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

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.

电池衰减对于电池供电产品的成本效益与可用性能至关重要。老化研究有助于更深入地理解电池衰减过程并建立其模型，同时优化产品运行策略。然而，目前针对此类应用的全面且可免费获取的老化数据集仍较为稀缺。
据我们所知，下文呈现的数据集是目前已公开的规模最大的数据集之一。该数据集涵盖228颗商用NMC/C+SiO锂离子电池的超30亿个数据点，这些电池在宽泛的运行工况下完成了超过一年的老化测试。我们对静置老化（calendar aging）与循环老化（cyclic aging）均开展了研究，同时还为部分电池施加了不同的驾驶循环工况。本数据集包含结果数据与原始数据两类：结果数据包括定期检测中测得的可用剩余容量与阻抗等参数，原始数据则为分辨率达2秒的测量日志。
该数据集可应用于诸多研究与工程场景，例如构建电池衰减模型、深入解析锂沉积现象、优化运行策略，或是借助机器学习或卡尔曼滤波（Kalman filtering）测试电池阻抗或状态估计算法。

搜集汇总

背景与挑战

背景概述

该数据集是当前公开的最大锂离子电池老化数据集之一，包含228个NMC/C-SiO电池在多种工况下超过一年的老化数据（30亿数据点），涵盖容量衰减和阻抗增加等关键指标，适用于电池退化研究和算法开发。数据集同时提供处理数据的Python示例代码，具有很高的研究和应用价值。