Tutorial 20 - extract_ocv_dva_ica#

Step 0: Setup the project and prepare the data#

[7]:

from pathlib import Path

import pydpeet as eet

We will use “ERROR” as the logging style for better readability of the notebook

[8]:

eet.set_logging_style("ERROR")

[9]:

standardized_data = eet.read(
    input_path=str(Path.cwd().parent.parent / "res" / "raw_data_from_cyclers" / "Cal_Ageing_Checkup1.xlsx"),
    config=eet.ReadConfig.Neware_8_0_0_516,
)

Step 1: extract_ocv_dva_ica#

[10]:

dva_ica_data = eet.extract_ocv_dva_ica(
    df=standardized_data,
    soc_c_ref=4.75,
    visualize=True,
)

../../_images/examples_notebooks_Tutorial_20_extract_ocv_dva_ica_7_0.png

Warning: The first value of the derivative is NaN, because of the numeric differentiation.

[11]:

dva_ica_data[["dV_dQ", "dQ_dV"]].head(6)

[11]:

	dV_dQ	dQ_dV
0	NaN	NaN
1	0.646906	1.545819
2	0.479381	2.086023
3	0.358246	2.791375
4	0.283503	3.527298
5	0.224226	4.459786

Step 2: With Savitzky-Golay smoothing#

[12]:

dva_ica_data_sg = eet.extract_ocv_dva_ica(
    df=standardized_data,
    soc_c_ref=4.75,
    savgol=True,
    visualize=True,
)

../../_images/examples_notebooks_Tutorial_20_extract_ocv_dva_ica_11_0.png

Warning: The first value of the derivative is NaN, because of the numeric differentiation.

[13]:

dva_ica_data_sg[["dV_dQ", "dQ_dV"]].head(6)

[13]:

	dV_dQ	dQ_dV
0	NaN	NaN
1	0.646906	1.574161
2	0.479381	2.086400
3	0.358246	2.738764
4	0.283503	3.531253
5	0.224226	4.463867