DanielPuentee / LSTM-GANS-RUL-Prediction-for-Lithium-ion-Bateries

This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).

Lithium-ion battery optimal RUL prediction combining LSTM and GANs

This repository contains the code used for the research study of RUL prediction, based on data augmentation .

What is this project about?

For this project, based on the RUL prediction, a deep learning based approach trained on the widely-used Oxford battery degradation dataset with the help of generative adversarial networks (GANS) has been implemented.

Lithium-ion batteries are one of the most widely used solutions in many sectors, such as electric vehicles, thanks to their higher energy density and low self-discharge. With the use and passage of time, batteries degrade and eventually die, endangering the integrity of the objects they power.

To prevent all these from happening a “A deep learning based approach for lithium-ion-battery RUL prediction based on data augmentation” model has been designed as our project.

Algorithms used

• Simple LSTM & GRU
• Bidirectional LSTM & GRU
• LSTM-GANs

Folder distribution

  .
  ├── 01_dev                    
  │   ├── functions         # Functions used 
  │   ├── hyperas_tunning         # Neural Network tunning notebook
  │   └── ...                # Rest of the notebooks used for the project development
  ├── images
  └──...

Development 👋

Want to contribute? Great! Open a discussion in Github in this repo and we will answer as soon as possible.

Authors

• Jon Amelibia
• Iker Cumplido
• Aitor Hernandez
• Daniel Puente
• Iñigo Ugarte