MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
arXiv:2605.20240v1 Announce Type: new Abstract: Battery health diagnostics today rely overwhelmingly on electrochemical signals measured at the cell terminals. A parallel literature has shown that magnetic sensing can resolve information that terminal-only measurements miss, but method development is limited by the absence, to the best of our knowledge, of public battery magnetic-measurement datasets paired with degradation labels. We release MagBridge-Battery v1.0, a synthetic dataset of 6,760 magnetic-field signatures that bridges real magnetic morphology from the Mohammadi-Jerschow Open Sci
The increasing demand for battery performance and longevity, especially in critical applications, is driving innovation in diagnostic methods beyond traditional electrochemical signals.
This development could enable more precise and earlier diagnosis of battery degradation, leading to improved battery safety, extended lifespan, and more efficient energy storage systems.
The availability of a public synthetic dataset for magnetic battery measurements will accelerate research and development in non-invasive battery diagnostics, potentially establishing magnetometry as a viable diagnostic tool.
- · Battery manufacturers
- · EV industry
- · Grid storage developers
- · AI/ML researchers in material science
- · Companies reliant solely on electrochemical battery diagnostics
Magnetometry becomes an established, non-invasive method for state-of-health diagnostics in batteries.
Improved battery management systems allow for longer battery life and reduced waste, impacting circular economy initiatives.
More reliable batteries enable faster adoption of electric vehicles and renewable energy storage, accelerating energy transition.
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Read at arXiv cs.LG