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
2024-02
(Vol.73 No.02)
10.5370/KIEE.2024.73.2.349
Journal XML
XML
PDF
INFO
REF
References
1
Cano, Z.P.; Banham, D.; Ye, S.; Hintennach, A.; Lu, J.; Fowler, M.; Chen, Z. Batteries and fuel cells for emerging electric vehiclemarkets. Nat. Energy 2018, 3, 279–289.
2
Nishi, Y. Lithiumion secondary batteries; Past 10 years and the future. J. Power Sources 2001, 100, 101–106.
3
Abada, S.; Marlair, G.; Lecocq, A.; Petit, M.; Sauvant- Moynot, V.; Huet, F. Safety focused modeling of lithium-ion batteries: A review. J. Power Sources 2016, 306, 178–192.
4
Wang, Y.; Zhang, C.; Chen, Z. An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles. J. Power Sources 2016, 305, 80–88.
5
Feng, X.; Lu, L.; Ouyang, M.; Li, J.; He, X. A 3D thermal runaway propagation model for a large format lithium ion batterymodule. Energy 2016, 115, 194–208.
6
Stroe, D.I.; Swierczynski, M.; Stan, A.I.; Teodorescu, R.; Andreasen, S.J. Accelerated lifetime testing methodology for lifetime estimation of lithium-ion batteries used in augmented wind power plants. IEEE Trans. Ind. Appl. 2014, 50, 4006–4017.
7
Khumprom, P.; Yodo, N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 2019, 12, 660.
8
Plett, G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 1. Background. J. Power Sources 2004, 134, 252–261.
9
Kim, Y.; Bang, H. Introduction to Kalman Filter and Its Applications. Introd. Implement. Kalman Filter 2019, 1, 1–16.
10
Sepasi, S.; Ghorbani, R.; Liaw, B.Y. Inline state of health estimation of lithium-ion batteries using state of charge calculation. J. Power Sources 2015, 299, 246–254.
11
Chen, Z.P.;Wang, Q.T. The Application of UKF Algorithm for 18650-type Lithium Battery SOH Estimation. Appl. Mech. Mater. 2014, 519–520, 1079–1084.
12
Oji, T.; Zhou, Y.; Ci, S.; Kang, F.; Chen, X.; Liu, X. Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis. IEEE Access 2021, 9, 126903–126916.
13
Nuhic, A.; Terzimehic, T.; Soczka-Guth, T.; Buchholz, M.; Dietmayer, K. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 2013, 239, 680–688.
14
Liu, D.; Zhou, J.; Liao, H.; Peng, Y.; Peng, X. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics. IEEE Trans. Syst. Man Cybern. Syst. 2015, 45, 915–928.
15
Li, Y.; Zhong, S.; Zhong, Q.; Shi, K. Lithium-ion battery state of health monitoring based on ensemble learning. IEEE
16
You, G.W.; Park, S.; Oh, D. Diagnosis of Electric Vehicle Batteries Using Recurrent Neural Networks. IEEE Trans. Ind. Electron. 2017, 64, 4885–4893.
17
Park, M.-S.; Lee, J.-k.; Kim, B.-W. SOH Estimation of Li-Ion Battery Using Discrete Wavelet Transform and Long Short-Term Memory Neural Network. Appl. Sci. 2022, 12, 3996. https://doi.org/10.3390/app12083996.
18
D. Anseán et al., “Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis,” in IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2992-3002, May-June 2019, doi: 10.1109/TIA.2019.2891213.
19
B. Saha, K. Goebel, Battery data set, Available online: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
20
B. Jia and M. Xin, “Data-Driven Enhanced Nonlinear Gaussian Filter,” in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 6, pp. 1144-1148, June 2020.
21
VETTER, Jens, et al. Ageing mechanisms in lithium-ion batteries. Journal of power sources, 2005, 147.1-2: 269-281.
22
DUBARRY, Matthieu; TRUCHOT, Cyril; LIAW, Bor Yann. Synthesize battery degradation modes via a diagnostic and prognostic model. Journal of power sources, 2012, 219: 204- 216.
23
VERMA, Pallavi; MAIRE, Pascal; NOVÁK, Petr. A review of the features and analyses of the solid electrolyte interphase in Li-ion batteries. Electrochimica Acta, 2010, 55.22: 6332- 6341.
24
PASTOR-FERNÁNDEZ, Carlos, et al. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems. Journal of Power Sources, 2017, 360: 301-318.