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Flight Time Estimation for Continuous Surveillance Missions Using a Multirotor UAV

Author

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  • Sunghun Jung

    (Department of Electric Vehicle Engineering, Dongshin University, Jeollanam-do 58245, Korea)

  • Yonghyeon Jo

    (Department of Energy Mechanical Facility, Dongshin University, Jeollanam-do 58245, Korea)

  • Young-Joon Kim

    (Department of Electronic Engineering, Gachon University, Gyeonggi-do 13120, Korea)

Abstract

To achieve the continuous surveillance capable multirotor type solar-powered unmanned aerial vehicle (UAV), we develop the photovoltaic power management system (PPMS) which manages power from photovoltaic (PV) modules and a battery pack to support the power of the UAV. To estimate the possible flight time of the UAV, we use the concept of state of charge (SOC) estimation based on the extended Kalman filter (EKF) and complementary filter (CF) and then calculate the possible flight time by using the slope of the SOC graph during hovering flight mode. According to the results, estimated flight time increases up to 54.14 min at 11:00 a.m. and decreases down to 6.70 min at 18:00 p.m.

Suggested Citation

  • Sunghun Jung & Yonghyeon Jo & Young-Joon Kim, 2019. "Flight Time Estimation for Continuous Surveillance Missions Using a Multirotor UAV," Energies, MDPI, vol. 12(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:867-:d:211203
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    References listed on IDEAS

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    1. Sunghun Jung & Heon Jeong, 2017. "Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs," Energies, MDPI, vol. 10(8), pages 1-13, August.
    2. Zhiwei He & Mingyu Gao & Caisheng Wang & Leyi Wang & Yuanyuan Liu, 2013. "Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model," Energies, MDPI, vol. 6(8), pages 1-18, August.
    3. 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.
    4. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
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    Cited by:

    1. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.

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