## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.3 Energy Management  
UAV energy management is a multifaceted issue involving energy harvesting, storage, distribution, and usage. As communication relays in SAGINs, UAVs not only need to sustain flight and basic communication functions but also handle additional energy-intensive tasks such as network communication, traffic scheduling, and computation offloading. Effectively managing UAV energy has therefore become a key research focus. This section summarizes recent research efforts in UAV energy management, covering critical areas such as energy harvesting, power control, data transmission, and deployment optimization.  
1. _Energy Harvesting_: UAV energy harvesting techniques aim to support operations by replenishing power through methods like solar energy conversion, wireless energy transfer, and hybrid energy recycling. Solar-powered UAVs convert sunlight into electricity, offering an environmentally friendly advantage. UAVs from HAPs benefit from superior solar exposure, enhancing their energy harvesting efficiency. However, for low-altitude UAVs, environmental obstructions significantly hinder efficiency. To address this, wireless energy transfer has emerged as a crucial solution. [PERSON] et al. [115] explored RF energy harvesting in a cooperative communication scenario, where a UAV relay collected energy from RF signals transmitted by a satellite using power-splitting relay protocols. Based on this, they proposed a UAV relay-assisted network optimization framework with cooperative energy harvesting. Additionally, [PERSON] et al. [116] introduced a power control strategy for joint communication, efficiently utilizing harvested energy while reducing channel interference over limited time periods. [PERSON] et al. [117] further optimized network throughput under security constraints with energy harvesting.
2. _Energy-Saving Data Transmission_: The high backhaul link load caused by massive data flow and limited cache capacity often degrades system communication and energy efficiency. To address this, [PERSON] et al. [118] proposed an energy-aware coded caching strategy. This method optimizes UAV transmission power through content placement and coded transmission (CT) techniques. To reduce energy consumption in link data transmission, [PERSON] et al. [119] developed an RL-based hybrid data scheduling approach. This method uses energy consumption as the optimization reward during iterative processes and outperforms traditional shortest-path routing algorithms in terms of network energy usage and average latency.
3. _Energy-Saving Task scheduling_: Real-time computation offloading tasks increase network burdens, such as distributing incoming computation tasks to cloud nodes. Imbalanced task scheduling can lead to resource waste or even node failures. For power-intensive tasks, optimizing energy consumption is necessary and justified. [PERSON] et al. [120] proposed a heuristic algorithm to minimize the maximum energy consumption among multiple UAVs while meeting delay constraints. With the exponential growth of IoT devices and user requests, [PERSON] et al. [121] introduced a hierarchical offloading strategy. Terminal devices initially offload requests to UAV-based edge computing resource pools, and when a node's resources are exhausted,tasks are further offloaded to other idle nodes. This study used the Nash equilibrium to determine the optimal offloading strategy.