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arXiv · 2601.03007

From inconsistency to decision: explainable operation and maintenance of battery energy storage systems

Jingbo Qu·Yijie Wang·Yujie Fu·Putai Zhang·Weihan Li·Mian Li
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Abstract

Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision-support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency-driven operation and maintenance paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guidance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in-service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost-effective integration of energy storage into modern power systems.

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Jingbo Qu, Yijie Wang, Yujie Fu, Putai Zhang, Weihan Li, Mian Li. 2026-01-07. From inconsistency to decision: explainable operation and maintenance of battery energy storage systems. https://doi.org/10.1016/j.xcrp.2026.103388

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