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Energies 2017, 10(8), 1237; https://doi.org/10.3390/en10081237

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

1
Department of Drone System, Chodang University, Muan-gun 58530, Jeollanam-do, Korea
2
Department of Fire Administration, Chodang University, Muan-gun 58530, Jeollanam-do, Korea
*
Author to whom correspondence should be addressed.
Received: 11 July 2017 / Revised: 16 August 2017 / Accepted: 18 August 2017 / Published: 21 August 2017
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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). View Full-Text
Keywords: equivalent circuit models (ECM); extended Kalman filter (EKF); state of charge (SOC); state of power (SOP) equivalent circuit models (ECM); extended Kalman filter (EKF); state of charge (SOC); state of power (SOP)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Jung, S.; Jeong, H. Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs. Energies 2017, 10, 1237.

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