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The paper proposes a physics-informed deep learning approach for lithium-ion battery state-of-health estimation using incomplete discharge curves, achieving high accuracy and enabling real-time degradation trend prognosis.
This paper introduces PiDDM, a physics-informed differentiable degradation modeling framework that embeds battery degradation kinetics into neural network training to improve lithium-ion battery state-of-health prediction accuracy and physical consistency across diverse cycling protocols.
Proposes RoSIP-Batt, a physics-guided multi-task transformer for joint estimation of battery State of Health and Remaining Useful Life, achieving state-of-the-art accuracy on NASA, MIT-Stanford, and HUST datasets.
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.