KIEE
The Transactions of
the Korean Institute of Electrical Engineers
KIEE
Contact
Open Access
Monthly
ISSN : 1975-8359 (Print)
ISSN : 2287-4364 (Online)
http://www.tkiee.org/kiee
Mobile QR Code
The Transactions of the Korean Institute of Electrical Engineers
ISO Journal Title
Trans. Korean. Inst. Elect. Eng.
Main Menu
Main Menu
최근호
Current Issue
저널소개
About Journal
논문집
Journal Archive
편집위원회
Editorial Board
윤리강령
Ethics Code
논문투고안내
Instructions to Authors
연락처
Contact Info
논문투고·심사
Submission & Review
Journal Search
Home
Archive
2026-04
(Vol.75 No.4)
10.5370/KIEE.2026.75.4.859
Journal XML
XML
PDF
INFO
REF
References
1
Y. Zhao, 2021, A review on battery market trends, second-life reuse, and recycling, Sustainable Chemistry, Vol. 2, No. 1, pp. 167-205
2
W. Mrozik, 2021, Environmental impacts, pollution sources and pathways of spent lithium-ion batteries, Energy & Environmental Science, Vol. 14, No. 12, pp. 6099-6121
3
A. Kampker, 2021, Battery pack remanufacturing process up to cell level with sorting and repurposing of battery cells, Journal of Remanufacturing, Vol. 11, No. 1, pp. 1-23
4
J. B. Jeong, 2023, A Study on Internal Resistance Evaluation for the Reuse of Medium- and Large-Sized Batteries, Journal of the Korean Institute of Electrical Engineers, Vol. 72, No. 6, pp. 717-723
5
M. M. Kabir, D. E. Demirocak, 2017, Degradation mechanisms in Li-ion batteries: A state-of-the-art review, International Journal of Energy Research, Vol. 41, No. 14, pp. 1963-1986
6
T. Wang, 2025, Review of aging mechanism and diagnostic methods for lithium-ion batteries, Energies, Vol. 18, No. 14
7
M. S. Park, J. S. Kim, B. W. Kim, 2024, A Study on SOH Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis and Deep Learning, Journal of the Korean Institute of Electrical Engineers, Vol. 73, No. 2, pp. 349-357
8
M. Dubarry, D. Ansean, 2022, Best practices for incremental capacity analysis, Frontiers in Energy Research, Vol. 10
9
M. Lewerenz, 2017, Differential voltage analysis as a tool for analyzing inhomogeneous aging: A case study for LiFePO4|Graphite cylindrical cells, Journal of Power Sources, Vol. 368, pp. 57-67
10
A. Maradesa, 2024, Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method, Joule, Vol. 8, No. 7, pp. 1958-1981
11
W. Hu, 2023, Application of electrochemical impedance spectroscopy to degradation and aging research of lithium-ion batteries, The Journal of Physical Chemistry C, Vol. 127, No. 9, pp. 4465-4495
12
A. Krupp, 2021, Incremental capacity analysis as a state of health estimation method for lithium-ion battery modules with series-connected cells, Batteries, Vol. 7, No. 1
13
J. He, 2020, Comparative study of curve determination methods for incremental capacity analysis and state of health estimation of lithium-ion battery, Journal of Energy Storage, Vol. 29
14
C. You, 2020, Application of the Kramers–Kronig relations to multi-sine electrochemical impedance measurements, Journal of The Electrochemical Society, Vol. 167, No. 2
15
L. Wildfeuer, P. Gieler, A. Karger, 2021, Combining the distribution of relaxation times from EIS and time-domain data for parameterizing equivalent circuit models of lithium-ion batteries, Batteries, Vol. 7, No. 3
16
Z. Habib, M. Sarfraz, M. Sakai, 2005, Rational cubic spline interpolation with shape control, Computers & Graphics, Vol. 29, No. 4, pp. 594-605
17
J. Tsiligaridis, 2023, Tree-based ensemble models and algorithms for classification, pp. 103-106
18
S. Cho, J. Hur, 2025, A Study on an XGBoost-Based Wind Power Generation Forecasting Model Using Spatial Interpolation of Meteorological Data, Journal of the Korean Institute of Electrical Engineers, Vol. 74, No. 5, pp. 870-877
19
X. Y. Liew, N. Hameed, J. Clos, 2021, An investigation of XGBoost-based algorithm for breast cancer classification, Machine Learning with Applications, Vol. 6
20
A. M. Salih, 2025, A perspective on explainable artificial intelligence methods: SHAP and LIME, Advanced Intelligent Systems, Vol. 7, No. 1