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
2021-10
(Vol.70 No.10)
10.5370/KIEE.2021.70.10.1467
Journal XML
XML
PDF
INFO
REF
References
1
X. Tan, 2020, Real-Time State-of-Health Estimation of Lithium- Ion Batteries Based on the Equivalent Internal Resistance, Ieee Access, Vol. 8, pp. 56811-56822
2
C. Lyu, 2017, A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework, Energy, Vol. 120, pp. 975-984
3
X. Hu, Xiaosong Hu, 2019, State estimation for advanced battery management: Key challenges and future trends, Renewable and Sustainable Energy Reviews, Vol. 114, pp. 10933
4
R. Relan, 2016, Data-driven nonlinear identification of Li-ion battery based on a frequency domain nonparametric analysis, IEEE Transactions on Control Systems Technology, Vol. 25, No. 5, pp. 1825-1832
5
Patil, A. M., 2015, A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation, Applied energy, Vol. 159, pp. 285-297
6
S. Zhang, 2019, Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks, Journal of Energy Storage, Vol. 26, pp. 100951
7
S. Shen, 2019, A deep learning method for online capacity estimation of lithium-ion batteries, Journal of Energy Storage, Vol. 25, pp. 100817
8
S. Shen, 2020, Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries, Applied Energy, Vol. 260, pp. 114296
9
K. Kaur, 2021, Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles, International Journal of Energy Research, Vol. 45, No. 2, pp. 3113-3128
10
N. S. Pearre, , 2011, Electric vehicles: How much range is required for a day’s driving?, Transportation Research Part C: Emerging Technologies, Vol. 19, No. 6, pp. 1171-1184
11
D. Liu, 2013, Satellite lithium-ion battery remaining cycle life prediction with novel indirect health indicator extraction, Energies, Vol. 6, No. 8, pp. 3654-3668
12
S. M. Rezvanizaniani, 2014, Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility, Journal of power sources, Vol. 256, pp. 110-124
13
P. Cicconi, 2013, Cooling Simulation of an EV Battery Pack to Support a Retrofit Project from Lead-Acid to Li-Ion Cells, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE
14
Z. Ma, 2020, Suitable feature selection for prediction of lithium- ion batteries remaining useful life, 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS). IEEE
15
L. C. Casals, 2015, PHEV battery aging study using voltage recovery and internal resistance from onboard data, IEEE Transactions on Vehicular Technology, Vol. 65, No. 6, pp. 4209-4216
16
J. Wang, 2016, A multi-scale convolution neural network for featureless fault diagnosis, 2016 International Symposium on Flexible Automation (ISFA). IEEE
17
S. Albawi, 2017, Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET). Ieee, pp. 1-6