From 16e19ea752574f5da198b3eb4123dbebc9f5faab Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 31 May 2024 14:50:22 +0100 Subject: [PATCH 1/8] Minor updates to SEI-on-cracks example notebook --- .../notebooks/models/SEI-on-cracks.ipynb | 46 ++++++++----------- 1 file changed, 20 insertions(+), 26 deletions(-) diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb index f9d41ffc54..47d618fb23 100644 --- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb @@ -20,9 +20,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -103,10 +100,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 426.174, , mxstep steps taken before reaching tout.\n", - "At t = 186.174, , mxstep steps taken before reaching tout.\n", - "At t = 430.603, , mxstep steps taken before reaching tout.\n", - "At t = 190.603, , mxstep steps taken before reaching tout.\n" + "At t = 431.796, , mxstep steps taken before reaching tout.\n", + "At t = 191.796, , mxstep steps taken before reaching tout.\n", + "At t = 430.599, , mxstep steps taken before reaching tout.\n", + "At t = 190.599, , mxstep steps taken before reaching tout.\n" ] } ], @@ -154,26 +151,24 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 4))\n", "ax1.plot(t1, V1, label=\"without cracking\")\n", - "ax1.plot(t2, V2, label=\"with cracking\")\n", + "ax1.plot(t2, V2, label=\"with cracking\", linestyle=\"dashed\")\n", "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", "ax1.legend()\n", "ax2.plot(t1, SEI1, label=\"without cracking\")\n", - "ax2.plot(t2, SEI2, label=\"with cracking\")\n", + "ax2.plot(t2, SEI2, label=\"with cracking\", linestyle=\"dashed\")\n", "ax2.set_xlabel(\"Time [s]\")\n", "ax2.set_ylabel(\"Loss of lithium to SEI [mol]\")\n", "ax2.legend()\n", @@ -196,14 +191,12 @@ "outputs": [ { "data": { - "image/png": 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", 