Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Abnormal Degradation Detection for Lithium-Ion Batteries With Denoising Diffusion Implicit Model

Authors
Jang, KyujinPark, JongwookBae, Sungwoo
Issue Date
Jan-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
anomaly detection; denoising diffusion implicit model; lithium-ion battery; time-series anomaly detection
Citation
2025 28th International Conference on Electrical Machines and Systems (ICEMS), pp 3321 - 3324
Pages
4
Indexed
SCOPUS
Journal Title
2025 28th International Conference on Electrical Machines and Systems (ICEMS)
Start Page
3321
End Page
3324
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211831
DOI
10.23919/ICEMS66262.2025.11317751
ISSN
2640-7841
2642-5513
Abstract
This paper proposes a method for the immediate detection of abnormal degradation in lithium-ion batteries by applying the denoising diffusion implicit model (DDIM). The proposed approach utilizes a diffusion-based generative model to emphasize and reconstruct battery operation data. In this framework, the DDIM compresses and reconstructs lithium-ion battery operational data, and the resulting reconstruction error is used as an indicator of anomalies. Experimental results demonstrate that the proposed method can detect abnormal degradation at least 10 cycles earlier than the benchmark models.
Files in This Item
Go to Link
Appears in
Collections
서울 공과대학 > 서울 전기공학전공 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Bae, Sung Woo photo

Bae, Sung Woo
COLLEGE OF ENGINEERING (MAJOR IN ELECTRICAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE