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82 changes: 52 additions & 30 deletions README.md
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![PyBaMM-TEA-logo](https://github.com/pybamm-team/pybamm-tea/blob/main/docs/_static/pybamm_tea_logo.PNG)

#

<div align="center">

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pybamm-team/PyBaMM/blob/main/)


</div>


# PyBaMM-TEA

This repository contains the work of the "Google Summer of Code" project on a techno-economic analysis library for battery cells, which can be combined with PyBaMM's functionality. So far, there is a method to visualize mass- and volume- loadings of an electrode stack and to estimate energy densities without simulation. The project further aims to estimate cell metrics with simulations (e.g. a Ragone plot) and manufacturing metrics with a Process-based Cost Model.
This repository contains the work of the "Google Summer of Code" project for a techno-economic analysis library, that can be combined with PyBaMM - as for creating a Ragone plot. By now, the package can be used to estimate metrics at the electrode stack level.

## Example usage

After setting up a TEA class from an input- and/or PyBaMM parameter-set, dataframes with cell metrics and different plots can be created.

```python3
import pybamm
import pybamm_tea

# create an input dictionary
input["Electrolyte density [kg.m-3]"] = 1276

# create a base parameter set
base = pybamm.ParameterValues("Chen2020")

# create a TEA class
tea_class = pybamm_tea.TEA(base, input)

# print the stack energy
print(tea_class.stack_energy_dataframe)

# print the capacities and potentials
print(tea_class.capacities_and_potentials_dataframe)

# print the mass and volume loadings as a dataframe
print(tea_class.masses_and_volumes_dataframe)

# print the electrolyte metrics
print(tea_class.electrolyte_dataframe)

# plot the mass and volume loadings
tea_class.plot_masses_and_volumes(show_plot=True)

# plot the lithiation plot
tea_class.plot_lithiation(show_plot=True)

# plot the differential lithiation plot
tea_class.differential_lithiation_plot(show_plot=True)

# plot a Ragone plot
pybamm_tea.plot_ragone([tea_class], C_rates = [0.1,1,2,3,5], plot_capacities_and_potentials=True, show_plot=True)
```

## Installation
We recommend installing within a [virtual environment](https://docs.python.org/3/tutorial/venv.html) in order to not alter any python distribution files on your machine.
Expand Down Expand Up @@ -64,35 +115,6 @@ To install the project in editable mode use `pip install -e .`

As an alternative, you can set up [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/about). This allows you to run a full Linux distribution within Windows.

## Example usage

Create a script - e.g. to plot a mass- and volume-breakdown of an electrode stack - as below or as in the example notebook.

```python3
from pybamm_tea import TEA
import pybamm

# Load a base parameter set from PyBaMM
param_nco = pybamm.ParameterValues("Ecker2015")

# Provide additional input data for the TEA model
nco_input_data = {
"Electrolyte density [kg.m-3]": 1276, # EC:EMC
"Negative electrode active material density [kg.m-3]": 2266, # Graphite
"Positive electrode active material density [kg.m-3]": 4750, # NCO
}

# Create a TEA object, passing in the parameter set and input data
tea_nco = TEA(param_nco, nco_input_data)

# Plot a mass- and volume-loading breakdown for an electrode stack
tea_nco.plot_stack_breakdown()

# Print a dataframe with values of the mass- and volume-loading breakdown for an
# electrode stack
print(tea_nco.stack_breakdown_dataframe)
```

## Documentation
API documentation for the `pybamm_tea` package can be built locally using [Sphinx](https://www.sphinx-doc.org/en/master/). To build the documentation first [clone the repository](https://github.com/git-guides/git-clone), then run the following command:
```bash
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45 changes: 29 additions & 16 deletions examples/stack_breakdown.py
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from pybamm_tea import TEA
import pybamm
import pybamm_tea

# Load a base parameter set from PyBaMM
param_nco = pybamm.ParameterValues("Ecker2015")
# create an input dictionary
input["Electrolyte density [kg.m-3]"] = 1276

# Provide additional input data for the TEA model
nco_input_data = {
"Electrolyte density [kg.m-3]": 1276, # EC:EMC
"Negative electrode active material density [kg.m-3]": 2266, # Graphite
"Positive electrode active material density [kg.m-3]": 4750, # NCO
}
# create a base parameter set
base = pybamm.ParameterValues("Chen2020")

# Create a TEA object, passing in the parameter set and input data
tea_nco = TEA(param_nco, nco_input_data)
# create a TEA class
tea_class = pybamm_tea.TEA(base, input)

# Plot a mass- and volume-loading breakdown for an electrode stack
tea_nco.plot_stack_breakdown()
# print the stack energy
print(tea_class.stack_energy_dataframe)

# Print a dataframe with values of the mass- and volume-loading breakdown for an
# electrode stack
print(tea_nco.stack_breakdown_dataframe)
# print the capacities and potentials
print(tea_class.capacities_and_potentials_dataframe)

# print the mass and volume loadings as a dataframe
print(tea_class.masses_and_volumes_dataframe)

# print the electrolyte metrics
print(tea_class.electrolyte_dataframe)

# plot the mass and volume loadings
tea_class.plot_masses_and_volumes(show_plot=True)

# plot the lithiation plot
tea_class.plot_lithiation(show_plot=True)

# plot the differential lithiation plot
tea_class.differential_lithiation_plot(show_plot=True)

# plot a Ragone plot
pybamm_tea.plot_ragone([tea_class], C_rates = [0.1,1,2,3,5], plot_capacities_and_potentials=True, show_plot=True)
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1 change: 1 addition & 0 deletions pybamm_tea/__init__.py
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__version__ = "0.0.0"

from .tea import TEA
from .tea import ragone_plot
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