A physics-informed deep learning framework for remaining useful life prediction of lithium-ion batteries with feature subset construction.
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| Title: | A physics-informed deep learning framework for remaining useful life prediction of lithium-ion batteries with feature subset construction. |
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| Authors: | Cheng, Hanlin1,2 (AUTHOR), Zhang, Lifeng1,2 (AUTHOR) lifeng.zhang@ncepu.edu.cn |
| Source: | Energy. Mar2026, Vol. 346, pN.PAG-N.PAG. 1p. |
| Subjects: | Lithium-ion batteries, Deep learning, Statistical models, Cost functions, Feature selection, Time series analysis |
| Abstract: | Addressing critical challenges in lithium-ion battery (LIBs) remaining useful life (RUL) prediction, namely weak feature correlations, limited model generalizability, and insufficient physical consistency, this study proposes a novel deep learning framework that integrates optimal feature subset construction and physics-informed constraints. To address inherent multi-scale variations and noise in raw health indicator sequences, Seasonal-Trend decomposition using Loess (STL) is employed, extracting structured temporal information from trend, seasonal, and residual components. This process enables multivariate feature analysis. Subsequently, a Wrapper-based feature selection algorithm identifies, reconstructs, and preserves the most predictive subset of health features, thereby strengthening feature relevance and capturing crucial degradation factors. For the model architecture, this paper introduces MSTEA-Net, which incorporates multi-scale temporal convolutional encoding and a cross-variable attention mechanism. This design comprehensively captures dynamic cross-feature dependencies and long-term temporal evolution patterns. Critically, to enhance physical plausibility and interpretability, a triple-composite loss function, integrating data-driven prediction errors with dual physics-based regularization terms, is formulated and applied during model training. Extensive experimental evaluations on the publicly accessible CALCE and TJU datasets demonstrate the superior performance of the proposed framework. It significantly outperforms mainstream benchmarks in both RUL prediction accuracy and end-of-life (EOL) cycle localization precision. Specifically, the method achieves reductions in MAE of 5.56% and 11.8% on the respective datasets, while consistently constraining EOL localization errors within a stringent threshold of two cycles. • A physics-informed deep learning framework for lithium-ion battery RUL prediction. • STL decomposition and Wrapper-based feature selection build optimal predictive feature subset. • MSTEA-Net extracts features and generate predictions across multiple scales. • With physics-informed constraints, triple-composite loss function dynamically optimizes network training. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191663791 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A physics-informed deep learning framework for remaining useful life prediction of lithium-ion batteries with feature subset construction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Hanlin%22">Cheng, Hanlin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lifeng%22">Zhang, Lifeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lifeng.zhang@ncepu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Mar2026, Vol. 346, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Addressing critical challenges in lithium-ion battery (LIBs) remaining useful life (RUL) prediction, namely weak feature correlations, limited model generalizability, and insufficient physical consistency, this study proposes a novel deep learning framework that integrates optimal feature subset construction and physics-informed constraints. To address inherent multi-scale variations and noise in raw health indicator sequences, Seasonal-Trend decomposition using Loess (STL) is employed, extracting structured temporal information from trend, seasonal, and residual components. This process enables multivariate feature analysis. Subsequently, a Wrapper-based feature selection algorithm identifies, reconstructs, and preserves the most predictive subset of health features, thereby strengthening feature relevance and capturing crucial degradation factors. For the model architecture, this paper introduces MSTEA-Net, which incorporates multi-scale temporal convolutional encoding and a cross-variable attention mechanism. This design comprehensively captures dynamic cross-feature dependencies and long-term temporal evolution patterns. Critically, to enhance physical plausibility and interpretability, a triple-composite loss function, integrating data-driven prediction errors with dual physics-based regularization terms, is formulated and applied during model training. Extensive experimental evaluations on the publicly accessible CALCE and TJU datasets demonstrate the superior performance of the proposed framework. It significantly outperforms mainstream benchmarks in both RUL prediction accuracy and end-of-life (EOL) cycle localization precision. Specifically, the method achieves reductions in MAE of 5.56% and 11.8% on the respective datasets, while consistently constraining EOL localization errors within a stringent threshold of two cycles. • A physics-informed deep learning framework for lithium-ion battery RUL prediction. • STL decomposition and Wrapper-based feature selection build optimal predictive feature subset. • MSTEA-Net extracts features and generate predictions across multiple scales. • With physics-informed constraints, triple-composite loss function dynamically optimizes network training. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.energy.2026.140288 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Lithium-ion batteries Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Time series analysis Type: general Titles: – TitleFull: A physics-informed deep learning framework for remaining useful life prediction of lithium-ion batteries with feature subset construction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheng, Hanlin – PersonEntity: Name: NameFull: Zhang, Lifeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03605442 Numbering: – Type: volume Value: 346 Titles: – TitleFull: Energy Type: main |
| ResultId | 1 |
