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https://github.com/Samuel-Buteau/universal-battery-database

Open source Li-ion data management and modelling software
https://github.com/Samuel-Buteau/universal-battery-database

deep-learning lithium-ion lithium-ion-cells ml tensorflow universal-battery-database

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Open source Li-ion data management and modelling software

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README 

        
# Universal Battery Database



The Universal Battery Database is an open source software for managing Lithium-ion cell data. Its primary purposes are:

1. Organize and parse experimental measurement (e.g. long term cycling and electrochemical impedance spectroscopy) data files of Lithium-ion cells.

2. Perform sophisticated modelling using machine learning and physics-based approaches.

3. Describe and organize the design and chemistry information of cells (e.g. electrodes, electrolytes, geometry), as well as experimental conditions (e.g. temperature).

4. Automatically refresh a database as new data comes in.

5. Visualize experimental results.

6. Quickly search and find data of interest.

7. Quality control.



The Universal Battery Database was developed at the [Jeff Dahn Research Group](https://www.dal.ca/diff/dahn/about.html) at Dalhousie University.



## Table of Contents



- [Preliminary Results](#preliminary-results)

- [Data Management Software Demo](#data-management-software-demo)

- [Installation](#installation)

  * [Prerequisites](#prerequisites)

  * [Two Installation Options](#two-installation-options)

- [Using the Software](#using-the-software)

- [Physics and Computer Science Behind the Software](#physics-and-computer-science-behind-the-software)

- [Contributing](#contributing)

  * [Code Conventions](#code-conventions)

  

## Preliminary Results



![alt text](https://github.com/Samuel-Buteau/universal-battery-database/blob/master/demo_screenshots/capacity_measured_and_modelled.png)



**Figure 1**: Model measurements and make predictions using [`ml_smoothing.py`](https://github.com/Samuel-Buteau/universal-battery-database/wiki/ml_smoothing.py).



## Data Management Software Demo



![alt text](https://github.com/Samuel-Buteau/universal-battery-database/blob/master/demo_screenshots/fix_cycle_example.png)



**Figure 2**: Fix anomologous cycling data using the web browser provided by [`manage.py`](https://github.com/Samuel-Buteau/universal-battery-database/wiki/manage.py).



## Installation



### Prerequisites



- [Python 3](https://www.python.org/downloads/)

- [pip and virtualenv](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)



### Two Installation Options



1. If you only want to play around with modelling and you have a compiled dataset from somewhere else, you can [install without a database](https://github.com/Samuel-Buteau/universal-battery-database/wiki/Installing-Without-a-Database-(Windows)). This option is simpler and you can always install a database later.

2. If you want to use the full database features such as parsing and organising experimental data and metadata, you should [install with a database](https://github.com/Samuel-Buteau/universal-battery-database/wiki/Installing-With-a-Database-(Windows)).



## Using the Software



Use [`manage.py`](https://github.com/Samuel-Buteau/universal-battery-database/wiki/manage.py) to see the web page and use its analytic features.



Use [`ml_smoothing.py`](https://github.com/Samuel-Buteau/universal-battery-database/wiki/ml_smoothing.py) to use the machine learning model and see the results.



## Physics and Computer Science Behind the Software



We hypothesize that we can make [good generalizations](https://github.com/Samuel-Buteau/universal-battery-database/wiki/Generalization-Criteria) by [approximating](https://github.com/Samuel-Buteau/universal-battery-database/wiki/The-Universal-Approximation-Theorem) the functions that map one degradation mechanism to another using neural networks. 



We aim to develop a theory of lithium-ion cells. We first break down the machine learning problem into smaller sub-problems. From there, we develop frameworks to convert the theory to practical implementations. Finally, we apply the method to experimental data and evaluate the result.



## Contributing



### Code Conventions



Generally, we follow [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md).