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synthetic-battery-data

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Generation of synthetic battery operation data using a generative adversarial network (GAN) and a state-of-charge (SOC) estimator.

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# Synthetic Battery Data
Generation of synthetic battery operation data using a generative adversarial network (GAN) and a state-of-charge (SOC) estimator.

<p align="center">
  <img src="./generative_framework.png" width="550">
</p>

## Introduction

This repository provides the implementation of a fusion approach combining a GAN and an [**SOC estimator**](https://github.com/KeiLongW/battery-state-estimation) for synthetic battery operation data generation.
The experiment is performed on the public battery dataset: [**LG 18650HG2 Li-ion Battery Data**](https://data.mendeley.com/datasets/cp3473x7xv/3).

## Publication

The work shown here is an implementation from a research article that was published in the  [**Electronics**](https://www.mdpi.com/journal/electronics). If you use this work, please cite our paper:

*A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation* 
[DOI: [10.3390/electronics12030657](https://doi.org/10.3390/electronics12030657)]

```
@Article{electronics12030657,
AUTHOR = {Wong, Kei Long and Chou, Ka Seng and Tse, Rita and Tang, Su-Kit and Pau, Giovanni},
TITLE = {A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation},
JOURNAL = {Electronics},
VOLUME = {12},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {657},
URL = {https://www.mdpi.com/2079-9292/12/3/657},
ISSN = {2079-9292},
DOI = {10.3390/electronics12030657}
}
```

## Source code structure

- `data` directory: contains the preprocessed data `lg_600_data.npy` of [**LG 18650HG2 Li-ion Battery Data**](https://data.mendeley.com/datasets/cp3473x7xv/3) that is reshape into the form of $[n,600,4]$. Please refer to the [**SOC estimator**](https://github.com/KeiLongW/battery-state-estimation) repository for detail handling of the preprocessing. 
- `result` directory: the training result will be output to this directory.
- `soc_models` directory: contains the pre-trained model of the SOC estimator used in this work (download from [here](https://github.com/KeiLongW/battery-state-estimation/releases/tag/v1.0)).
- `arg_parser.py`: code to parse command line arguments.
- `lg_dataset.py`: code to handle the LG dataset.
- `gan_trainer_base.py`: code to handle the basic GAN training procedure.
- `train_lstm_gan.ipynb`: jupyter notebook for evaluating the training result.
- `train_lstm_gan.py`: main code for the proposed GAN model.

## Quick start

### Install requirements
Create conda environment from our yml file. Depending on your server's configuration, the version of PyTorch and TensorFlow may differ.
```
conda env create -f environment.yml
```

### Available arguments
Use below command to view the available arguments. The default arguments are the parameters used in our paper.
```
python3 train_lstm_gan.py -h
```

### Training
Start training with the default parameters.
```
python3 train_lstm_gan.py
```

### Evaluation
Once you start the training, a new result will be created in the `results` directory. Run the `result.ipynb` notebook to view the training result.
Alternatively, you can download our result form release and place to the `results` directory to evaluate our trained model that was presented in the paper.

synthetic-battery-data

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