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).
Highlights
With the advent of the fourth industrial revolution era, the use of unmanned aerial vehicles (UAVs) has recently emerged
Since this paper focuses on a small size agricultural UAV whose payload is limited and which faces high crash risk, the equivalent circuit models (ECMs) including only an impedance characteristic is chosen to quickly and exactly estimate battery state of charge (SOC) and state of power (SOP) states
Ksoc = 1, but the most accurate SOP estimation occurs when the extended Kalman filter (EKF) is not applied with Ksoc = 1
Summary
With the advent of the fourth industrial revolution era, the use of unmanned aerial vehicles (UAVs) has recently emerged. Due to the previously mentioned issues, SOC and SOP measurements of the Li-ion battery have been in the spotlight for the optimal control of the battery power management in the renewable energy industry and vehicle industry fields, including electric vehicles (EVs) and UAVs. To understand the characteristics of Li-Po batteries, the battery equivalent circuit model (ECM). Since this paper focuses on a small size agricultural UAV whose payload is limited and which faces high crash risk, the ECM including only an impedance characteristic is chosen to quickly and exactly estimate battery SOC and SOP states. The exact state estimation using an ECM is dependent on the accuracy of these parameters To predict these parameters, open circuit voltages (OCVs) are measured by applying specific current patterns to the battery in various test environments (temperature, SOC, C-rate, etc.).
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