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Battery AI15

[Nature-2024] Dynamic cycling enhances battery lifetime [논문 전문] : https://www.nature.com/articles/s41560-024-01675-8[출처] Geslin, A., Xu, L., Ganapathi, D. et al. Dynamic cycling enhances battery lifetime. Nat Energy 10, 172–180 (2025). https://doi.org/10.1038/s41560-024-01675-8 ※ The picture and content of this article are from the original paper.All picture and figures used in this article are sourced from publicily available on the internet. [논문 요.. 2025. 7. 17.
[Electronics-2023] A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for synthetic Battery Operation Data Generation [논문 전문] : https://www.mdpi.com/2079-9292/12/3/657[출처] Wong, K.L.; Chou, K.S.; Tse, R.; Tang, S.-K.; Pau, G. A Novel Fusion Approach Consisting of GAN and State-of Charge Estimator for Synthetic Battery Operation Data Generation. Electronics 2023, 12, 657. https://doi.org/10.3390/electronics12030657 ※ The picture and content of this article are from the original paper.All picture and figures use.. 2025. 7. 14.
[ICDM-2021] Towards Generating Real-World Time Series Data [논문 전문] : https://onlinelibrary.wiley.com/doi/abs/10.1002/er.7013[출처] https://arxiv.org/abs/2111.08386 ※ The picture and content of this article are from the original paper.All picture and figures used in this article are sourced from publicily available on the internet. [논문 요약]Towards Generating Real-World Time Series Data Microsoft와 UIUC에서 나온 RTS-GAN이라는 논문입니다.데이터의 퀄리티에 집중하는것 보다는, 실제 데이터에서 있는 .. 2025. 6. 30.
[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.
[Batteries-2022] An Electrical-Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures 논문 전문 : https://www.mdpi.com/2313-0105/8/10/140 [출처] Mao, S.; Han, M.; Han, X.; Lu, L.; Feng, X.; Su, A.; Wang, D.; Chen, Z.; Lu, Y.; Ouyang, M. An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures. Batteries 2022, 8, 140. https://doi.org/10.3390/batteries8100140 ※ Th.. 2024. 4. 13.

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