Published November 30, 2023 | Version Rephrase the dataset with separate partial

Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries

  • 1. ROR icon Technical University of Munich
  • 2. ROR icon Karlsruhe Institute of Technology
  • 1. ROR icon Karlsruhe Institute of Technology
  • 2. ROR icon Technical University of Munich
  • 3. Munich Data Science Institute (MDSI)
  • 4. Munich Institute for Robotic and Machine Intelligence (MIRMI)

Description

Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript.

Codes and mechanical parts of the project:

AutoBASS 2.0: GitHub - Helge-Stein-Group/AutoBASS at AutoBASS_2.0

Notes (English)

For the data, please go to the files folder on this page. There you will find 3 .zip files which you need to download and unzip:

  1. Base.zip
  2. FEC.zip
  3. VC.zip

After unziping them, you will get three folders each with 2 subfolders namely "1mAh" and "3mAh" which corresponds to the two cell batches using area capacity of 1mAh/cm2 and 3mAh/cm2 respectively and each subfolder contains folders named with the information of cell number, mass ratio and additive name of individual cell, in which the cycling data is saved as .CSV  file that you can read in python via:

import pandas as pd

data = pd.read_csv("...the/file/path/of/your/.csv")

Then the data variable will contain all data for the specific cell (failed or not) as a dictionary with essentially the raw data what arbin spits out, bassically, we listed the following keys to the data that are most often used:

Keys: Test_Time(s), Voltage(V), Current(A), Cycle_Index, Step_Index

For more detailed information on how to expresse the data, you can refer to our GitHub repo: https://github.com/Helge-Stein-Group/AutoBASS/tree/AutoBASS_2.0

Files

Base.zip

Files (2.8 GB)

Name Size
md5:d750e2e8f79871494b7a0448b7714d53
386.4 MB Preview Download
md5:927fdd25341524f9457adb78ea0fb692
1.1 GB Preview Download
md5:1473a7b6cf3886b58b2b083598a45531
1.3 GB Preview Download

Additional details

Related works

Continues
Journal article: 10.1039/d2dd00046f (DOI)
Is metadata for
Working paper: 10.26434/chemrxiv-2024-6nm0d-v2 (DOI)

Dates

Available
2023-11-30

Software

Repository URL
https://github.com/Helge-Stein-Group/AutoBASS
Programming language
Python , C++
Development Status
Active