VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals
The VBFDD-Agent is a new approach for detecting and diagnosing faults in electric vehicle batteries. It utilizes descriptive text modeling to transform battery signals into structured natural language descriptions. This innovative framework aims to enhance battery maintenance and diagnosis by integrating historical data and AI reasoning.
- ▪The safety and reliability of lithium-ion batteries are critical as electric vehicles become more prevalent.
- ▪Traditional fault detection methods are often limited to specific scenarios, making them less effective in complex applications.
- ▪The VBFDD-Agent combines descriptive texts, case retrieval, and AI reasoning to provide actionable maintenance recommendations.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20742 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Artificial Intelligence arXiv:2605.20742 (cs) [Submitted on 20 May 2026] Title:VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals Authors:Joey Chan, Zhen Chen, Ershun Pan View a PDF of the paper titled VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals, by Joey Chan and 2 other authors View PDF HTML (experimental) Abstract: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.
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