Abstract:
Objective In Hybrid Energy Storage Systems (HESS), the differences among heterogeneous storage units in response characteristics, degradation mechanisms, and energy management strategies lead to strong coupling, multi-timescale interactions, and cross-hierarchical dynamic behaviors. Traditional monitoring and early-warning methods for conventional energy storage systems are therefore insufficient to satisfy the safety perception requirements under complex operating conditions. To address these challenges, this paper presents a systematic review of key technologies for HESS safety monitoring and early warning, aiming to provide references for the safe operation and intelligent maintenance of multi-energy storage systems.
Method Specifically, this study investigated HESS from the perspectives of topology, storage unit characteristics, and application scenarios. First, the engineering adaptability of passive, semi-active, and fully active topologies was comparatively analyzed. Subsequently, the development status of conventional electrical monitoring and specialized sensing technologies—including electrochemical impedance spectroscopy (EIS), fiber Bragg grating (FBG), and acoustic emission (AE)—was reviewed. Key technical challenges such as multi-physics coupling perception, state decoupling and observability, and multi-timescale data synchronization were further discussed. Finally, the performance differences and application boundaries of physics-based, data-driven, and hybrid-driven early-warning methods were comparatively analyzed.
Result The results indicate that fully active topologies exhibit superior state perception accuracy and energy regulation capability, while suffering from higher system cost and control complexity. In contrast, semi-active topologies have become the mainstream engineering solution due to their balanced performance in terms of economy, flexibility, and scalability. Meanwhile, multi-source heterogeneous data fusion and complex coupled-state decoupling remain critical bottlenecks restricting the accuracy and generalization capability of HESS safety early-warning systems. Existing demonstration projects show that multi-level safety early-warning frameworks integrating physics-based models and intelligent diagnostic algorithms have achieved minute-level early warning capabilities and significantly improved fault identification and operation-maintenance decision-making performance.
Conclusion Considering current technological trends, future HESS monitoring and early-warning systems are expected to evolve toward standardized sensing, heterogeneous device interoperability, artificial intelligence-assisted decision-making, and full-lifecycle digital twins, thereby promoting the transition of energy storage safety management from "passive alarm" to "proactive prediction and intelligent control".
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