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PINNs3

[IEEE-2022] An LSTM-PINN Hybrid Method to Estimate Lithium-Ion Battery Pack Temperature 논문 전문 : https://ieeexplore.ieee.org/document/9895422[출처]  G. Cho, D. Zhu, J. J. Campbell and M. Wang, "An LSTM-PINN Hybrid Method to Estimate Lithium-Ion Battery Pack Temperature," in IEEE Access, vol. 10, pp. 100594-100604, 2022, doi: 10.1109/ACCESS.2022.3208103.  ※ The picture and content of this article are from the original paper.    All picture and figures used in this article are sourced f.. 2024. 11. 28.
[MDPI-2023] Hybrid Modeling of Lithium-Ion Battery : Physics-Informed Neural Network for Battery State Estimation 논문 전문 : https://www.mdpi.com/2313-0105/9/6/301 [출처] Singh S, Ebongue YE, Rezaei S, Birke KP. Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation. Batteries. 2023; 9(6):301. https://doi.org/10.3390/batteries9060301 ※ The picture and content of this article are from the original paper. [논문 요약] Hybrid Modeling of Lithium-Ion Battery : Physics-Informe.. 2023. 9. 15.
[Power Sources-2021] Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis 논문 전문 : https://www.sciencedirect.com/science/article/abs/pii/S0378775321010259 [출처] Renato G. Nascimento, Matteo Corbetta, Chetan S. Kulkarni, Felipe A.C. Viana,Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis,Journal of Power SourcesVolume 513,2021,230526,ISSN 0378-7753,https://doi.org/10.1016/j.jpowsour.2021.230526. ※ The picture and content of this artic.. 2023. 9. 1.