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

TL;DR

This paper presents an innovative, explainable framework that transforms inconsistency diagnostics into actionable maintenance decisions for large-scale battery energy storage systems, significantly improving response efficiency and reducing costs.

Contribution

It introduces a novel inconsistency-driven paradigm combining multi-dimensional evaluation with language model reasoning for scalable, interpretable BESS operation and maintenance.

Findings

Reduced response time and operational costs by over 80%

Effective translation of diagnostics into maintenance actions

Demonstrated scalability with real-world field data

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.