state-of-health

The paper proposes BatteryMFormer, a multi-level Transformer for early battery degradation trajectory forecasting that integrates aging-condition-aware decoding, meta degradation pattern memory, and dual-view encoding to capture multi-level degradation structures and SOC-localized variations, consistently outperforming state-of-the-art baselines across four battery domains.

This paper introduces MagBridge-Battery, a synthetic dataset of 6,760 magnetic-field signatures for Li-ion battery state-of-health diagnostics, combining real magnetic morphology with real degradation labels to bridge the gap in public magnetic-sensing battery data.