LiBEIS

LiBEIS is a software tool for Electrochemical Impedance Spectroscopy (EIS) computation on rechargable litium batteries (LiB)

See also https://electrical-and-electronic-measurement.github.io/LiBEIS/

Data

Before run LiBEIS you need some EIS data.

To test the EIS computation Matlab script you can downalod some raw Voltage and current data acquisition file from Dataset on Voltage and Current Data Acquisition During Broadband Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries for Different Values of the State of Charge

Buchicchio, Emanuele; De Angelis, Alessio; Santoni, Francesco; Carbone, Paolo (2022), “Dataset on Voltage and Current Data Acquisition During Broadband Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries for Different Values of the State of Charge”, Mendeley Data, V1, doi: 10.17632/zdsgxwksn5.1

Dowalod at least on battery folder (including all subfolders) into /data folder

You can also downaload EIS data from: Dataset on broadband Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries for Different Values of the State of Charge.

Buchicchio, Emanuele; De Angelis, Alessio; Santoni, Francesco; Carbone, Paolo (2022), “Dataset on broadband Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries for Different Values of the State of Charge”, Mendeley Data, V3, doi: 10.17632/mbv3bx847g.3

Download imepdance.csv and frequencyes.csv into the /data folder.

Usage

There are two options to use LiBEIS:

1. from a docker container image (see the docker file in /environment folder)
2. locally from the /code folder:

Run Locally

1. Move to /code

2. run matlab -nodisplay -r "addpath(genpath('.')); to compute_impedance_values.

3. run python3 export_eis_data.py to generate the EIS dataset files (impedence.csv and frequency.csv). A copy of these files can also be download from https://data.mendeley.com/datasets/mbv3bx847g

4. run python3 lda_pca_scatter_plots.pyto Perform LDA and PCA and save the scattering plots in reults folder

5. run python3 classification.py to train and score different SOC classification models. retrieve the results in the classification_results_out@config/config.yaml file

6. run matlab -nodisplay -r "addpath(genpath('.')) to fit the equivalent circuit model of the battery

7. run python3 export_model_data.py to generate the circuit model parameters dataset file (parameters.csv)

Settings

• Edit the config/config.yaml to adjust the settings - e.g., connection to the data source and output folders.