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State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

Author

Listed:
  • Haitao Zhang

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

  • Ming Zhou

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

  • Xudong Lan

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The lack of endurance is an important reason restricting further development of unmanned aerial vehicles (UAVs). Accurately estimating the state of charge (SOC) of the Li-Po battery can maximize the battery energy utilization and improve the endurance of UAVs. In this paper, the main current methods for estimating the SOC of vehicles were explored and discussed to unveil their advantages and disadvantages. In addition, the extended Kalman filter algorithm based on an equivalent circuit model was used to estimate SOC of power-type Li-Po batteries at different temperatures. The result showed that the closed-loop control method can effectively improve the battery life of small-sized electric UAVs.

Suggested Citation

  • Haitao Zhang & Ming Zhou & Xudong Lan, 2019. "State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter," Energies, MDPI, vol. 12(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3960-:d:277897
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    References listed on IDEAS

    as
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    2. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
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    5. Zhibing Zeng & Jindong Tian & Dong Li & Yong Tian, 2018. "An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-16, January.
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