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A Nested Optimization Framework for Fuel Cell–Battery UAV Powertrain Sizing
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DOI：10.1109/TAES.2025.3627569.png)
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摘要
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This article proposes a nested optimization framework to design an optimal fuel cell (FC)–battery hybrid powertrain for uncrewed aerial vehicles (UAVs). The framework integrates a genetic algorithm to explore the powertrain design space and the dynamic programming-based energy management strategies (EMSs) to evaluate each candidate configuration’s feasibility and cost under a typical mission profile. The physics-based UAV dynamics model is embedded in the optimization loop to capture the feedback among powertrain sizing, UAV weight, and propulsion power demand. As powertrain components vary in size across design options, the corresponding changes in UAV weight directly alter the required thrust and energy consumption, which are iteratively updated throughout the optimization process. Two distinct EMS objectives, minimizing hydrogen consumption (MHC) and minimizing total cost (MTC), are systematically compared across two powertrain topologies (Full-active and FC semi-active). Optimization results reveal that the MTC strategy consistently yields lighter and more cost-effective designs than MHC. In the full-active topology, MTC reduces UAV weight by 12.50% and total cost by 19.15%. In the FC semi-active topology, UAV weight and total cost are reduced by 7.66% and 12.08%, respectively. The FC semi-active topology combined with the MTC strategy achieves the best overall performance, delivering the lowest UAV weight and total cost, relative to the full-active topology under MHC, UAV weight decreases by 14.92% and total cost by 23.95%. These findings highlight the strong coupling between powertrain sizing, aerodynamics, and EMSs, emphasizing the importance of integrated optimization approaches in hybrid electric UAV design.
Keyword:
Energy management strategy (EMS)
fuel cell (FC) UAVS
optimal sizing
optimization
powertrain
期刊
IF:
5.7
论文数:
758
被引数:
2.4W
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