Metadata-Version: 2.1
Name: liionpack
Version: 0.3
Summary: A battery pack simulator for PyBaMM
Home-page: https://liionpack.readthedocs.io/en/latest/
Author: Tom Tranter
Author-email: t.g.tranter@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://liionpack.readthedocs.io/en/latest/
Project-URL: Source, https://github.com/pybamm-team/liionpack
Project-URL: Tracker, https://github.com/pybamm-team/liionpack/issues
Description: ![logo](https://raw.githubusercontent.com/pybamm-team/liionpack/main/docs/liionpack.png)
        
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        # Overview of liionpack
        *liionpack* takes a 1D PyBaMM model and makes it into a pack. You can either specify
        the configuration e.g. 16 cells in parallel and 2 in series (16p2s) or load a
        netlist.
        
        ## Installation
        
        Follow the steps given below to install `liionpack`. The package must be installed to run the included examples. It is recommended to create a virtual environment for the installation, see [the documentation](https://liionpack.readthedocs.io/en/main/install/).
        
        To install `liionpack` using `pip`, run the following command:
        ```bash
        pip install liionpack
        ```
        
        ### LaTeX
        
        In order to use the `draw_circuit` functionality a version of Latex must be installed on your machine. We use an underlying Python package `Lcapy` for making the drawing and direct you to its installation instructions [here](https://lcapy.readthedocs.io/en/latest/install.html) for operating system specifics.
        
        ## Example Usage
        
        The following code block illustrates how to use liionpack to perform a simulation:
        
        ```python
        import liionpack as lp
        import numpy as np
        import pybamm
        
        # Generate the netlist
        netlist = lp.setup_circuit(Np=16, Ns=2, Rb=1e-4, Rc=1e-2, Ri=5e-2, V=3.2, I=80.0)
        
        output_variables = [
            'X-averaged total heating [W.m-3]',
            'Volume-averaged cell temperature [K]',
            'X-averaged negative particle surface concentration [mol.m-3]',
            'X-averaged positive particle surface concentration [mol.m-3]',
        ]
        
        # Heat transfer coefficients
        htc = np.ones(32) * 10
        
        # Cycling experiment, using PyBaMM
        experiment = pybamm.Experiment([
            "Charge at 20 A for 30 minutes",
            "Rest for 15 minutes",
            "Discharge at 20 A for 30 minutes",
            "Rest for 30 minutes"],
            period="10 seconds")
        
        # PyBaMM parameters
        chemistry = pybamm.parameter_sets.Chen2020
        parameter_values = pybamm.ParameterValues(chemistry=chemistry)
        
        # Solve pack
        output = lp.solve(netlist=netlist,
                          parameter_values=parameter_values,
                          experiment=experiment,
                          output_variables=output_variables,
                          htc=htc)
        ```
        
        ## Documentation
        
        There is a full API documentation, hosted on Read The Docs that can be found [here](https://liionpack.readthedocs.io/).
        
        ## Contributing to liionpack
        
        If you'd like to help us develop liionpack by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at these [guidelines](https://github.com/pybamm-team/liionpack/blob/main/docs/contributing.md) first.
        
        ## Get in touch
        
        For any questions, comments, suggestions or bug reports, please see the [contact page](https://www.pybamm.org/contact).
        
        ## Acknowledgments
        
        PyBaMM-team acknowledges the funding and support of the Faraday Institution's multi-scale modelling project and Innovate UK.
        
        The development work carried out by members at Oak Ridge National Laboratory was partially sponsored by the Office of Electricity under the United States Department of Energy (DOE).
        
        ## License
        
        liionpack is fully open source. For more information about its license, see [LICENSE](https://github.com/pybamm-team/liionpack/blob/main/LICENSE).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
