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NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling — arXiv:2610.21391

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21391
License (as archived): CC0-1.0
Archived: 2026-10-16T10:55:57.966Z
SHA-256 of the archived text: e5c8366008586208280a8d570fee66860dc5ad10e58795f865d0d45620aa1e03
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How to cite

NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling — arXiv:2610.21391 — arXiv (en), https://arxiv.org/abs/2610.21391 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_760a6749ddcd (Metraipolis archive dc_760a6749ddcd, sha256 e5c8366008586208280a8d570fee66860dc5ad10e58795f865d0d45620aa1e03)

BibTeX

@misc{dc760a6749ddcd,
  title = {NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling — arXiv:2610.21391},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21391},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_760a6749ddcd; sha256 e5c8366008586208280a8d570fee66860dc5ad10e58795f865d0d45620aa1e03},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21391. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Learning effective representations from battery operational history is essential for predictive health management and control-oriented applications. Existing approaches rely on response-dependent engineered features or short early-life windows, limiting generalisability and precluding use in prospective settings where future responses are unavailable. We propose NOCE-Net, a deep nested sequence model that learns control-enabling representations directly from raw cyclic operational or telemetric data. Our data pipeline, re-BatteryData, standardises heterogeneous aging datasets into semantically equivalent Full Equivalent Cycles (FECs) defined by absolute charge throughput, enabling end-to-end learning across diverse operational regimes. NOCE-Net combines multi-patch transformer encoders for intra-cycle dynamics with a GRU backbone for inter-cycle state evolution, operating on approximately $700$ batteries across ten public datasets spanning over $300$ operational profiles. In zero-shot evaluation on unseen battery chemistries and usage patterns, NOCE-Net achieves $13.7\%$ MAPE in voltage response prediction, outperforming strong long-sequence baselines by over $43\%$ while using $40 \times$ fewer parameters. Shape and component diagnostics of the learned hidden states reveal that the GRU's principal direction of evolution achieves Spearman $|\rho| \geq 0.95$ with the capacity fade trajectory on average across all datasets with monotone degradation profiles, including zero-shot test batteries, providing evidence that the representations encode degradation dynamics transferable to downstream tasks beyond battery response modelling. Code is available at this https URL .

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv eess.SY, nuovi lavori del giorno).
Categorie arXiv: Computational Engineering, Finance, and Science (cs.CE) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.21391 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T09:01:52.492Z dalla pagina https://arxiv.org/list/eess.SY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).