Hydrogen fuel cell hybrid unmanned aerial vehicle energy management method, system and storage medium
By combining neural networks and game-theoretic decision-making mechanisms, the energy distribution between hydrogen fuel cells and power batteries is optimized, solving the problems of fuel economy and inconsistency in decay process in the energy management strategies of UAV hybrid power systems in existing technologies, and improving the overall economy and environmental adaptability of UAVs.
CN121871839BActive Publication Date: 2026-06-09TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY +1
Patent Information
- Authority / Receiving Office
- CN Β· China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-09
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Figure CN121871839B_ABST
Abstract
The present application relates to the technical field of hybrid unmanned aerial vehicle energy management, in particular to a hydrogen fuel cell hybrid unmanned aerial vehicle energy management method, system and storage medium, the method specifically comprises: based on the real-time running state data of the unmanned aerial vehicle and the load demand power of each step within the prediction step length, solving the expected output power of each power source in the multi-objective optimization cost function of the power source; based on the load demand power and the expected output power of each power source, solving the hydrogen fuel cell and power battery energy game Nash equilibrium solution in the bargaining game whole machine energy model, and performing whole process energy management control according to the solved Nash equilibrium solution. The present application overcomes the problem that the existing unmanned aerial vehicle hybrid power system energy management strategy only considers the single power source life degradation, takes into account the fuel economy and durability of each power source, realizes dynamic self-adaptive adjustment of complex and variable working conditions, and effectively enhances the whole machine economy and complex environment adaptability of the unmanned aerial vehicle.
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