Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Physics-guided deep neural network for state of health and remaining useful life predictions of lithium-ion batteries with a framework of accelerated degradation experiments
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 오기용 | - |
| dc.date.accessioned | 2026-06-25T14:07:47Z | - |
| dc.date.available | 2026-06-25T14:07:47Z | - |
| dc.date.issued | 2024-12-05 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/216843 | - |
| dc.title | Physics-guided deep neural network for state of health and remaining useful life predictions of lithium-ion batteries with a framework of accelerated degradation experiments | - |
| dc.type | Conference | - |
| dc.citation.conferenceName | 7th International Conference on Materials and Reliability | - |
| dc.citation.conferencePlace | Busan, Korea | - |
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 서울 공과대학 > 서울 기계공학부 > 2. Conference Papers
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Oh, Ki-Yong
- COLLEGE OF ENGINEERING (SCHOOL OF MECHANICAL ENGINEERING)