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Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs

Author

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  • Sunghun Jung

    (Department of Drone System, Chodang University, Muan-gun 58530, Jeollanam-do, Korea)

  • Heon Jeong

    (Department of Fire Administration, Chodang University, Muan-gun 58530, Jeollanam-do, Korea)

Abstract

Customer requirements for unmanned aerial vehicles (UAVs) with long flight times are increasing exponentially in the personal, commercial, and military use areas. Due to their limited payload, large numbers of on-board battery packs cannot be used and this is the main reason behind the need for battery management software (BMS) packages with state of charge (SOC) estimation functions to increase the flight time. At the same time, as the UAV application range has extended widely, the size of UAVs has increased and heavy-duty UAVs are slowly appearing. As a result, the system operating power of the UAVs has been increased tremendously and their safe system power operation has become an issue. This is the main reason for the need of BMS having state of power (SOP) estimation functions. In this work a 6 S Li-Po battery pack is simulated with two ladder equivalent circuit models (ECMs) considering an impedance effect whose parameters are found using hybrid pulse power characterization (HPPC) current patterns with parameter determination using the table-based linear interpolation (TBLI) method. Two state estimation methods, including the current integration method and the extended Kalman filter (EKF) method are developed and the estimation accuracies of SOC and SOP are compared. Results show that the most accurate SOC estimation turns out to be 0.1477% (indoor test with HPPC), 0.1324% (outdoor test with 0 kg payload), and 0.2021% (outdoor test with 10 kg payload). Also, the most accurate SOP estimation error turns out to be 1.2% (indoor test with HPPC), 3.6% (outdoor test with 0 kg payload), and 4.2% (outdoor test with 10 kg payload).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1237-:d:109143
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    References listed on IDEAS

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    1. Zuchang Gao & Cheng Siong Chin & Wai Lok Woo & Junbo Jia, 2017. "Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO 4 Battery in Actual Embedded Application," Energies, MDPI, vol. 10(1), pages 1-22, January.
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    Cited by:

    1. Can Aksakal & Altug Sisman, 2018. "On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 11(1), pages 1-14, January.
    2. Sunghun Jung & Yonghyeon Jo & Young-Joon Kim, 2019. "Aerial Surveillance with Low-Altitude Long-Endurance Tethered Multirotor UAVs Using Photovoltaic Power Management System," Energies, MDPI, vol. 12(7), pages 1-14, April.
    3. Yongjie Zhai & Hailong Zhao & Meng Zhao & Songming Jiao, 2018. "Design of Electric Patrol UAVs Based on a Dual Antenna System," Energies, MDPI, vol. 11(4), pages 1-8, April.
    4. Shaojie Ai & Jia Song & Guobiao Cai, 2022. "Sequence-to-Sequence Remaining Useful Life Prediction of the Highly Maneuverable Unmanned Aerial Vehicle: A Multilevel Fusion Transformer Network Solution," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    5. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.
    6. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    7. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    8. 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.

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