# universal-battery-database

**Repository Path**: kingarthurhust/universal-battery-database

## Basic Information

- **Project Name**: universal-battery-database
- **Description**: Open source Li-ion data management and modelling software
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-02-22
- **Last Updated**: 2021-06-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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).