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DC Field | Value | Language |
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dc.citation.startPage | 110004 | - |
dc.citation.title | MECHANICAL SYSTEMS AND SIGNAL PROCESSING | - |
dc.citation.volume | 188 | - |
dc.contributor.author | Lee, Gyumin | - |
dc.contributor.author | Kwon, Daeil | - |
dc.contributor.author | Lee, Changyong | - |
dc.date.accessioned | 2023-12-21T12:44:12Z | - |
dc.date.available | 2023-12-21T12:44:12Z | - |
dc.date.created | 2023-01-26 | - |
dc.date.issued | 2023-04 | - |
dc.description.abstract | Previous machine learning models for state-of-health (SOH) estimation of Li-ion batteries have relied on prescribed statistical features. However, there is little theoretical understanding of the relationships between these features and SOH degradation patterns of the batteries. This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion bat-teries in the early phases of qualification tests. First, capacity degradation data are transformed into two-dimensional images using recurrence plots and Gramian angular fields, highlighting the time-series features of the data. Second, five types of convolutional neural network models are developed to estimate the SOH values of Li-ion batteries for a certain cycle. Here, class activation maps are generated to present how the models arrive at their conclusions. Finally, the perfor-mance and reliability of the developed models are assessed under various experimental condi-tions. The proposed approach has the following two advantages: it automatically extracts important temporal features from the capacity degradation data for SOH estimation, and obtains the contribution of each temporal feature with respect to the estimation process. The experi-mental results on 379Li-ion batteries confirm that the proposed approach can reduce the time required for qualification tests to 50 cycles, under a 6% mean absolute percentage error. | - |
dc.identifier.bibliographicCitation | MECHANICAL SYSTEMS AND SIGNAL PROCESSING, v.188, pp.110004 | - |
dc.identifier.doi | 10.1016/j.ymssp.2022.110004 | - |
dc.identifier.issn | 0888-3270 | - |
dc.identifier.scopusid | 2-s2.0-85144051375 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/62552 | - |
dc.identifier.wosid | 000907032500001 | - |
dc.language | 영어 | - |
dc.publisher | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD | - |
dc.title | A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Mechanical | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Li-ion battery | - |
dc.subject.keywordAuthor | State-of-health estimation | - |
dc.subject.keywordAuthor | Recurrence plot | - |
dc.subject.keywordAuthor | Gramian angular field | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordPlus | EQUIVALENT-CIRCUIT MODEL | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordPlus | PROGNOSTICS | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | CHARGE | - |
dc.subject.keywordPlus | SMOTE | - |
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