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Sandbox to develop, test and compare neural network-enabled estimation techniques for state of charge of a sample lithium-ion battery using deep learning methodologies, utilizing transient signals to predict state across points in time.

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Battery State Estimation using Neural Nets (SOC)

This repository is a part of a series of repositories aimed at deepening personal understanding of lithium-ion battery management systems along with practical implementations and contexts. Through this repo, SOC estimation techniques using NNs are explored for me to expand on experience learnt in career + courses + self-learning while identifying areas for self-improvement in my own knowledge and skills. It is designed more so as a sandbox for me to develop, test and implement state estimation techniques for various sample li-ion batteries.

Getting Started

Prerequisites

To run this project, you will need Python installed on your machine along with the required libraries. Current version utilizations being utilzied locally are as follows:

  • Python (3.11.7)
  • Virtual environment created using Python 3.11 (Not required but suggested)

To create and activate virtual environment:

python -m venv .venv_soc_nn
source .venv_soc_nn/bin/activate  # Linux/Mac
# or
.venv_soc_nn\Scripts\activate  # Windows
python -m ipykernel install --name ".venv_soc_nn" --display-name "NN (SOC est.)" --user

Otherwise, you can install the dependencies using the following command:

pip install -r requirements.txt

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Sandbox to develop, test and compare neural network-enabled estimation techniques for state of charge of a sample lithium-ion battery using deep learning methodologies, utilizing transient signals to predict state across points in time.

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