A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting.
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| Title: | A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting. |
|---|---|
| Authors: | Liu, Zhi-Feng1 (AUTHOR) liuzhifeng@tust.edu.cn, Huang, Ya-He1 (AUTHOR) huangyahe@mail.tust.edu.cn, Zhang, Shu-Rui1 (AUTHOR) zsr030923@mail.tust.edu.cn, Luo, Xing-Fu1 (AUTHOR) fu406699@mail.tust.edu.cn, Chen, Xiao-Rui1 (AUTHOR) 21021127@mail.tust.edu.cn, Lin, Jun-Jie1 (AUTHOR), Tang, Yu2,3 (AUTHOR), Guo, Liang4 (AUTHOR) guoliang@sgepri.sgcc.com.cn, Li, Ji-Xiang1 (AUTHOR) lijixiangtust@126.com |
| Source: | Applied Energy. Jan2025:Part D, Vol. 377, pN.PAG-N.PAG. 1p. |
| Subjects: | Remaining useful life, Probability density function, Deep learning, Short-term memory, Lithium-ion batteries |
| Abstract: | Lithium-ion batteries (LIBs) are widely employed in electric vehicles due to their environmental friendliness and extended lifespan. However, accurately forecasting the remaining useful life of LIBs presents challenges owing to intricate internal electrochemical reactions and external environmental uncertainties. Therefore, this study proposes a new LSTM-Informer deep learning model based on cooperative interaction gates for lithium-ion battery capacity point-interval prediction. Specifically, building upon the three gating mechanisms of LSTM, a novel cooperative interaction gate is proposed to thoroughly explore the direct correlation between the degraded sequences; and a long and short-term memory weight control strategy based on the capacity regeneration ratio is introduced to dynamically adjust the influence of the four gating mechanisms based on the fluctuation degree of the data, so as to accurately capture the capacity regeneration phenomenon of the degraded sequences, and to increase the prediction point prediction precision. The efficient ProbSparse is introduced to improve the prediction performance at the later stage of sequence data. Based on the interval prediction Kernel Density Estimation model, a dynamic optimal bandwidth optimization strategy is constructed to adaptively adjust the interval width according to the data fluctuation characteristics, which improves the uncertainty expression of the model. The results demonstrate that the proposed model achieves impressive prediction performance for different types of batteries, with the point prediction evaluation index MAPE controlled below 1.5 % and the interval prediction evaluation index CWC controlled below 0.7. [Display omitted] • Introducing a novel collaborative interaction gate-based deep learning model. • Proposing an innovative capacity regeneration ratio-based long short-term memory weight control strategy. • Presenting the Informer with effective ProbSparse as a unique advancement to reduce computational complexity. • Devising an innovative dynamic optimal bandwidth adjustment strategy-based LIB capacity interval forecasting method. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Energy is the property of Elsevier B.V. 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: 180928434 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhi-Feng%22">Liu, Zhi-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuzhifeng@tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Ya-He%22">Huang, Ya-He</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huangyahe@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shu-Rui%22">Zhang, Shu-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zsr030923@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Xing-Fu%22">Luo, Xing-Fu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fu406699@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiao-Rui%22">Chen, Xiao-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 21021127@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Jun-Jie%22">Lin, Jun-Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Yu%22">Tang, Yu</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Liang%22">Guo, Liang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> guoliang@sgepri.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ji-Xiang%22">Li, Ji-Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lijixiangtust@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Jan2025:Part D, Vol. 377, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remaining+useful+life%22">Remaining useful life</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Short-term+memory%22">Short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Lithium-ion batteries (LIBs) are widely employed in electric vehicles due to their environmental friendliness and extended lifespan. However, accurately forecasting the remaining useful life of LIBs presents challenges owing to intricate internal electrochemical reactions and external environmental uncertainties. Therefore, this study proposes a new LSTM-Informer deep learning model based on cooperative interaction gates for lithium-ion battery capacity point-interval prediction. Specifically, building upon the three gating mechanisms of LSTM, a novel cooperative interaction gate is proposed to thoroughly explore the direct correlation between the degraded sequences; and a long and short-term memory weight control strategy based on the capacity regeneration ratio is introduced to dynamically adjust the influence of the four gating mechanisms based on the fluctuation degree of the data, so as to accurately capture the capacity regeneration phenomenon of the degraded sequences, and to increase the prediction point prediction precision. The efficient ProbSparse is introduced to improve the prediction performance at the later stage of sequence data. Based on the interval prediction Kernel Density Estimation model, a dynamic optimal bandwidth optimization strategy is constructed to adaptively adjust the interval width according to the data fluctuation characteristics, which improves the uncertainty expression of the model. The results demonstrate that the proposed model achieves impressive prediction performance for different types of batteries, with the point prediction evaluation index MAPE controlled below 1.5 % and the interval prediction evaluation index CWC controlled below 0.7. [Display omitted] • Introducing a novel collaborative interaction gate-based deep learning model. • Proposing an innovative capacity regeneration ratio-based long short-term memory weight control strategy. • Presenting the Informer with effective ProbSparse as a unique advancement to reduce computational complexity. • Devising an innovative dynamic optimal bandwidth adjustment strategy-based LIB capacity interval forecasting method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. 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.apenergy.2024.124741 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Remaining useful life Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Short-term memory Type: general – SubjectFull: Lithium-ion batteries Type: general Titles: – TitleFull: A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Zhi-Feng – PersonEntity: Name: NameFull: Huang, Ya-He – PersonEntity: Name: NameFull: Zhang, Shu-Rui – PersonEntity: Name: NameFull: Luo, Xing-Fu – PersonEntity: Name: NameFull: Chen, Xiao-Rui – PersonEntity: Name: NameFull: Lin, Jun-Jie – PersonEntity: Name: NameFull: Tang, Yu – PersonEntity: Name: NameFull: Guo, Liang – PersonEntity: Name: NameFull: Li, Ji-Xiang IsPartOfRelationships: – BibEntity: Dates: – D: 10 M: 01 Text: Jan2025:Part D Type: published Y: 2025 Identifiers: – Type: issn-print Value: 03062619 Numbering: – Type: volume Value: 377 Titles: – TitleFull: Applied Energy Type: main |
| ResultId | 1 |
