Acknowledgement
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 지역지능화혁신인재양성사업의 연구결과로 수행되었음 (IITP-2023-RS-2022-00156287). 본 연구는 과학기술정보통신부 및 정보통신기획평가원의 인공지능융합혁신인재양성사업의 연구결과로 수행되었음(IITP-2023-RS-2023-00256629)
References
- H. Dai, G. Zhao, M. Lin, J. Wu and G. Zheng, "A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain," IEEE Transactions on Industrial Electronics, vol. 66, no. 10, pp. 7706-7716, Nov. 2019.
- Z. Guo, X. Qiu, G. Hou, B. Y. Liaw and C. Zhang, "State of health estimation for lithium ion batteries based on charging curves," Journal of Power Sources, vol. 249, pp. 457-462, Mar. 2014. https://doi.org/10.1016/j.jpowsour.2013.10.114
- Y. Li, Z. Wei, B. Xiong, and D. Vilathgamuwa, "Adaptive Ensemble-Based Electrochemical-Thermal Degradation State Estimation of Lithium-Ion Batteries," IEEE Transactions on Industrial Electronics, vol. 69, no. 7, pp. 6984-6996, Jul. 2022. https://doi.org/10.1109/TIE.2021.3095815
- X. Hu, D. Cao and B. Egardt, "Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model," IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 167-178, Feb. 2017. https://doi.org/10.1109/TMECH.2017.2675920
- Z. Chen, M. Sun, X. Shu, R. Xiao and J. Shen, "Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine," Applied Sciences, vol. 8, no. 6, pp. 925(1-13), Jun. 2018. https://doi.org/10.3390/app8060925
- D. Yang, X. Zhang, R. pan, Y. Wang and Z. Chen, "A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve," Journal of Power Sources, vol. 384, pp. 387-395, Apr. 2018. https://doi.org/10.1016/j.jpowsour.2018.03.015
- J.H. Hong, D.H. Lee, E.R. Jeong, Y. Yi, "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, vol. 278, pp. 115646(1-12), Nov. 2020.
- A. Eddahech, O. Briat, N. Bertrand, Y. Deletage and J. Vinassa, "Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks," International Journal of Electrical Power & Energy Systems, vol. 42, no. 1, pp. 487-494, Nov. 2012. https://doi.org/10.1016/j.ijepes.2012.04.050
- W. Li, N. Sengupta, P. Dechent, D. Howey, A. Annaswamy and D. Sauer, "Online capacity estimation of lithium-ion batteries with deep long short-term memory networks," Journal of Power Sources, vol. 482, pp. 228863(1-11), Jan. 2021.
- L. Ren, L. Zhao, S. Zhao, H. Wang and L. Zhang, "Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach," IEEE Access, vol. 6, pp. 50587-50598, Jul. 2018. https://doi.org/10.1109/ACCESS.2018.2858856
- L. Ren, J. Dong, X. Wang, Z. Meng, L. Zhao and M. Deen, "A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life," IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 3478-3487, Jul. 2020.
- 박성윤, 이평연, 유기수, 김종훈, "증분용량 및 차동전압 분석법을 이용한 리튬이온 배터리 SOH 추정 방법 연구," 대한기계학회논문집 A권, 제 45권, 제 3호, 259-266쪽, 2021년 3월 https://doi.org/10.3795/KSME-A.2021.45.3.259
- M. Lin, J. Wu, J. Meng, W. Wang and J. Wu, "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, vol. 268, pp. 126706(1-12), Apr. 2023.
- H. Gabbar, A. Othman and M. Abdussami, "Review of Battery Management Systems (BMS) Development and Industrial Standards," Technologies, vol. 9, no. 2, pp. 28(1-23), Sep. 2021. https://doi.org/10.3390/technologies9020028
- G. Ji, Y. Ma and J.M. Lee, "Mitigating the initial capacity loss (ICL) problem in high-capacity lithium ion battery anode materials," Journal of Materials Chemistry, vol. 21, no. 27, pp. 9819-9824, 2011. https://doi.org/10.1039/c0jm03759a