A progressive learning residuals based on multivariate Mamba and adaptive singular value decomposition method for remaining useful life prediction of lithium-ion batteries DOI
Hai‐Kun Wang, X. C. Dai, Lang Cui

и другие.

Ionics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 20, 2024

Язык: Английский

A review of Bayesian-filtering-based techniques in RUL prediction for Lithium-Ion batteries DOI
May Htet Htet Khine, Cheong Kim, Nattapol Aunsri

и другие.

Journal of Energy Storage, Год журнала: 2025, Номер 111, С. 115371 - 115371

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

1
A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting DOI
Zhi-Feng Liu,

Ya-He Huang,

Shu-Rui Zhang

и другие.

Applied Energy, Год журнала: 2024, Номер 377, С. 124741 - 124741

Опубликована: Окт. 21, 2024

Язык: Английский

Процитировано

4
RUL Prediction Method for Lithium‐Ion Batteries Based on the SOA‐ELM Algorithm DOI Creative Commons
Xiangdong Meng, Haifeng Zhang, Dexin Li

и другие.

Engineering Reports, Год журнала: 2025, Номер 7(3)

Опубликована: Март 1, 2025

ABSTRACT With the rapid advancement of electrochemical energy storage power stations and electric vehicles, lithium‐ion batteries have gained widespread adoption due to their high specific superior performance. However, increasing frequency safety incidents in facilities recent years has raised significant concerns. Effective monitoring battery conditions is crucial ensure systems support sustainable growth industry. Among key challenges, accurate prediction remaining useful life (RUL) essential for maintaining safe reliable operation management systems. This paper proposes an advanced RUL model that combines seagull optimization algorithm (SOA) with extreme learning machine (ELM) enhance accuracy. The proposed SOA‐ELM validated using NASA dataset, results demonstrate its effectiveness potential improving batteries. study contributes development more efficient systems, paving way safer solutions.

Язык: Английский

Процитировано

0
Remaining useful life prediction with uncertainty quantification for rotating machinery: A method based on explainable variational deep gaussian process DOI

Xiuli Liu,

Shiyue Cui, Qiao Wan

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130232 - 130232

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A progressive learning residuals based on multivariate Mamba and adaptive singular value decomposition method for remaining useful life prediction of lithium-ion batteries DOI
Hai‐Kun Wang, X. C. Dai, Lang Cui

и другие.

Ionics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 20, 2024

Язык: Английский

Процитировано

0