The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems.

The accurate and reliable SOC (state of charge) estimation is difficult, although it is a core factor especially for battery energy storage system. Current studies of SOC estimation theories and models are mainly focused on voltage model, current model, resistance model, intelligent algorithm model, and hybrid model. Specific attentions were paid to estimate the model and their impact factors on SOC. Then, a critical review of available literature on major SOC issues was presented. Influencing factors on SOC were studied and analyzed, such as dynamic hysteresis characteristics, temperature, aging, self-discharge, and rate of charge/discharge. The economy and practicability of the SOC estimation are analyzed and compared objectively.