Integrated Energy-Efficient Planning and Management Framework for Autonomous Long-Endurance Flight of Hydrogen Fuel Cell/Battery Hybrid UAVs

被引:0
作者
Guo, Xiaoyu [1 ]
Song, Xiaowei [2 ]
Zeng, Dan [2 ]
Dong, Zhen [3 ]
Yu, Xiang [2 ]
Liu, Lu [1 ]
Fang, Yuguang [4 ]
机构
[1] City Univ Hong Kong, Dept Biomed Engn, Kowloon, Hong Kong, Peoples R China
[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[3] Harbin Inst Technol, Sch Elect Engn & Automat, Harbin 150001, Peoples R China
[4] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Trajectory; Autonomous aerial vehicles; State of charge; Trajectory planning; Batteries; Hydrogen; Cost function; Vectors; Splines (mathematics); Polynomials; Fuel cell (FC); unmanned aerial vehicle (UAV); trajectory planning; energy management; energy efficiency; UNMANNED AERIAL VEHICLE; STRATEGY; DESIGN;
D O I
10.1109/TMECH.2024.3524751
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The fuel cell/battery hybrid energy topology can enable autonomous long-endurance flight of multirotor unmanned aerial vehicles (UAVs) for persistent missions. Aiming at further enhancing flight endurance, energy-efficient trajectory planning, and energy management (power allocation between the hydrogen fuel cell and battery) have been widely investigated. However, existing studies mostly design the trajectory planning and energy management separately, largely neglecting the shared information and objectives between planning and management layers. This article introduces an integrated planning and energy management framework be leveraging the trajectory planning results to provide an optimized reference for online energy management. A B-spline parameterized flight trajectory is first generated based on a cost function that balances energy efficiency and dynamic feasibility. Then, an optimal state-of-charge (SOC) trajectory is constructed based on the predictive flight information. Finally, an adaptive equivalent consumption minimization strategy is designed to track the optimal SOC trajectory and distribute power online. In addition, parameter identification is introduced to update the fuel cell characteristics according to flight conditions, enhancing environmental adaptability. Experimental results on a self-developed fuel cell/battery hybrid UAV validate the performance of the proposed method.
引用
收藏
页数:11
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