Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell.

Sci Data

Karlsruhe Institute of Technology (KIT), Institute for Data Processing and Electronics (IPE), Eggenstein-Leopoldshafen, 76344, Germany.

Published: September 2024

AI Article Synopsis

  • Battery degradation significantly impacts the cost and performance of battery-powered devices, making aging studies essential for understanding and optimizing their use.
  • The dataset introduced is one of the largest publicly available, encompassing over 3 billion data points from 228 NMC/C+SiO lithium-ion cells that were monitored for over a year under various conditions.
  • It provides both result data (like usable capacity and impedance) and raw data (detailed measurement logs), which can be utilized for modeling battery degradation, optimizing operating strategies, and developing algorithms for battery state estimation using advanced techniques like machine learning.

Article Abstract

Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating 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 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 operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405776PMC
http://dx.doi.org/10.1038/s41597-024-03831-xDOI Listing

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