Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

• Published in: Fuel cells (Weinheim an der Bergstrasse, Germany) Vol. 23; no. 2; pp. 221 - 236
• Main Authors: Quan, Rui, Li, Zhongxin, Liu, Pin, Li, Yangxin, Chang, Yufang, Yan, Huaicheng
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.04.2023
• Subjects: Algorithms; Consumption; direction prediction optimal foraging algorithm; Energy management; Energy storage; fuel cell unmanned aerial vehicles; Fuel cells; Fuzzy control; Fuzzy logic; Genetic algorithms; hybrid energy storage system; Hybrid systems; Hydrogen; Lithium-ion batteries; minimum hydrogen consumption; Optimization; State machines; Storage systems; Unmanned aerial vehicles
• ISSN: 1615-6846; 1615-6854
• DOI: 10.1002/fuce.202200121

In hybrid energy storage systems of fuel cell unmanned aerial vehicles (UAVs), achieving energy management while minimizing hydrogen consumption is the main goal for economic aspects and endurance enhancement. The external energy maximization strategy (EEMS) and the equivalent consumption minimization strategy (ECMS) are commonly used energy management strategies. However, they use a gradient descent approach, which converges slowly and does not guarantee the optimal solution. Thus, this paper proposes an optimization method based on a direction prediction optimal foraging algorithm (OFA/DP), which has the advantages of high optimization capability and simple parameter definition. In this study, the hybrid energy storage system comprises fuel cells and lithium‐ion batteries for powering UAVs. To verify the validity of the proposed strategy, it is compared with rule‐based and optimized methods of state machine control, fuzzy logic control based on frequency separation, ECMS, EEMS, and genetic algorithm. The obtained results confirm the superiority of the proposed OFA/DP‐based EEMS method with an efficiency of 88.65% and a minimum hydrogen consumption of 19.06 g. Furthermore, it achieves optimal power distribution and leads to 38.62% minimization in hydrogen consumption.