AI Multi-Agent System Diagnoses Battery Storage Faults

Managing large-scale Battery Energy Storage Systems (BESSs) requires complex operations and maintenance (O&M) decisions, often involving the synthesis of diverse data sources like alarms, cell-level measurements, device topology, diagnostic tables, historical cases, and maintenance documents. Existing monitoring platforms can flag issues but frequently lack the ability to explain the root cause of problems such as voltage inconsistencies or thermal abnormalities. Researchers have developed a traceable BESS fault-diagnosis assistant that leverages retrieval-augmented multi-agent reasoning. This AI system is designed to connect various data points, including operational data, specialized domain knowledge, visual evidence, and past maintenance records, to generate comprehensive diagnostic reports. The assistant enhances reliability through several key features: BESS-specific task routing, schema-constrained natural-language access to databases, hybrid text-image retrieval capabilities, and an evidence-based approach to synthesizing answers. Preliminary internal evaluations have shown promising results in task routing, database access, and diagnostic reasoning.