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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics

Authors: Gunasekar, Sakthi Prabhu; Rangarajan, Prasanna Kumar;

MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics

Abstract

MagBridge-Battery v1.0 is the first dataset to bridge two previously disconnected worlds of battery research: magnetic-field sensing and electrochemical state-of-health diagnostics. Magnetometry is a promising route to non-invasive, contactless battery diagnostics, but progress has been bottlenecked by a structural gap in the available data — the largest open magnetometry archives carry no electrochemical health labels, while the richest state-of-health (SOH) datasets carry no magnetic signatures. MagBridge-Battery closes that gap. Using a bridging procedure that conditions the Mohammadi–Jerschow OSF magnetometry archive on electrochemical labels from the PulseBat dataset, it produces 6,760 labeled magnetic-field signatures for lithium iron phosphate (LFP) cells — enabling machine-learning research on magnetic battery diagnostics that was not previously possible at scale. The release contains 5,600 PulseBat-conditioned grounded samples, 600 synthetic sensor-anomaly samples derived from clean parents (four subtypes: sensor_dropout, calibration_drift, temporal_warp, periodic_interference), and 560 low-voltage Regime-B extrapolation samples. A cell-disjoint, parent-child-leakage-free primary benchmark split is verified to contain zero overlapping cells, zero cross-split parent-child pairs, and zero sample-ID overlap. Four benchmark tasks are defined: SOH regression, second-life classification (cutoff SOH = 0.85), three-class anomaly detection, and four-class anomaly subtype classification. Bridge validity is established through structural sanity invariants, distributional KS tests at grounded anchors, and a controlled label-shuffle ablation that collapses SOH regression from R² ≈ 0.77 to R² ≈ 0 — confirming that the bridge encodes input SOH non-trivially rather than producing label-aligned artifacts. Users are kindly requested to cite both this dataset DOI and the associated paper (see CITING.md in the bundle). Code, paper source, and reference implementations are available on GitHub at https://github.com/SakthiGs/MagBridge-Battery. ARXIV: https://arxiv.org/abs/2605.20240

Keywords

state of health, Li-ion, SOH, Magnetometry, Battery, out-of-distribution, LFP, Electric battery, anomaly detection, benchmark, second life, lithium iron phosphate, lithium iron phosphate battery, lithium-ion, synthetic dataset

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average