<![CDATA[General Background: The rapid advancements in solar-powered unmanned aerial vehicles (UAVs) have increased interest in optimizing their energy management systems (EMS) to enhance performance and flight endurance. Specific Background: Effective EMS in solar UAVs requires advanced strategies for solar energy harvesting, energy storage, and power distribution to maximize operational efficiency under varying environmental conditions. Knowledge Gap: Despite recent progress, challenges remain in energy conversion efficiency, battery storage capacity, and the integration of intelligent predictive control mechanisms, limiting the UAVs’ ability to operate autonomously for extended periods. Aims: This review investigates current EMS optimization strategies for solar-powered UAVs, emphasizing multi-objective optimization techniques, energy management algorithms, and the impact of environmental conditions on UAV performance. It also explores the role of artificial intelligence (AI) and machine learning in improving EMS efficiency. Results: Studies highlight that multi-objective genetic algorithms (MOGAs) effectively balance energy allocation between propulsion, battery storage, and payload, leading to significant endurance improvements. Fuzzy logic-based power management systems enhance energy efficiency by dynamically adjusting power distribution based on real-time UAV energy demands. Adaptive energy management strategies that integrate environmental and operational data improve flight times by up to 30% under extreme conditions. Novelty: This review synthesizes state-of-the-art EMS strategies, identifying key optimization techniques and emerging AI-driven solutions for adaptive and predictive energy management. By consolidating findings from diverse methodologies, it provides a comprehensive assessment of how intelligent control systems enhance UAV autonomy. Implications: The findings underscore the necessity of developing more efficient power conversion technologies, advanced battery storage solutions, and AI-based EMS frameworks to enable long-duration UAV operations. Future research should focus on refining these technologies to improve UAV performance in energy-intensive applications such as surveillance, environmental monitoring, and disaster response. Highlights: Optimization: MOGAs and fuzzy logic improve energy efficiency and endurance. Adaptation: Real-time power adjustments enhance UAV performance in harsh conditions. AI Integration: Machine learning enables predictive, autonomous energy management. Keywords: Solar-powered UAVs, Energy Management Systems, Optimization Algorithms, Adaptive Control, Artificial Intelligence]]>
