Unmanned Aerial Vehicles (UAVs) present a promising solution for urban logistics, where an effective energy management strategy guided by optimal path planning is crucial for reducing operational costs and extending system lifespan. This study begins by analyzing the wind field distribution in a specific urban area of Chengdu using Computational Fluid Dynamics, and establishes a data-driven power prediction model to evaluate UAV energy consumption. A hybrid wind-field-aware A* with Ant Colony Optimization algorithm is subsequently proposed to compute the optimal flight path that balances energy consumption and distance, generating corresponding power demand profiles for the ensuing energy management strategy. Finally, a Deep Q-Learning (DQN)-based energy management strategy is implemented to regulate power distribution between the fuel cell and the battery, aiming to minimize hydrogen consumption and stabilize the power output of the primary source. Experimental results demonstrate that the proposed path planning method can effectively reduce energy consumption across different scenarios while causing only a marginal increase in travel distance. In addition, the DQN-based strategy significantly suppresses fuel cell power fluctuations at the cost of only a slight increase in hydrogen consumption, thereby demonstrating the effectiveness of the path-planning-informed energy management strategy.
Ji et al. (2026) studied this question.