State-of-Health Estimation of Lithium-Ion Batteries with Attention-Based Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cui, Shengmin | - |
dc.contributor.author | Shin, Jisoo | - |
dc.contributor.author | Woo, Hyehyun | - |
dc.contributor.author | Hong, Seokjoon | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2022-07-07T09:23:55Z | - |
dc.date.available | 2022-07-07T09:23:55Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144198 | - |
dc.description.abstract | Lithium-ion batteries are most commonly used in electric vehicles (EVs). The battery management system (BMS) assists in utilizing the energy stored in the battery more effectively through various functions. State of health (SOH) estimation is an essential function in a BMS. The accurate estimation of SOH can be used to calculate the remaining lifetime and ensure the reliability of batteries. In this paper, we propose a data-driven deep learning method that combines Gate Recurrent Unit (GRU) and attention mechanism for SOH estimation of lithium-ion batteries. Real-life datasets of batteries from NASA are used for evaluating our proposed model. The experimental results show that the proposed deep learning model has higher accuracy than conventional data-driven models. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | State-of-Health Estimation of Lithium-Ion Batteries with Attention-Based Deep Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.1007/978-3-030-63319-6_28 | - |
dc.identifier.scopusid | 2-s2.0-85098227674 | - |
dc.identifier.bibliographicCitation | Advances in Intelligent Systems and Computing, v.1295, pp.322 - 331 | - |
dc.relation.isPartOf | Advances in Intelligent Systems and Computing | - |
dc.citation.title | Advances in Intelligent Systems and Computing | - |
dc.citation.volume | 1295 | - |
dc.citation.startPage | 322 | - |
dc.citation.endPage | 331 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Computational methods | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Intelligent systems | - |
dc.subject.keywordPlus | Ions | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Lithium-ion batteries | - |
dc.subject.keywordPlus | NASA | - |
dc.subject.keywordPlus | Software engineering | - |
dc.subject.keywordPlus | Accurate estimation | - |
dc.subject.keywordPlus | Attention mechanisms | - |
dc.subject.keywordPlus | Data-driven model | - |
dc.subject.keywordPlus | Electric Vehicles (EVs) | - |
dc.subject.keywordPlus | Learning methods | - |
dc.subject.keywordPlus | Real life datasets | - |
dc.subject.keywordPlus | State of health | - |
dc.subject.keywordPlus | Various functions | - |
dc.subject.keywordPlus | Battery management systems | - |
dc.subject.keywordAuthor | Attention | - |
dc.subject.keywordAuthor | Gated recurrent unit | - |
dc.subject.keywordAuthor | Lithium-ion battery | - |
dc.subject.keywordAuthor | State of health | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-030-63319-6_28 | - |
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