{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "expmkveO04pw" }, "source": [ "## Creating a Model\n", "\n", "In this notebook, we create and solve a single particle model (SPM). This is achieved using a predefined parameter set introduced in Marquis et al. [[1]](https://doi.org/10.1149/1945-7111/abbce4) \n", "\n", "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --upgrade pip ipywidgets -q\n", "%pip install pybop -q\n", "\n", "import pybop\n", "\n", "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" ] }, { "cell_type": "markdown", "metadata": { "id": "5XU-dMtU04p2" }, "source": [ "## Creating a Model\n", "\n", "PyBOP offers the both forward emperical and physics-based forward models. These are provided by PyBaMM, with PyBOP adding wrappers on the underlying classes to reduce complexity and improve stability with parameter inference and design optimisation. Likewise, PyBOP provides a light wrapper on the PyBaMM parameter sets, with user-defined parameters available through the same `pybop.ParameterSet` class.\n", "\n", "Let's construct the parameter set and then the single-particle model (SPM):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Marquis2019\")\n", "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the model is constructed with the Maquis parameter set, we can use the `model.predict` method as a light wrapper on the `PyBaMM.Simulation` class. This is the recommended way to generate synthetic data, but not for parameter inference as the performance cost of constructing the `Simulation` class is high. For parameter inference, `model.simulate` and `model.simulateS1` offer a performant way to solve the forward model with and without sensitivities respectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing the Model\n", "Having constructed the model, we can now have a look at its voltage discharge curve to verify that it is working. The discharge curve is evaluated on the time interval specified by `t_eval`. `model.predict` returns the `PyBaMM.solution` object with all of its functionality. As we are only working with the forward model, PyBaMM plotting methods will be used; however, when performing parameter inference or design optimisation, PyBOP plotting methods are recommended." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_eval = [0, 3700]\n", "solution = model.predict([], t_eval) # No inputs i.e []\n", "\n", "# Plot with PyBaMM\n", "solution.plot_voltage_components()" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 }