DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Jay Hyung | - |
dc.contributor.advisor | 이재형 | - |
dc.contributor.author | Sohn, Suyeon | - |
dc.date.accessioned | 2023-06-23T19:31:44Z | - |
dc.date.available | 2023-06-23T19:31:44Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997307&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308888 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 생명화학공학과, 2022.2,[iii, 34 p. :] | - |
dc.description.abstract | Accurate monitoring of capacity degradation of a lithium-ion battery is important as it enables the user to manage the battery usage for optimal performance/lifetime and to take preemptive actions against any potential explosion or fire. Battery capacity fades gradually through repetitive charging and discharging until it reaches the so called ‘knee-point’, after which it goes through rapid and irreversible deterioration to reach its end-of-life. It is crucial to forecast the knee-point early and accurately for safety and economic use of the battery. Machine learning based methods have been used to predict the knee-point with early cycles cell data. Despite some notable progress made, the existing methods make the unrealistic assumption of constant cycle-to-cycle charge/discharge operation. In this study, a novel two-stage deep learning method is proposed for online knee-point prediction under variable battery usage. A CNN-based model extracts temporal features across past and current cycles to sort out those cells should be monitored closely for near-term failures, and then predict the number of cycles left to reach the knee-point for them. The proposed method extracts features from time-series data and thus reflects dynamic changes in battery properties, resulting in improved prediction performance under realistic scenarios. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.title | Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation | - |
dc.title.alternative | 리튬 이온 전지 용량 퇴화에서의 실시간 knee-point 예측을 위한 2단계 딥러닝 모델 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :생명화학공학과, | - |
dc.contributor.alternativeauthor | 손수연 | - |
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