This paper presents a systematic review of energy management strategies (EMSs) for fuel cell hybrid unmanned aerial vehicles (UAVs). It begins by explaining the necessity of hybrid energy systems. This paper then categorizes existing EMSs into three main classes: rule-based, optimization-based, and learning-based. It provides an in-depth analysis of the core principles, technical advantages, and application challenges for each class. The review also traces the evolution of these strategies from experience-dependent methods to data-driven and autonomous learning approaches. A key finding is that future EMSs will not operate as standalone control modules. By addressing the limitations of current studies, this paper identifies four key development trends: multi-objective collaborative optimization, joint energy-task planning, safe deployment from simulation to real-world environments, and high-fidelity dynamic validation. This work aims to offer theoretical guidance and technological foresight for the research and development of next-generation, high-performance, and high-reliability hydrogen-powered UAVs.
Wu et al. (Wed,) studied this question.