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Build Status Coverage Status PEP8 Documentation Status Scrutinizer Code Quality PyPI version

BattDeg

Introduction

Lithium-ion batteries degrade during any application, be it a cell-phone or a laptop, or an electric vehicle. While the exact cause of degradation is not fully understood, it is known to be affected by the operating conditions like charge/discharge current, temperature etc. With battdeg we are trying to predict the degradation in a battery given a few cycles of operation. We use a seq2seq LSTM model in Keras to do the prediction and the model has been trained on the CALCE data. The users can also train the models using their own dataset for their batteries and operating conditions.

Installation

To install, use pip install battdeg, and then you can import the module battdeg in your python script or jupyter notebook.

Usage

The functions are written according to the datafiles available for PL samples, CX2 and CS2 cells on the Center for Advanced Life Cycle Engineering(CALCE) Battery Research Group website

This Python package contains following functions:

  1. file_reader(): This function can be used to format the input dataframe such that it can be used by the 'model_prediction' function.
  2. model_prediction(): This function takes the formatted dataframe and predicts the discharge capacity values.

The jupyter notebooks 'WorkingWithCX2', 'WorkingWithCX2' and 'WorkingWithPL' in the examples directory show the usage of the above mentioned functions.

For a new dataset, below are the usage instructions:

  • Input data format
    In the input dataframe, the essential columns must be named as below:
Data Column Name
Current Current(A)
Voltage Voltage(V)
Discharge capacity(Ah) Discharge_Ah
Charge capacity(Ah) Charge_Ah

  • If the data is cumulative, the above mentioned two functions can be used.

  • If the data is not cumulative, the dataframe can be formatted as required for use by 'model_prediction' by using the following function:

3.data_formatting() : This function drops all the unnecessary columns in the input dataframe and prepares the dataframe for use by the function file_reader()

For development

  1. Install python version 3.6.
  2. Install the dependencies from requirements.txt using pip3 install -r requirements.txt
  3. Run npm install to install grunt.
  4. In the root folder in terminal, run grunt. This will automatically run the unit tests in battdeg folder when you save any Python file in the battdeg folder. This includes your unit tests and modules.
  5. Remember to TDD.

Future Scope

  1. Convert the files from .mat format to .csv in Python.
  2. Include ability to train for multiple chemistries, voltage windows etc.
  3. Deploy the solution as a web-service and create a web-UI.