{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Map-driven analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way to use **galpynostatic** is to perform an analysis with the map by determining the fundamental parameters involved using another technique (experimental, computational, etc.).\n", "\n", "First, we import the libraries that we will use throughout this example." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import galpynostatic as gp\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare materials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the map to predict the maximum SOC value of different materials and use it as a figure of merit to compare them.\n", "\n", "Suppose we have two different materials (A and B) with the same particle size but different diffusion coefficient and kinetic rate constant properties. We want to know if they are good candidates for a 15 minute load." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d = 0.001\n", "\n", "materials = {\n", " \"A\": {\"dcoeff\": 3.7e-9, \"k0\": 2.03e-7},\n", " \"B\": {\"dcoeff\": 1.17e-11, \"k0\": 1.14e-6},\n", "}\n", "\n", "C_rate = np.array([[4.0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have all the information we need to describe the system in the model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "material A has a maximum SOC value of 0.87\n", "material B has a maximum SOC value of 0.02\n" ] } ], "source": [ "spherical = gp.datasets.load_dataset(\"spherical\")\n", "greg = gp.model.GalvanostaticRegressor(d=d)\n", "\n", "for m in (\"A\", \"B\"):\n", " # artificial fit\n", " greg._map = gp.base.MapSpline(spherical)\n", " greg.dcoeff_ = materials[m][\"dcoeff\"]\n", " greg.k0_ = materials[m][\"k0\"]\n", " \n", " soc_max = greg.predict(C_rate)\n", " \n", " print(f\"material {m} has a maximum SOC value of {soc_max[0]:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see that Material A will retain 87% of its capacity if charged for 15 minutes, while Material B will only retain the 2%. Then material A is a good candidate for a 15 minute charge but material B is not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Map visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can see where the materials are located on the map." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "greg = gp.model.GalvanostaticRegressor(d=d)\n", "greg._map = gp.base.MapSpline(spherical)\n", "\n", "ax = greg.plot.render_map(ax=ax)\n", "\n", "for m, marker, color in zip([\"A\", \"B\"], [\"^\", \"v\"], [\"blue\", \"red\"]):\n", " # artificial fit\n", " greg.dcoeff_ = materials[m][\"dcoeff\"]\n", " greg.k0_ = materials[m][\"k0\"]\n", " \n", " greg.plot.in_render_map(\n", " C_rate, \n", " ax=ax, \n", " marker=marker, \n", " markersize=7,\n", " linestyle=\"\",\n", " color=color,\n", " label=m,\n", " )\n", "\n", "ax.legend(title=\"material\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A generalisation of this procedure can be found in the [following tutorial](https://galpynostatic.readthedocs.io/en/latest/tutorials/benchmarking_metrics.html)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }