Traceable Fault Diagnosis for Battery Energy Storage Systems via Retrieval-Augmented Multi-Agent O&M Assistant
arXiv:2607.01992v1 Announce Type: new Abstract: 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 operationa
The increasing scale and complexity of BESS deployments necessitate more sophisticated and automated O&M solutions to ensure reliability and efficiency.
This development enhances the operational lifespan and safety of critical energy infrastructure, reducing human error and improving diagnostic speed for BESS.
BESS O&M shifts from purely human-intensive monitoring to AI-assisted diagnosis, allowing for more precise interventions and predictive maintenance.
- · Battery energy storage system operators
- · AI software providers
- · Renewable energy sector
- · Industrial IoT platform developers
- · Traditional manual maintenance services
- · BESS operators without advanced diagnostic tools
Improved reliability and reduced downtime for large-scale energy storage systems become standard.
This drives down the operational costs of renewable energy projects, accelerating their deployment and economic viability.
The increased stability and efficiency of grid-scale storage systems support a more rapid and widespread transition to renewable energy sources, impacting energy markets and national energy security.
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Read at arXiv cs.AI