Search papers, labs, and topics across Lattice.
This paper introduces a traceable fault diagnosis assistant for battery energy storage systems (BESSs) that leverages retrieval-augmented multi-agent reasoning to synthesize operational data, domain knowledge, and visual evidence for improved decision-making. By employing BESS-specific task routing and schema-constrained natural-language database access, the system enhances reliability in diagnosing issues such as voltage inconsistencies and thermal abnormalities. Preliminary evaluations demonstrate its effectiveness in routing and diagnostic reasoning, highlighting its potential to streamline O&M decisions in complex energy systems.
A novel multi-agent assistant can diagnose BESS faults with unprecedented reliability by integrating diverse operational data and visual evidence.
Large-scale battery energy storage systems (BESSs) require O&M decisions that combine alarms, cell-level measurements, device topology, diagnostic tables, historical cases, and maintenance documents. Monitoring platforms can flag threshold violations, but they often cannot explain whether voltage inconsistency, resistance drift, short-circuit risk, capacity divergence, or thermal abnormality needs intervention. This digest presents a traceable BESS fault-diagnosis assistant that uses retrieval-augmented multi-agent reasoning to connect operational data, domain knowledge, visual evidence, and report generation. Reliability is improved through BESS-specific task routing, schema-constrained natural-language database access, hybrid text-image retrieval, and evidence-based answer synthesis. Preliminary internal evaluation is reported for routing, database access, and diagnostic reasoning.
