Algorithm optimizes solar panel selection for UAVs' battery charging, indicating potential for efficient deployments.
Growing military and civilian reliance on multirotor unmanned aerial vehicles (UAVs) accentuates the need for resilient, field-deployable power supplies capable of continuous operation whenever conventional grids are inaccessible or hazardous. The paper addresses this challenge by proposing the comprehensive methodology for choosing photovoltaic (PV) converters and complementary storage technologies that can sustain round-the-clock wireless charging of UAV batteries under Northern-Ukrainian insolation conditions. The problem is formulated as joint optimisation of the daily number of battery charging cycles, overall-energy demand and PV-array configuration while accounting for climatic variability and flight-mission logistics.The research objective is to substantiate the autonomous solar power plant sized to support three DJI Mavic 3 class UAVs performing uninterrupted aerial reconnaissance. Methods include energy-balance modelling, scenario scheduling of flight/charge cycles, comparative efficiency analysis of mono-, poly- and thin-film PV modules, and evaluation of two battery chemistries—LiFePO₄ and AGM/GEL—using manufacturer data and recent literature.The key results show that, for Chernihiv’s mean global tilted irradiation of 3.61 kWh m⁻² day⁻¹, a 3.5 kWh daily yield suffices to cover 35 full 100 W charges. LiFePO₄ storage (round-trip η ≈ 95 %) cuts required array area by 5–15 % versus AGM/GEL (η ≈ 85 %), enabling a lighter, cheaper and more mobile installation. Among thirteen commercially available panels analysed, high-efficiency monocrystalline modules (η > 20 %) teamed with LiFePO₄ batteries minimise total mass (≈ 55 kg) and capital cost (< 400 USD) while meeting mission autonomy. The proposed selection algorithm may be adapted to other regions and fleet sizes, and the findings facilitate rapid deployment of reliable solar-powered UAV charging outposts for de-fence, rescue and environmental monitoring tasks.
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Shokodko et al. (2025) studied this question.
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