Battery lifetime prediction using surface temperature features from early cycle data
Energy Environ. Sci., 2025, Advance ArticleDOI: 10.1039/D4EE05179C, PaperLawnardo Sugiarto, Zijie Huang, Yi-Chun LuStatistical temperature features from the first 10 cycles were used to develop machine learning models, showing competitive performance across various battery cathodes and operating conditions for early-cycle battery lifetime prognostics.To cite this article before page numbers are assigned, use the DOI form of citation above.The content of this RSS Feed (c) The Royal Society of Chemistry
Energy Environ. Sci., 2025, Advance Article
DOI: 10.1039/D4EE05179C, Paper
DOI: 10.1039/D4EE05179C, Paper
Lawnardo Sugiarto, Zijie Huang, Yi-Chun Lu
Statistical temperature features from the first 10 cycles were used to develop machine learning models, showing competitive performance across various battery cathodes and operating conditions for early-cycle battery lifetime prognostics.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry
Statistical temperature features from the first 10 cycles were used to develop machine learning models, showing competitive performance across various battery cathodes and operating conditions for early-cycle battery lifetime prognostics.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry
