VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals
arXiv:2605.20742v1 Announce Type: new 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. However, as battery systems and operating scenarios become increasingly complex, battery fault diagnosis and maintenance require stronger cross-domain adaptability and human-AI collaboration. Traditional fault detection and diagnosis methods are usually designed for specific scenarios and predefined workflows, making them less effective i
The proliferation of electric vehicles and increasing complexity of battery systems are driving demand for more sophisticated and adaptive fault detection methods that leverage AI.
Reliable and safe operation of EV batteries is crucial for consumer adoption and the broader energy transition, making advanced AI-driven diagnostics a critical enabler.
Traditional rule-based fault detection is becoming obsolete as AI agents offer more adaptable, cross-domain, and human-AI collaborative solutions for battery maintenance.
- · EV manufacturers
- · Battery diagnostic AI developers
- · Automotive AI integrators
- · EV owners
- · Traditional diagnostic tool providers
- · Less adaptive battery system designers
Improved safety and reliability of electric vehicles due to advanced battery fault detection.
Reduced maintenance costs and extended lifespan for EV batteries, increasing EV affordability and sustainability.
Accelerated development and adoption of AI agents in other complex industrial systems beyond automotive, seeking similar improvements in predictive maintenance and operational reliability.
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Read at arXiv cs.AI