Battery-dataset-preprocessing-code-library

A code library for reading and preprocessing public battery dataset

1.简介（Introduction）

本代码库包含一个用于读取和预处理各种公开电池数据集的Python代码库。该代码旨在方便地访问电池数据，并为电池健康管理领域的分析准备数据。

This repository contains a Python code library for reading and preprocessing various publicly available battery datasets. The code is designed to facilitate easy access to battery data and prepare it for analysis in the field of battery health management.

2. 动机（Motivation）

在电池健康管理领域，有许多大规模的公开电池数据集。然而，往往缺乏相应的用于读取和预处理这些数据集的代码。 因此，开发了这个代码库来填补这一空白，并为研究人员和从业者提供一个方便的工具。

In the field of battery health management, there are numerous large-scale publicly available battery datasets. However, there is often a lack of corresponding code for reading and preprocessing these datasets. Therefore, this code library was developed to address this gap and provide a convenient tool for researchers and practitioners working with battery data.

3. 数据集（Datasets）

3.1 XJTU Battery Datasets：https://zenodo.org/records/10963339

Important

Summary of articles using the XJTU Battery Dataset: https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary

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3.2 MIT Datasets: https://data.matr.io/1/projects/5c48dd2bc625d700019f3204

3.3 华中科技大学电池数据集: https://data.mendeley.com/datasets/nsc7hnsg4s/2

3.4 同济大学电池数据集: https://zenodo.org/records/6405084

3.5 Continuously updating...

4.引用（Citation）

如果您在您的论文或项目中使用了本代码库，请引用我们的论文或者该仓库：

If you use this code library in your paper or project, please cite our paper or this repository:

论文链接：https://www.nature.com/articles/s41467-024-48779-z

论文引用格式

@article{wang2024physics,
  title={Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis},
  author={Wang, Fujin and Zhai, Zhi and Zhao, Zhibin and Di, Yi and Chen, Xuefeng},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={4332},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

代码引用格式

@misc{wang2024Battery,
  title = {Battery-dataset-preprocessing-code-library},
  author = {Fujin Wang},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library}},
}