Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis
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DOI: 10.1016/j.apenergy.2023.120774
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- Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
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- Xie, Jiahang & Yang, Rufan & Hui, Shu-Yuen Ron & Nguyen, Hung D., 2024. "Dual Digital Twin: Cloud–edge collaboration with Lyapunov-based incremental learning in EV batteries," Applied Energy, Elsevier, vol. 355(C).
- Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
- Calum Strange & Rasheed Ibraheem & Gonçalo dos Reis, 2023. "Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling," Energies, MDPI, vol. 16(7), pages 1-14, April.
- Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
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Keywords
Lithium-ion; Battery; Degradation; Efficiency; Digital twin;All these keywords.
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