물리기반 인공신경망을 활용한 리튬이온 전지 열화 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김승욱 | - |
dc.contributor.author | 오기용 | - |
dc.contributor.author | 이승철 | - |
dc.date.accessioned | 2021-12-22T00:40:41Z | - |
dc.date.available | 2021-12-22T00:40:41Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1598-2785 | - |
dc.identifier.issn | 2287-5476 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52652 | - |
dc.description.abstract | Currently, lithium-ion batteries are becoming the most promising power source for a variety of portable electronics as well as electric vehicles. Some of the advantages that promote their widespread usage include their long battery cycle life, high durability, low self-discharge rate, and fast charge rate. However, despite their superiority in comparison with other power sources, there exists a lack of understanding regarding their battery lifetime owing to their sophisticated electrochemical actions, which cannot be sufficiently modeled and predicted using traditional physics-based models. This limitation has motivated the development of numerous data-driven approaches. However, data-driven methods also have certain limitations, such as low interpretability and inability to extrapolate well. This necessitates an alternative method that can leverage the strengths of both models while complementing their drawbacks. In this study, the state-of-health of lithium-ion batteries is estimated using a physics-informed neural network with the integration of physics in the deep learning pipeline. The results of this study indicate that the proposed model outperforms the conventional data-driven methods in RMSE and physical inconsistency. | - |
dc.format.extent | 8 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국소음진동공학회 | - |
dc.title | 물리기반 인공신경망을 활용한 리튬이온 전지 열화 예측 | - |
dc.title.alternative | Physics-informed Neural Network for Estimation of Lithium-Ion Battery State-of-health | - |
dc.type | Article | - |
dc.identifier.doi | 10.5050/KSNVE.2021.31.2.177 | - |
dc.identifier.bibliographicCitation | 한국소음진동공학회논문집, v.31, no.2, pp 177 - 184 | - |
dc.identifier.kciid | ART002707291 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 184 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 177 | - |
dc.citation.title | 한국소음진동공학회논문집 | - |
dc.citation.volume | 31 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Physics-informed neural network | - |
dc.subject.keywordAuthor | physical inconsistency | - |
dc.subject.keywordAuthor | capacity degradation | - |
dc.subject.keywordAuthor | end-of-life | - |
dc.subject.keywordAuthor | model-based method | - |
dc.subject.keywordAuthor | remaining useful life | - |
dc.subject.keywordAuthor | 물리기반 인공신경망 | - |
dc.subject.keywordAuthor | 물리적 불일치성 | - |
dc.subject.keywordAuthor | 용량 열화 | - |
dc.subject.keywordAuthor | 수명 | - |
dc.subject.keywordAuthor | 모델기반 방식 | - |
dc.subject.keywordAuthor | 잔존유효수명 | - |
dc.description.journalRegisteredClass | kci | - |
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