Lithium-ion Battery SOH Prediction Model Based on LSTM Optimized by Singer Chaotic Map Zebra Optimization Algorithm.
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| Title: | Lithium-ion Battery SOH Prediction Model Based on LSTM Optimized by Singer Chaotic Map Zebra Optimization Algorithm. |
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| Authors: | Yao, Cheng1 250849634@qq.com, Wang, Fei-Fei1 3151744145@qq.com, Li, Ling1 liling2002@jlu.edu.cn, Zhang, Hai-Ying1 24652900@qq.com |
| Source: | Engineering Letters. Jul2026, Vol. 34 Issue 7, p2861-2876. 16p. |
| Subjects: | Long short-term memory, Metaheuristic algorithms, Prediction models, Lithium-ion batteries |
| Abstract: | To build an accurate model for battery state-of-health (SOH) prediction, a hybrid approach that combines a novel metaheuristic algorithm, the Zebra Optimization Algorithm (ZOA), with Long Short-Term Memory (LSTM) is proposed. The ZOA is employed to optimize the hyperparameters of the LSTM network, thereby enhancing its predictive performance. Furthermore, recognizing the limitations of the original ZOA in solving complex optimization problems, we introduce a chaotic map convergence factor, SC6, to improve the algorithm's convergence accuracy. Experimental results on the NASA datasets show that the proposed SC6-ZOA-LSTM model reduces the RMSE by 2.3% compared to the standard LSTM model. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088788 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Lithium-ion Battery SOH Prediction Model Based on LSTM Optimized by Singer Chaotic Map Zebra Optimization Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yao%2C+Cheng%22">Yao, Cheng</searchLink><relatesTo>1</relatesTo><i> 250849634@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Fei-Fei%22">Wang, Fei-Fei</searchLink><relatesTo>1</relatesTo><i> 3151744145@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ling%22">Li, Ling</searchLink><relatesTo>1</relatesTo><i> liling2002@jlu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hai-Ying%22">Zhang, Hai-Ying</searchLink><relatesTo>1</relatesTo><i> 24652900@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jul2026, Vol. 34 Issue 7, p2861-2876. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To build an accurate model for battery state-of-health (SOH) prediction, a hybrid approach that combines a novel metaheuristic algorithm, the Zebra Optimization Algorithm (ZOA), with Long Short-Term Memory (LSTM) is proposed. The ZOA is employed to optimize the hyperparameters of the LSTM network, thereby enhancing its predictive performance. Furthermore, recognizing the limitations of the original ZOA in solving complex optimization problems, we introduce a chaotic map convergence factor, SC6, to improve the algorithm's convergence accuracy. Experimental results on the NASA datasets show that the proposed SC6-ZOA-LSTM model reduces the RMSE by 2.3% compared to the standard LSTM model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2861 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Lithium-ion batteries Type: general Titles: – TitleFull: Lithium-ion Battery SOH Prediction Model Based on LSTM Optimized by Singer Chaotic Map Zebra Optimization Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yao, Cheng – PersonEntity: Name: NameFull: Wang, Fei-Fei – PersonEntity: Name: NameFull: Li, Ling – PersonEntity: Name: NameFull: Zhang, Hai-Ying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 7 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
