Table of Contents

0. Setup

1. Import and Preprocessing

2. Add Columns

3. Extract Features

4. Plot Results

5. Export

Setup

Load PyDPEET and set logging style to “Error” to prevent log spamming.

import pydpeet as eet

eet.set_logging_style("ERROR")

Import and Preprocessing

• Import example data

  • Test device: Neware

  • 4 checkups (capacity test, GITT)

  • Calendar ageing

• Merge the 4 files into one DataFrame for easier processing

• Add primitive segments, like “CC”,”CV” or “Rest” to the DataFrame and saving in an extra variable

dfs = eet.read(config="neware_8_0_0_516", input_path="../../../res/raw")
df_merged = eet.merge_into_series(dfs)
df_segments = eet.add_primitive_segments(df_merged)

Add Columns

• Loading the configuration of the used cell (LGM50LT with 4.8 Ah)

• Adding SOC to the DataFrame

• Adding internal resistance to the DataFrame

• Adding the charge throughput to the DataFrame

config = eet.lgm50lt_nmc_4800
df_merged = eet.add_soc(df_merged, df_segments, standard_method=eet.SocMethod.WITH_RESET_WHEN_FULL, config=config)
df_merged = eet.add_resistance_internal(df_merged, config=config)
df_merged = eet.process.analyze.capacity.add_charge_throughput(df_merged)

Extract Features

• Extract iOCV

ocv_data = eet.extract_ocv_iocv(df=df_merged, config=eet.lgm50lt_nmc_4800, visualize=False)

Plot Results

• Plotting the results using Matplotlib

from plot_helper import KWB_plot  # noqa

KWB_plot(df_merged, ocv_data)

Export

Export data frame to Parquet file.

eet.write(df_merged, output_path="./parquet", output_file_name="Test_Output")