DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Joonki | ko |
dc.contributor.author | Lee, Dongheon | ko |
dc.contributor.author | Jeong, Eui-Rim | ko |
dc.contributor.author | Yi, Yung | ko |
dc.date.accessioned | 2020-12-14T07:30:05Z | - |
dc.date.available | 2020-12-14T07:30:05Z | - |
dc.date.created | 2020-12-04 | - |
dc.date.created | 2020-12-04 | - |
dc.date.created | 2020-12-04 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.citation | APPLIED ENERGY, v.278, pp.115646 | - |
dc.identifier.issn | 0306-2619 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278393 | - |
dc.description.abstract | This paper presents the first full end-to-end deep learning framework for the swift prediction of lithium-ion battery remaining useful life. While lithium-ion batteries offer advantages of high efficiency and low cost, their instability and varying lifetimes remain challenges. To prevent the sudden failure of lithium-ion batteries, researchers have worked to develop ways of predicting the remaining useful life of lithium-ion batteries, especially using data-driven approaches. In this study, we sought a higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature. We took an in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning | - |
dc.type | Article | - |
dc.identifier.wosid | 000596117700004 | - |
dc.identifier.scopusid | 2-s2.0-85089429242 | - |
dc.type.rims | ART | - |
dc.citation.volume | 278 | - |
dc.citation.beginningpage | 115646 | - |
dc.citation.publicationname | APPLIED ENERGY | - |
dc.identifier.doi | 10.1016/j.apenergy.2020.115646 | - |
dc.contributor.localauthor | Yi, Yung | - |
dc.contributor.nonIdAuthor | Hong, Joonki | - |
dc.contributor.nonIdAuthor | Lee, Dongheon | - |
dc.contributor.nonIdAuthor | Jeong, Eui-Rim | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Lithium-ion battery | - |
dc.subject.keywordAuthor | Remaining useful life | - |
dc.subject.keywordAuthor | End-to-end deep learning | - |
dc.subject.keywordAuthor | Dilated convolutional neural networks | - |
dc.subject.keywordAuthor | Prediction uncertainty | - |
dc.subject.keywordPlus | CAPACITY FADE ANALYSIS | - |
dc.subject.keywordPlus | POWER FADE | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | CELLS | - |
dc.subject.keywordPlus | MODEL | - |
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