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Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft

Author

Listed:
  • Manuel R. Arahal

    (Department of Systems Engineering and Automation, Universidad de Sevilla, 41092 Seville, Spain
    These authors contributed equally to this work.)

  • Alfredo Pérez Vega-Leal

    (Department of Electronic Engineering, Universidad de Sevilla, 41092 Seville, Spain
    These authors contributed equally to this work.)

  • Manuel G. Satué

    (Department of Systems Engineering and Automation, Universidad de Sevilla, 41092 Seville, Spain)

  • Sergio Esteban

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

Abstract

This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators.

Suggested Citation

  • Manuel R. Arahal & Alfredo Pérez Vega-Leal & Manuel G. Satué & Sergio Esteban, 2024. "Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft," Energies, MDPI, vol. 17(20), pages 1-12, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5161-:d:1500359
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    References listed on IDEAS

    as
    1. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    2. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
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