Traceable Fault Diagnosis for Battery Energy Storag... | AI Research

Large-scale battery energy storage systems (BESSs) are critical infrastructure that generate vast amounts of operational data, yet they often lack the ability to explain the root causes of performance issues. While current monitoring platforms can detect when a system crosses a safety threshold, they struggle to distinguish between complex issues like voltage inconsistency, resistance drift, or thermal abnormalities. This paper introduces a traceable fault-diagnosis assistant designed to bridge this gap by using a retrieval-augmented multi-agent system to synthesize operational data, domain knowledge, and visual evidence into actionable maintenance reports.

Addressing the Diagnostic Gap

The primary challenge in BESS maintenance is the fragmented nature of the information required to make a decision. Operators must manually reconcile alarms and cell-level measurements with device topology, historical case files, and technical maintenance documents. The proposed assistant automates this process, moving beyond simple threshold alerts to provide a clear, evidence-based explanation of whether a specific intervention is required.

How the Multi-Agent System Works

The system employs a multi-agent architecture that utilizes several specialized techniques to ensure accuracy and reliability:

• Task Routing: The system intelligently directs diagnostic tasks to the appropriate agents based on the specific BESS context.

• Database Access: It uses schema-constrained natural-language queries to interact with databases, ensuring that the information retrieved is precise and relevant to the system's structure.

• Hybrid Retrieval: The assistant combines text and image retrieval to analyze both numerical data and visual evidence, allowing for a more comprehensive assessment of system health.

• Synthesis: By integrating these diverse data sources, the system generates evidence-based answers that provide a clear rationale for its diagnostic conclusions.

Evaluating Performance

The authors conducted a preliminary internal evaluation to test the effectiveness of the assistant. The assessment focused on three core areas: the accuracy of the task routing mechanism, the reliability of the natural-language database access, and the overall quality of the diagnostic reasoning provided by the multi-agent framework. These tests serve as an initial validation of the system's ability to support complex O&M decision-making in real-world energy storage environments.