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Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data — arXiv:2609.21932

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21932
License (as archived): CC0-1.0
Archived: 2026-10-10T01:45:19.063Z
SHA-256 of the archived text: 5052680ab13b2938e92573a284f0dbfa27675361b75b0afe53515fbf22b87c23
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How to cite

Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data — arXiv:2609.21932 — arXiv (en), https://arxiv.org/abs/2609.21932 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd0bc0907f2f (Metraipolis archive dc_cd0bc0907f2f, sha256 5052680ab13b2938e92573a284f0dbfa27675361b75b0afe53515fbf22b87c23)

BibTeX

@misc{dccd0bc0907f2f,
  title = {Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data — arXiv:2609.21932},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21932},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd0bc0907f2f; sha256 5052680ab13b2938e92573a284f0dbfa27675361b75b0afe53515fbf22b87c23},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21932. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. This paper presents a cross-expert framework using partial-charging measurements without requiring measured historical full-cycle capacity as an input. The RUL Expert captures long-term degradation from nominal 10-min segments sampled across a 30-cycle history, while the Capacity Expert characterizes recent battery behavior from statistical descriptors of nominal 40-min segments over ten consecutive cycles. Their complementary representations are integrated through feature-wise linear modulation for joint RUL and capacity prediction. A key contribution is a three-stage training strategy that progressively controls frozen and trainable components: supervised representation pretraining, independent expert pretraining, and final fusion training with both experts frozen. This staged optimization preserves expert-specific degradation knowledge while improving the balance between the two prediction tasks, with RUL treated as the primary prognostic objective. On two public battery-aging datasets, the reference configuration achieves mean RUL root-mean-square errors of 143.69 and 161.10 cycles and capacity errors of 12.36 and 7.28 mAh, respectively. On Dataset I, cross-expert fusion reduces both mean errors relative to either standalone expert. The proposed framework achieves the lowest reported RUL RMSE among the compared methods on both datasets while maintaining competitive capacity-estimation accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.21932 — 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 2026-10-10T01:14:16.662Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).