Electric vehicle battery fault detection and diagnosis agent: Descriptive text modeling of battery digital signals

With the rapid proliferation of electric vehicles, the safety and reliability of lithium-ion batteries have become critical concerns. Effective anomaly detection is essential for ensuring safe battery operation. However, as battery systems and operating scenarios become increasingly complex, Battery fault diagnosis and maintenance increasingly require stronger adaptability to heterogeneous operating scenarios and intelligent human–AI collaboration. Traditional fault detection and diagnosis paradigms are usually tailored to specific scenarios and predefined workflows, making them less effective in complex real-world applications. To address the scarcity of open-source battery fault report corpora and the lack of unified maintenance knowledge representation, this study investigates a descriptive text modeling approach for battery signal reports. Monitoring signals, statistical features, anomaly records, and state assessment results are transformed into structured and readable natural language descriptions, thereby constructing a language corpus for battery health diagnosis and maintenance. Based on this framework, we propose VBFDD-Agent, a vehicle battery fault detection and diagnosis agent for automotive-grade battery systems. VBFDD-Agent integrates descriptive battery-state texts, historical case retrieval, local maintenance manuals, and large language model reasoning to generate structured diagnostic results and maintenance recommendations. Experimental results show that the proposed framework can accurately perform anomaly monitoring and provide flexible, efficient, and actionable maintenance suggestions. Expert evaluation further confirms the practical value of the generated recommendations.