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

Haitao Zhang, Ming Zhou and Xudong Lan
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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

Energies, 2019, vol. 12, issue 20, 1-15

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.

Keywords: unmanned aerial vehicle; power-type Li-Po battery; state of charge; extended Kalman filter algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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