504–511. https://doi.org/10.1109/TEC.2006.874229
Dutczak J (2018) “Issues related to fuel cells application to small drones propulsion. IOP Conf Ser Mater Sci Eng 421:4. https://doi.org/10.1088/1757-899X/421/4/042014
Gadalla M, Zafar S (2016) Analysis of a hydrogen fuel cell-PV power system for small UAV. Int J Hydrog Energy 41(15):6422–6432. https://doi.org/10.1016/j.ijhydene.2016.02.129
GAMS (2021) Cutting edge modeling, Gams.com, 2021. https://www.gams.com/. Accessed 12 Jan 2020
Gong A, MacNeill R, Verstraete D, Palmer JL (2018) Analysis of a fuel-cell/battery/supercapacitor hybrid propulsion system for a UAV using a hardware-in-the-loop flight simulator. AIAA/IEEE Electr Aircr Technol Symp 2018:1–17. https://doi.org/10.2514/6.2018-5017
Lemmon EW, Huber ML, McLinden MO (2013) NIST Standard Reference Database 23: reference fluid thermodynamic and transport properties-REFPROP, Version 9.1. U.S. National Institute of Standards and Technology, Natl Std. Ref. Data Series. https://www.nist.gov/publications/. Accessed 29 Mar 2020
Liu K, Liu Y, Lin D, Pei A, Cui Y (2018) Materials for lithium-ion battery safety. Sci Adv 4(6):1–11. https://doi.org/10.1126/sciadv.aas9820
Masters G (2013) Renewable and efficient electric power systems. Wiley-Blackwell, Hoboken
Pozo B, Garate JI, Ferreiro S, Fernandez I, Fernandez de Gorostiza E (2018) Supercapacitor electro-mathematical and machine learning modelling for low power applications. Electron (switzerl) 7(4):1–17. https://doi.org/10.3390/electronics7040044
Reid J (2018) Drone flight—what does basic physics say? pp 1–13. https://homepages.abdn.ac.uk/nph120/meteo/DroneFlight.pdf. Accessed 09 Jun 2019
Sahinidis N, Tawarmalani M (2014) Global optimization with GAMS/BARON. https://www.gams.com/presentations/present_baron2.pdf
Samosir AS, Anwari M, Yatim AHM (2010) A simple PEM fuel cell emulator using electrical circuit model. In: 2010 9th international power and energy conference, IPEC 2010, pp 881–885. https://doi.org/10.1109/IPECON.2010.5697090
Stolaroff J, Samaras C, O’Neill E, Lubers A, Mitchell A, Ceperley D (2018) Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nat Commun 9(1):1–13. https://doi.org/10.1038/s41467-017-02411-5
Thirkell A, Chen R, Harrington I (2017) A fuel cell system sizing tool based on current production aircraft. In: SAE Technical Papers, pp 1–11. https://doi.org/10.4271/2017-01-2135
Trenev V, Mladenov M, Kanev K, Petrov E, Chavdarov I (2010) Unmanned aerial vehicle energy efficiency improvement by batery-supercapacitor system. In: Proceedings of the 19th International conference batch production automation AD, pp 476–481
Varghese J, Boone R (2015) Overview of autonomous vehicle sensors and systems. In: Proceedings of the 2015 international conference on operations excellence and service engineering, IEOM Society, pp 1–14
Yao Q, Tian Y, Wang Q, Wang S (2020) Control strategies on path tracking for autonomous vehicle: state of the art and future challenges. IEEE Access 8:161211–161222. https://doi.org/10.1109/ACCESS.2020.3020075
Zhuo J, Chakrabarti C, Naehyuck C, Vrudhula S (2006) Extending the lifetime of fuel cell based hybrid systems. In: 2006 43rd ACM/IEEE design automation conference, pp 562–567. https://doi.org/10.1109/DAC.2006.229290
Zubi G, Dufo-López R, Carvalho M, Pasaoglu G (2018) The lithium-ion battery: State of the art and future perspectives. Renew Sustain Energy Rev 89:292–308. https://doi.org/10.1016/j.rser.2018.03.002
Funding
No specific funding was received for this work.
Author information
Authors and Affiliations
Contributions
All authors contributed to the research. Material preparation, data collection, and analysis were performed by Lubna S. Mahmood, Mostafa F. Shaaban, Shayok Mukhopadhyay, and Manal Alblooshi. The first draft of the manuscript was written by Lubna S. Mahmood and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interests
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 A. Lithium-polymer batteries database
See Table 4.
1.2 B. Super-capacitor database
See Table 5.
1.3 C. Fuel cell database
For simplicity, all the fuel cells considered have the same cell type in the stack. The cell data and the stack data are given below. \({\mathrm{SP}}_{\mathrm{H}2\mathrm{O}}\) = 0.0319 kPa, \({A}_{\mathrm{cell}}\)= 50 cm2, \({R}_{\mathrm{FC}}=\) 0.007 Ω, \({E}_{\mathrm{Nernst}}\)= 1.1974905 V, \({V}_{act}\) = 0.000146794 V, and \({V}_{conc}\)= 0.092 V (Bernard et al. 2009).
Rights and permissions
About this article
Cite this article
Mahmood, L.S., Shaaban, M.F., Mukhopadhyay, S. et al. Optimal resource selection and sizing for unmanned aerial vehicles. Soft Comput 26, 5685–5697 (2022). https://doi.org/10.1007/s00500-022-06934-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-06934-y