2026-06-24T17:19:02Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/19472024-10-02T09:36:47Zradar4kit

10.35097/1947 Luh, Matthias Matthias Luh 0000-0003-4731-2207 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT), Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT) Blank, Thomas Thomas Blank 0000-0002-7543-5653 Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT) Karlsruhe Institute of Technology 2024 2024 Computer Science lithium-Ion batteries battery degradation battery aging battery ageing battery data cycling data electrochemical impedance spectroscopy (EIS) High Power Pulse Charging (HPPC) battery lifetime NMC NCM silicon anode NMC/C-SiO battery degradation dataset battery impedance capacity fade power fade lithium plating lithium stripping Open Access Creative Commons Attribution 4.0 International Luh, Matthias 0000-0003-4731-2207 Blank, Thomas 0000-0002-7543-5653 Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies can help to better understand and model degradation and to optimize the operation strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells. The dataset includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operation strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering. The data is described in detail in the open-access publication "Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell" published in Nature Scientific Data under the DOI: 10.1038/s41597-024-03831-x, also see “Related identifier”. An updated dataset is published under the DOI 10.35097/1969 (result data, e.g., capacity fade and impedance increase) and 10.35097/kww7jv8ajuvchcah (log data), also see “Related identifier”. Python example code to read, process, and visualize the data is provided in the GitHub repository: https://github.com/energystatusdata/bat-age-data-scripts/ Note: The "cell_eisv2.zip" file in this dataset is incomplete and only contains data for cells P001_1 to P044_2. The corrected file "cell_eisv2_fixed.zip" containing data for all 228 cells P001_1 to P076_3 can be found in the dataset “Addendum to "Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset]"” with the DOI 10.35097/krk531nmj4bsshha (see “Related identifier”). https://publikationen.bibliothek.kit.edu/1000168959 application/x-tar