Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk.
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| Title: | Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk. |
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| Authors: | Shon, Jong Won1 (AUTHOR) jongwon516@gachon.ac.kr, Choi, Donmook1 (AUTHOR), Lee, Hyunjae2 (AUTHOR), Son, Sung-Yong2 (AUTHOR) xtra@gachon.ac.kr |
| Source: | Energies (19961073). Jun2024, Vol. 17 Issue 11, p2566. 15p. |
| Subjects: | Thermal batteries, Predicate calculus, Probability theory, Lithium-ion batteries |
| Abstract: | This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177858569 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shon%2C+Jong+Won%22">Shon, Jong Won</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jongwon516@gachon.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Choi%2C+Donmook%22">Choi, Donmook</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Hyunjae%22">Lee, Hyunjae</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Son%2C+Sung-Yong%22">Son, Sung-Yong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xtra@gachon.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2024, Vol. 17 Issue 11, p2566. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Thermal+batteries%22">Thermal batteries</searchLink><br /><searchLink fieldCode="DE" term="%22Predicate+calculus%22">Predicate calculus</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=177858569 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en17112566 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 2566 Subjects: – SubjectFull: Thermal batteries Type: general – SubjectFull: Predicate calculus Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Lithium-ion batteries Type: general Titles: – TitleFull: Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shon, Jong Won – PersonEntity: Name: NameFull: Choi, Donmook – PersonEntity: Name: NameFull: Lee, Hyunjae – PersonEntity: Name: NameFull: Son, Sung-Yong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 17 – Type: issue Value: 11 Titles: – TitleFull: Energies (19961073) Type: main |
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