Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation

doi:10.1016/j.etran.2026.100553

Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation

2026-01-27

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DOI：10.1016/j.etran.2026.100553

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• A semi-supervised method predicts battery end of life (EOL) for early quality classification. • Sequence-sampling data augmentation scheme efficiently expands the dataset, saving time and cost. • Masked autoencoder-based approach automatically extracts features. • The method achieves high-precision EOL prediction with labeled data from only 20% of the experimental cells.

Facilitating battery quality classification: Early life prediction with sequence-sampling data augmentation

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