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Modeling, Control System Design and Preliminary Experimental Verification of a Hybrid Power Unit Suitable for Multirotor UAVs

Author

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  • Matija Krznar

    (Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

  • Petar Piljek

    (Department of Technology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

  • Denis Kotarski

    (Department of Mechanical Engineering, Karlovac University of Applied Sciences, 47000 Karlovac, Croatia)

  • Danijel Pavković

    (Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

A key drawback of multirotor unmanned aerial vehicles (UAVs) with energy sources based solely on electrochemical batteries is related to the available on-board energy. Flight autonomy is typically limited to 15–30 min, with a flight duration upper limit of 90 min currently being achieved by high-performance battery-powered multirotor UAVs. Therefore, propulsion systems that utilize two or more different energy sources (hybrid power systems) may be considered as an alternative in order to increase the flight duration while retaining key performance benefits of battery energy storage use. The research presented in this work considers a multirotor UAV power unit, based on the internal combustion engine (ICE) powering an electricity generator (EG) connected to the common direct current (DC) bus in parallel with the lithium-polymer (LiPo) battery, and the respective modeling and identification of individual power unit subsystem, along with the dedicated control system design. Experimental verification of the proposed hybrid power unit control system has been carried out on the custom-build power unit prototype. The results show that the proposed control system combines the two power sources in a straightforward and effective way, with subsequent analysis showing that a two-fold energy density increase can be achieved with a hybrid energy source, consequently making it possible to achieve higher flight autonomy of the prospective multirotor (hover load around 1000–1400 W) equipped with such a hybrid system.

Suggested Citation

  • Matija Krznar & Petar Piljek & Denis Kotarski & Danijel Pavković, 2021. "Modeling, Control System Design and Preliminary Experimental Verification of a Hybrid Power Unit Suitable for Multirotor UAVs," Energies, MDPI, vol. 14(9), pages 1-24, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2669-:d:549698
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    References listed on IDEAS

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    1. Mohamed Dahbi & Said Doubabi & Ahmed Rachid, 2018. "Current Spikes Minimization Method for Three-Phase Permanent Magnet Brushless DC Motor with Real-Time Implementation," Energies, MDPI, vol. 11(11), pages 1-14, November.
    2. Cipek, Mihael & Pavković, Danijel & Kljaić, Zdenko & Mlinarić, Tomislav Josip, 2019. "Assessment of battery-hybrid diesel-electric locomotive fuel savings and emission reduction potentials based on a realistic mountainous rail route," Energy, Elsevier, vol. 173(C), pages 1154-1171.
    3. Fischer, M. & Weber, M. & Schwartz, P.V., 2009. "Corrigendum to "Batteries: Higher energy density than gasoline?" [Energy policy 37 (2009) 2639-2641]," Energy Policy, Elsevier, vol. 37(9), pages 3709-3709, September.
    4. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
    5. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    6. Fischer, Michael & Werber, Mathew & Schwartz, Peter V., 2009. "Batteries: Higher energy density than gasoline?," Energy Policy, Elsevier, vol. 37(7), pages 2639-2641, July.
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    Cited by:

    1. Matija Krznar & Danijel Pavković & Mihael Cipek & Juraj Benić, 2021. "Modeling, Controller Design and Simulation Groundwork on Multirotor Unmanned Aerial Vehicle Hybrid Power Unit," Energies, MDPI, vol. 14(21), pages 1-26, November.

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