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Journal of the Korean Institute of Illuminating and Electrical Installation Engineers

ISO Journal TitleJ Korean Inst. IIIum. Electr. Install. Eng.

References

1 
B. H. Seo, et al., “Condition monitoring of Lithium polymer batteries based on sigma-point kalman filter,” J. of Power Electronics, vol. 12, no. 5, pp. 778-786, 2012.URL
2 
J. Y. Lim, D. W. Kim, T. W. Noh, and B. K. Lee, “Remaining useful life prediction for Lithium-ion batteries using EMD-CNN-LSTM hybrid method,” Trans. Korean Inst. Power Electronics, vol. 27, no. 1, pp. 48-55, 2022.DOI
3 
S. Chen, et al., “A data-driven battery SOH estimation method with CNN-LSTM model and SSA optimizing,” in Proc. Chinese Control and Decision Conference, pp. 3381-3385, 2024.DOI
4 
G. Xu, et al., “DNPSO-LSTM based SOH estimation for Li-ion batteries,” in Proc. 3rd Int. Conf. New Energy and Power Engineering (ICNEPE), pp. 724-727, 2023.DOI
5 
B. Y. K., A. V. P., V. U., and S. Shetty, “Long-term estimation of SoH using cascaded LSTM-RNN for Lithium batteries subjected to aging and accelerated degradation,” Energy Storage, vol. 6, no. 5, e70066, 2024.DOI
6 
J. H. Lee, T. H. Gong, and J. H. Kim, “Deriving optimal health indicators base on fuzzy logic using Lithium-ion battery EIS measurement data and developing SOH estimation based on LSTM algorithm,” Trans. Korean Inst. Power Electronics, vol. 29, no. 4, pp. 308-315, 2024.DOI
7 
Y. H. Choi and J. J. Yun, “Differential Thermal voltammetry analysis and LSTM-based lithium-ion battery SOH estimation,” KSAE 2023 Annual Fall Conference, pp. 1284-1290, 2023.URL
8 
S. R. Hong,et al., “OCV estimation based on artificial neural network in Lithium-ion battery,” J. Power Electronics Conference, pp. 445-446, 2019.URL
9 
S. J. Lee, et al.i, “Implementation of the DCIR-SOC relationship for pulse power capability prediction of a Li-ion cell,” J. advanced engineering and technology, vol. 10, no. 2, pp. 225-230, 2017.DOI
10 
Y. S. Kim, “A study on SOH prediction of Lithium secondary battery based on numerical analysis,” Ph.D. Dissertation, Department of Mechatronics Engineering, Graduate School of Korea University of Technology Education, p. 182, 2019.URL
11 
S. Y. Park, J. H. Kim, S. B. Park, and Y. M. Kim, “A study on SOH estimation of Lithium-ion battery based on Bayesian Regression,” J. Power Electronics Conference, pp. 53-55, 2019.URL