IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i12p3101-d1677580.html
   My bibliography  Save this article

Hybrid Powerplant Design and Energy Management for UAVs: Enhancing Autonomy and Reducing Operational Costs

Author

Listed:
  • Javier A. Quintana

    (ENGREEN—Laboratory of Engineering for Energy and Enviromental Sustainability, Universidad de Sevilla, 41092 Seville, Spain)

  • Carlos Bordons

    (ENGREEN—Laboratory of Engineering for Energy and Enviromental Sustainability, Universidad de Sevilla, 41092 Seville, Spain
    Department of Systems Engineering and Automation, Universidad de Sevilla, 41092 Seville, Spain)

  • Sergio Esteban

    (Department of Aerospace Engineering, Universidad de Sevilla, 41092 Seville, Spain)

  • Julian Delgado

    (Zelenza S.L., 28053 Madrid, Spain)

Abstract

This study presents the design of a hybrid powerplant for unmanned aerial vehicles (UAVs), improving its autonomy compared to power systems based solely on batteries. The powerplant is designed for the Mugin EV-350 aircraft. Using experimental data from electric motors in a wind tunnel and fuel cells, a comparative analysis of different energy management strategies, such as fuzzy logic and passive, is conducted to reduce the operational and maintenance costs. A Python-based software program is developed and utilized for the real-time implementation and simulation of energy management strategies, with data collected in databases. This study integrates experimental data (wind tunnel and fuel cells) with real-time EMS strategies, and simulation-based predictions indicate practical improvements in endurance and cost reduction, as well as an increase in flight autonomy of 50%.

Suggested Citation

  • Javier A. Quintana & Carlos Bordons & Sergio Esteban & Julian Delgado, 2025. "Hybrid Powerplant Design and Energy Management for UAVs: Enhancing Autonomy and Reducing Operational Costs," Energies, MDPI, vol. 18(12), pages 1-25, June.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3101-:d:1677580
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/12/3101/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/12/3101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. JiHyun Choi & Hyun-Jong Park & Jaeyoung Han, 2025. "Development of Hydrogen Fuel Cell–Battery Hybrid Multicopter System Thermal Management and Power Management System Based on AMESim," Energies, MDPI, vol. 18(2), pages 1-15, January.
    2. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.
    3. Luxon Nhamo & James Magidi & Adolph Nyamugama & Alistair D. Clulow & Mbulisi Sibanda & Vimbayi G. P. Chimonyo & Tafadzwanashe Mabhaudhi, 2020. "Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
    4. Massimo Cardone & Bonaventura Gargiulo & Enrico Fornaro, 2021. "Modelling and Experimental Validation of a Hybrid Electric Propulsion System for Light Aircraft and Unmanned Aerial Vehicles," Energies, MDPI, vol. 14(13), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sanghyun Yun & Jinwon Yun & Jaeyoung Han, 2023. "Development of a 470-Horsepower Fuel Cell–Battery Hybrid Xcient Dynamic Model Using Simscape TM," Energies, MDPI, vol. 16(24), pages 1-22, December.
    2. Yi Zhang & Qiang Guo & Jie Song, 2023. "Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 16(18), pages 1-17, September.
    3. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    4. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.
    5. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    6. Kofler, Sandro & Jakubek, Stefan & Hametner, Christoph, 2025. "Predictive energy management strategy with optimal stack start/stop control for fuel cell vehicles," Applied Energy, Elsevier, vol. 377(PB).
    7. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).
    8. Pei, Pucheng & Jia, Xiaoning & Xu, Huachi & Li, Pengcheng & Wu, Ziyao & Li, Yuehua & Ren, Peng & Chen, Dongfang & Huang, Shangwei, 2018. "The recovery mechanism of proton exchange membrane fuel cell in micro-current operation," Applied Energy, Elsevier, vol. 226(C), pages 1-9.
    9. Lingfei Weng & Wentao Dou & Yejing Chen, 2023. "Study on the Coupling Effect of Agricultural Production, Road Construction, and Ecology: The Case for Cambodia," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    10. Chen, Dongfang & Pei, Pucheng & Meng, Yining & Ren, Peng & Li, Yuehua & Wang, Mingkai & Wang, Xizhong, 2022. "Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells," Energy, Elsevier, vol. 255(C).
    11. Macias, A. & Kandidayeni, M. & Boulon, L. & Trovão, J.P., 2021. "Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain," Energy, Elsevier, vol. 224(C).
    12. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Liao, Hongbo & Li, Yuan, 2024. "A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation," Renewable Energy, Elsevier, vol. 237(PC).
    13. Hu, Zunyan & Xu, Liangfei & Huang, Yiyuan & Li, Jianqiu & Ouyang, Minggao & Du, Xiaoli & Jiang, Hongliang, 2018. "Comprehensive analysis of galvanostatic charge method for fuel cell degradation diagnosis," Applied Energy, Elsevier, vol. 212(C), pages 1321-1332.
    14. Ke Song & Yimin Wang & Xiao Hu & Jing Cao, 2020. "Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter," Energies, MDPI, vol. 13(23), pages 1-21, November.
    15. Jouin, Marine & Bressel, Mathieu & Morando, Simon & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine & Jemei, Samir & Hilairet, Mickael & Ould Bouamama, Belkacem, 2016. "Estimating the end-of-life of PEM fuel cells: Guidelines and metrics," Applied Energy, Elsevier, vol. 177(C), pages 87-97.
    16. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
    17. Magidi, J. & van Koppen, Barbara & Nhamo, L. & Mpandeli, S. & Slotow, R. & Mabhaudhi, Tafadzwanashe, 2021. "Informing equitable water and food policies through accurate spatial information on irrigated areas in smallholder farming systems," Papers published in Journals (Open Access), International Water Management Institute, pages 1-13(24):36.
    18. Ren, Peng & Meng, Yining & Pei, Pucheng & Fu, Xi & Chen, Dongfang & Li, Yuehua & Zhu, Zijing & Zhang, Lu & Wang, Mingkai, 2023. "Rapid synchronous state-of-health diagnosis of membrane electrode assemblies in fuel cell stacks," Applied Energy, Elsevier, vol. 330(PA).
    19. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
    20. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3101-:d:1677580. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.