Bridging Raw Data and LLM Reasoning

Traditional battery fault detection often relies on numerical analysis of time-series data, which can struggle with complex, non-linear fault patterns. The VBFDD-Agent (Vehicle Battery Fault Detection and Diagnosis Agent) introduces a novel approach by transforming raw battery digital signals—such as voltage, current, and temperature—into descriptive text representations. By converting these numerical streams into a human-readable, semantic format, the system allows Large Language Models (LLMs) to leverage their reasoning capabilities to identify, classify, and diagnose battery health issues that might be missed by purely statistical methods.

The Descriptive Modeling Pipeline

Instead of feeding raw data directly into a model, the VBFDD-Agent acts as a translation layer. It interprets the temporal dynamics of battery signals and encodes them into a structured text format that captures the physical state of the battery. This descriptive modeling allows the agent to:

• Contextualize anomalies: By turning numerical spikes or drifts into descriptive events, the model can differentiate between normal operational noise and genuine fault signatures.
• Improve Interpretability: Because the input is text-based, the diagnostic process becomes more transparent, allowing engineers to understand the 'why' behind an AI-generated fault alert.
• Enhance Diagnostic Accuracy: By utilizing the pre-trained knowledge of LLMs, the agent can correlate specific signal patterns with known failure modes in electric vehicle battery management systems (BMS), leading to more robust detection of degradation and safety-critical faults.