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AdVrdYNN/DsqFCSqLbmHPEY5aEhERVcUTFVqrVq1C69atoVKpoFKp0KZNG/z444/VnY1qEI8W/ljX+nu8Xh6JT3dcQnF5pdSRiIiIajy9C6158+ZhzJgx6NWrF9avX4/169ejR48eGD16NL766itDZKQa4o2+feFiWw9Z+SVYuOec1HGIiIhqPL3naHl5eeHf//43hg4dqtO+cuVKfPbZZ8jMzKzWgMautszRum/Xiet4f9WfmGC2Gd0HR8Cjhb/UkYiIiJ45g83Rys7ORkhIyAPtISEhyM7O1vdwAIBFixbB09MTSqUSwcHBSE1NfWTfpUuXolOnTrCzs4OdnR1CQ0Mf6H/37l2MGzcObm5uUKlU8PHxwZIlussSjBo1Ck2aNIFKpYKDgwP69euHU6dO6fSJj49HSEgIrKys4OzsjEmTJqGy8v8vmV28eBEymeyBLTk5+Yl+D7XBSz5O+I/9z3jX5Bfc3TQeGrVG6khEREQ1lt6FVtOmTbF+/foH2mNiYtCsWTO9A8TExCAiIgKRkZE4dOgQ/Pz80L17d+Tm5j60/969ezFo0CAkJCQgKSkJ7u7uCAsLQ1ZWlrZPREQE4uLisHr1apw8eRITJkzAuHHjEBsbq+0TEBCA5cuX4+TJk9i5cyeEEAgLC4NarQYAZGRkoFevXujRowfS09MRExOD2NhYTJ48+YFMu3fvRnZ2tnYLCAjQ+/dQmzR5/d8oFuZoVX4UBzbxcjEREdEjCT1t2LBBmJiYiO7du4vp06eL6dOni+7duwtTU1OxadMmfQ8ngoKCxNixY7U/q9Vq4eLiIqKioqq0f2VlpbCyshIrV67UtrVq1UpMnz5dp5+/v7+YOnXqI4+TkZEhAIhz584JIYSYMmWKCAwM1OkTGxsrlEqlKCwsFEIIkZmZKQCI9PT0KmV9mIKCAgFAFBQUPPExpJD003QhIq3FnWlOIvvyWanjEBERPVNV/f7We0TrtddeQ0pKCuzt7bFlyxZs2bIF9vb2SE1NxSuvvKLXscrLy5GWlobQ0FBtm1wuR2hoKJKSkqp0jOLiYlRUVKB+/fratpCQEMTGxiIrKwtCCCQkJODMmTMICwt76DGKioqwfPlyeHl5aZ/RWFZWBqVSqdNPpVKhtLQUaWlpOu19+/aFo6MjOnbsqDNq9jBlZWUoLCzU2Wqj5wZMwSnTlrCUleD6mjEQGl5CJCIi+l9PtLxDQEAAVq9ejbS0NKSlpWH16tVo166d3se5efMm1Go1nJycdNqdnJyQk5NTpWNMmjQJLi4uOsXaggUL4OPjAzc3NygUCvTo0QOLFi1C586ddfZdvHgxLC0tYWlpiR07dmDXrl1QKBQAgO7du2P//v1Yu3Yt1Go1srKyMH36dADQzkWztLTE3Llz8fPPP+PXX39Fx44d0b9//8cWW1FRUbCxsdFutfXh2yamplC9vhjlwhR+Jak4GMtH8xAREf2vJ16wNDc3F8eOHcORI0d0tmcpOjoa69atw+bNm3VGnxYsWIDk5GTExsYiLS0Nc+fOxdixY7F7926d/QcPHoz09HQkJibC29sbAwYMQGlpKQAgLCwMs2fPxujRo2Fubg5vb2/06tULwL1RNwCwt7dHREQEgoOD8dxzzyE6OhpvvfUWZs+e/cjMU6ZMQUFBgXa7cuVKdf9anhmPFv5IazwKAOB8eD5y8+9KnIiIiKiG0fea5MGDB0WrVq2EXC4XMplMZ5PL5Xodq6ysTJiYmIjNmzfrtA8dOlT07dv3sfvOnj1b2NjYiAMHDui0FxcXCzMzM7Ft2zad9hEjRoju3bs/NouFhYVYs2aNTrtGoxFZWVmiuLhYnDhxQgAQqampjzzOwoULhbOz82Oz/1VtnaN1X3lZqdgYFS6em/SjCP8hRWg0GqkjERERGZzB5mi9/fbb8Pb2xv79+3HhwgVkZmZqN30fKK1QKBAQEID4+Hhtm0ajQXx8PDp06PDI/WbNmoXPP/8ccXFxCAwM1HmtoqICFRUV2lGn+0xMTKB5zDwi8d/HCZWVlem0y2QyuLi4QKVSYe3atXB3d4e//6PXjjp8+DAaNmz4yNeNjZnCHK2GLUC+aQPsPX0DP6VcljoSERFRjWGq7w4XLlzAxo0b0bRp02oJEBERgfDwcAQGBiIoKAjz589HUVERhg8fDgAYOnQoXF1dERUVBQCYOXMmpk2bhjVr1sDT01M7l+v+XCtra2t06dIFEydOhEqlgoeHBxITE7Fq1SrMmzdPew4xMTEICwuDg4MDrl69iujoaKhUKu3lQQCYPXs2evToAblcjk2bNiE6Ohrr16+HiYkJgHuLtCoUCu38tE2bNuGHH37AsmXLquV3U1s0d7bCR92bY8avJ5H06yp0se8P96atpY5FREQkPX2Hyvr16yc2bNjwhANtD7dgwQLRqFEjoVAoRFBQkEhOTta+1qVLFxEeHq792cPDQwB4YIuMjNT2yc7OFsOGDRMuLi5CqVSK5s2bi7lz52ova2VlZYmePXsKR0dHYWZmJtzc3MSbb74pTp06pZOra9euwsbGRiiVShEcHCy2b9+u8/qKFStEy5YthYWFhbC2thZBQUHi559/1uvca/ulw/vUao1YOXeiEJHW4tTnz4mK8jKpIxERERlMVb+/9X4Ez82bNxEeHo6goCD4+vrCzMxM5/W+fftWTwVYR9S2R/A8Ts6Vc7D4vhOsUYykRu+iw9uPvimAiIioNqvq97felw6TkpKwb98+7Nix44HXZDKZdmV1qnuc3ZviYMBnCEz7CEGXluJE8ovwad9D6lhERESS0Xsy/HvvvYe33noL2dnZ0Gg0OhuLLArsMwoHbLrDRCbQIO6fyL9ZtfXQiIiIjJHehdatW7fwwQcfPLDIKNF9PiO/wxWZC5xwC5k/DOeq8UREVGfpXWi9+uqrSEhIMEQWMhL1rGxR1n8ZyoUp2hXvR9yvG6WOREREJAm952h5e3tjypQp+PPPP9G6desHJsOPHz++2sJR7dXU73nsO/UxVmTcRWKyJVza5sPP3VbqWERERM+U3ncdenl5PfpgMpnei5bWdcZ01+H/EkJg1I9p+O3EdbjaqrDtvY6wq6eQOhYREdFTM9hdh5mZmU8VjOoOmUyG2W/44fTCP1F26wr2/ec99Hp/MeT/XfCViIjI2D3xQ6WJqsJGZYYl//DFRvN/4+XCdUhZ9bHUkYiIiJ6ZKhVaERERKCoqqvJBp0yZgry8vCcORcalpbsDstq+DwAIvvgfHE3cJHEiIiKiZ6NKhdbXX3+N4uLiKh900aJFyM/Pf9JMZISCXhmPVLuXIZcJeCSMw5WzGVJHIiIiMrgqzdESQsDb2xsymaxKB9Vn9IvqDr9RS3FqTje0qDyJ/LWDUDj+D1jbNpA6FhERkcFUqdBavny53gfmgqb0v8yVFrAfuR7Xl7yARposHFkyEK0+jIOJqd73ZBAREdUKei/vQNXLmJd3eJRzGX/CddMryBQN8VvAEkzoFyJ1JCIiIr0YbHkHoqfV1K8j9t38HiN3VaAk6Tbc3a7itQA3qWMRERFVOy7vQJJ4/sW+GNHVFwAwZdNRHD5/ReJERERE1Y+FFkkm4iVvvNTSESOxCS4/dsb1q+eljkRERFStWGiRZORyGb56rTleN0+FI/JQuHwASoruSB2LiIio2jx1oVVYWIgtW7bg5MmT1ZGH6hhLS2soh8TgNqzQTH0OpxYNhLqyUupYRERE1ULvQmvAgAFYuHAhAKCkpASBgYEYMGAA2rRpg40bN1Z7QDJ+Ll4tkNPze5QJM7Qr3ocD/xktdSQiIqJqoXeh9fvvv6NTp04AgM2bN0MIgfz8fHzzzTeYMWNGtQekuqFlcHccC54JAGh/42ckr/lc4kRERERPT+9Cq6CgAPXr1wcAxMXF4bXXXoOFhQV69+6Ns2fPVntAqjsCeo1ActMJAICg03Pxx77fpQ1ERET0lPQutNzd3ZGUlISioiLExcUhLCwMAHD79m0olcpqD0h1S/CbkUhyeANTK9/GyB1FOHiRDycnIqLaS+9Ca8KECRg8eDDc3NzQsGFDvPDCCwDuXVJs3bp1deejOkYmlyNozFLc8B6EskoN3l5xACezC6WORURE9ET0LrT++c9/IikpCT/88AP27dsHufzeIRo3bsw5WlQtTOQyLBjkj0APO5iU5iH3u1dx7cIJqWMRERHp7YmfdVheXo7MzEw0adIEpnwo8BOri886rKqCkgocntcfXSr+RJbMCebv7IK9i4fUsYiIiKr8/a33iFZxcTFGjBgBCwsLtGrVCpcvXwYAvPfee4iOjn7yxET/w0ZlhlbDFyNL5gRXcR13lvVFQd4NqWMRERFVmd6F1pQpU5CRkYG9e/fqTH4PDQ1FTExMtYYjsnfxAIZsxU3YwktzEde+7cvV44mIqNbQu9DasmULFi5ciI4dO0Imk2nbW7VqhfPn+aw6qn6ujVui8PX1KEQ9tKw4gTMLX0VFeZnUsYiIiP6W3oXWjRs34Ojo+EB7UVGRTuFFVJ0a+wbjWs8VKBEK+JWkIunb0dBonmh6IRER0TOjd6EVGBiIX3/9Vfvz/eJq2bJl6NChQ/UlI/ofLYLDcKbLIlwQDfFJTid8vPkoiy0iIqrR9L5d8Msvv0TPnj1x4sQJVFZW4uuvv8aJEyewf/9+JCYmGiIjkZZftwH4xS4YV9cfw7oDV2BqIsPn/Xw5mkpERDWS3iNaHTt2xOHDh1FZWYnWrVvjt99+g6OjI5KSkhAQEGCIjEQ6+rTzwJw3/CCTAddStyDp2zEQGo3UsYiIiB7wxOtoUfXgOlpP7pffDyAsvifMZRVIdhqE4FGLIZPr/f8OREREeqvq93eVLh0WFlb9ESgsFuhZ6dP5OaRcn4Lg49PR/vpaJC8zQ/DIr1lsERFRjVGlQsvW1rbKc2DUavVTBSLSR/Ab/0KKpgLBJ6PQ/toqJC1XoMOIuVLHIiIiAlDFQishIUH774sXL2Ly5MkYNmyY9i7DpKQkrFy5ElFRUYZJSfQYwQMnI3lNBdqfmYMOV5YhabkpOgyfKXUsIiIi/edovfjiixg5ciQGDRqk075mzRp899132Lt3b3XmM3qco1V9kn+chvbnvwYAbG05F30HjODdiEREZBAGe9ZhUlISAgMDH2gPDAxEamqqvocDACxatAienp5QKpUIDg5+7HGWLl2KTp06wc7ODnZ2dggNDX2g/927dzFu3Di4ublBpVLBx8cHS5Ys0ekzatQoNGnSBCqVCg4ODujXrx9OnTql0yc+Ph4hISGwsrKCs7MzJk2ahMrKyofmOnfuHKysrGBra/tEvwN6eu2HTEdy4/HYoO6MCelOmBl3GrzXg4iIpKR3oeXu7o6lS5c+0L5s2TK4u7vrHSAmJgYRERGIjIzEoUOH4Ofnh+7duyM3N/eh/ffu3YtBgwYhISEBSUlJcHd3R1hYGLKysrR9IiIiEBcXh9WrV+PkyZOYMGECxo0bh9jYWG2fgIAALF++HCdPnsTOnTshhEBYWJh2jllGRgZ69eqFHj16ID09HTExMYiNjcXkyZMfyFRRUYFBgwahU6dOep8/Va/2Qz9HYdjXEJBjSeJ5fB57hEs/EBGRdISefv31V6FUKoWvr68YMWKEGDFihGjdurVQKpXi119/1fdwIigoSIwdO1b7s1qtFi4uLiIqKqpK+1dWVgorKyuxcuVKbVurVq3E9OnTdfr5+/uLqVOnPvI4GRkZAoA4d+6cEEKIKVOmiMDAQJ0+sbGxQqlUisLCQp32jz76SLz11lti+fLlwsbG5rF5S0tLRUFBgXa7cuWKACAKCgqqcrpURT8mXRRek2JF7CdhIuXrt4S6slLqSEREZEQKCgqq9P2t94hWr169cPbsWfTp0wd5eXnIy8tDnz59cObMGfTq1UuvY5WXlyMtLQ2hoaHaNrlcjtDQUCQlJVXpGMXFxaioqED9+vW1bSEhIYiNjUVWVhaEEEhISMCZM2cQFhb20GMUFRVh+fLl8PLy0o7KlZWVQalU6vRTqVQoLS1FWlqatm3Pnj34+eefsWjRoirljYqKgo2NjXZ7klFA+ntvtffA910r0UuegqC8WKQteBPqR1z2JSIiMphnU/c9XFZWlgAg9u/fr9M+ceJEERQUVKVjjBkzRjRu3FiUlJRo20pLS8XQoUMFAGFqaioUCoXOiNd9ixYtEvXq1RMARPPmzbWjWUIIsXPnTiGXy8WaNWtEZWWluHr1qujUqZMAINasWSOEEOLmzZvC3d1dJCYmCiEER7RqoAO//EdUTLMVItJaHJzTX5SXlUodiYiIjIDBRrQAID8/H3PnzsXIkSMxcuRIfPXVVygoKKi+6q+KoqOjsW7dOmzevFln9GnBggVITk5GbGws0tLSMHfuXIwdOxa7d+/W2X/w4MFIT09HYmIivL29MWDAAJSWlgIAwsLCMHv2bIwePRrm5ubw9vbWjtjJ/7sg5jvvvIM333wTnTt3rnJmc3NzWFtb62xkOIEvv4ujIfNRLkwQcGcPjs1/BaUlRVLHIiKiukLfCu7AgQOifv36wtXVVbzyyivilVdeEW5ubqJBgwYiLS1Nr2OVlZUJExMTsXnzZp32oUOHir59+z5239mzZwsbGxtx4MABnfbi4mJhZmYmtm3bptM+YsQI0b1798dmsbCw0I5W3afRaERWVpYoLi4WJ06cEABEamqqEEIIGxsbYWJiot3kcrkAIExMTMT333//d6cvhKh6RUxP53D8WlE6rYEQkdbi6JedxZ2CPKkjERFRLWawEa0PPvgAffv2xcWLF7Fp0yZs2rQJmZmZePnllzFhwgS9jqVQKBAQEID4+Hhtm0ajQXx8vHYx1IeZNWsWPv/8c8TFxT2w1ERFRQUqKiq0o073mZiYQPOYu8+EEBBCoKysTKddJpPBxcUFKpUKa9euhbu7O/z9/QHcW+ri8OHD2m369OmwsrLC4cOH8corr1T590CG59ftHzgbthx3hQpNS4/js+834tbdsr/fkYiI6ClUaWX4vzp48CCWLl0KU9P/39XU1BQfffTRQ9fX+jsREREIDw9HYGAggoKCMH/+fBQVFWH48OEAgKFDh8LV1VW76vzMmTMxbdo0rFmzBp6ensjJyQEAWFpawtLSEtbW1ujSpQsmTpwIlUoFDw8PJCYmYtWqVZg3bx4A4MKFC4iJiUFYWBgcHBxw9epVREdHQ6VS6Uzonz17Nnr06AG5XI5NmzYhOjoa69evh4mJCQCgZcuWD/xu5HI5fH199f49kOH5Pt8HZ+v9jAWx+xB7vSEO/ScJP44IhqutSupoRERkpPQutKytrXH58mW0aNFCp/3KlSuwsrLSO8DAgQNx48YNTJs2DTk5OWjbti3i4uLg5OQEALh8+bLO6NS3336L8vJyvP766zrHiYyMxGeffQYAWLduHaZMmYLBgwcjLy8PHh4e+OKLLzB69GgAgFKpxB9//IH58+fj9u3bcHJyQufOnbF//344Ojpqj7ljxw588cUXKCsrg5+fH7Zu3YqePXvqfY5UczRr2wnvu7bDwWUpuHCjCJMWrcUXr7eDR/N2UkcjIiIjpPcjeMaPH4/Nmzdjzpw5CAkJAQDs27cPEydOxGuvvYb58+cbIqfR4iN4pHEtvwQTl8Zi3t2PoJCpcaPvT/D27yJ1LCIiqiWq+v2t94jWnDlzIJPJMHToUO3jaMzMzDBmzBhER0c/eWKiZ8jFVoWFwzrjxn8c4V15BoqtA3D07ndo3bmf1NGIiMiI6DWipVarsW/fPrRu3Rrm5uY4f/48AKBJkyawsLAwWEhjxhEtad0tvI2Li/rDt+wwKoQJMgK+QGDfMVLHIiKiGq6q3996XzpUKpU4efIkvLy8njoksdCqCcpKi3Fs0ZsIuJMAAEjyGov2Q2ZAJn+iZeaIiKgOqOr3t97fJL6+vrhw4cJThSOqScyVFmg3YSOSnQcDADpkLsIv33+OSjUfRk1ERE9H70JrxowZ+PDDD7Ft2zZkZ2ejsLBQZyOqjeQmJmg/ejGSm3+EDE1jTDnfCqNXp6G4nM9HJCKiJ6f3pcO/LrUgk8m0/xZCQCaTQa1WV1+6OoCXDmueuIzLGP/zcZRXauDnZoPlg1qgfgN7qWMREVENYrC7DhMSEp4qGFFN18OvEdbYWGLkqoN4PnsVihf+jqLBP8O9aWupoxERUS2j94jW5cuX4e7urjOaBdwb0bpy5QoaNWpUrQGNHUe0aq7zWdehWtYJLuI68mGJrLClaBXS6+93JCIio2ewyfBeXl64cePGA+15eXm8E5GMShNXJyhG7cYZU2/Y4i6a7XwLB7YslDoWERHVInoXWvfnYv2vu3fvQqlUVksooprC3rkRGkUk4JBlFyhkajx3eCqSl74PDeciEhFRFVR5jlZERASAexPgP/30U50FStVqNVJSUtC2bdtqD0gkNaWFJdp+sBlJP/wLHbKWo33WChz46jJaj/8ZSoXe0xyJiKgOqfK3RHp6OoB7I1pHjx6FQqHQvqZQKODn54cPP/yw+hMS1QByExN0eGc+DmxpCr/0adh4uzFmLE3B0qEBcLTiSC4RET2c3pPhhw8fjq+//poTt6sJJ8PXPukZ6Ri+9QbyiyvgaqvCd2+1RSu3+lLHIiKiZ8hgk+Hnz5+vfZj0X+Xl5XHBUqoT2vm1w+Z/Pg8v+3ooys+F2dLOSNu+XOpYRERUA+ldaP3jH//AunXrHmhfv349/vGPf1RLKKKazsu+Hrb883l85pgIb9kVBKROQNLSCZwkT0REOvQutFJSUtC1a9cH2l944QWkpKRUSyii2sDGwgwvv/cNku4/IzFrOY7M7Y07BXkSJyMioppC70KrrKzsoZcOKyoqUFJSUi2hiGoLUzMzdBi9GAf9o1EmzNC2OAl5X3fGlXNHpY5GREQ1gN6FVlBQEL777rsH2pcsWYKAgIBqCUVU2wT2HYNL/TYhF/XhobkCm9XdcWj/b1LHIiIiiem9CNCMGTMQGhqKjIwMvPjiiwCA+Ph4HDhwAL/9xi8Wqru8/TvjpksiTi0bAFX5LYz45TbGVJ7HO50aP3SRXyIiMn56j2g9//zzSEpKgpubG9avX49ffvkFTZs2xZEjR9CpUydDZCSqNeydG8Hrwz34qcVC3BaW+HL7KUSsz0BpaanU0YiISAJ6r6NF1YvraBknIQRWJV3C9G0nMEC2G2+rfodV+Do4uzeVOhoREVUDg62jBQDnz5/HJ598gjfffBO5ubkAgB07duD48eNPlpbIyMhkMoSHeOKnob6IMNuEZpVnYf79Czj2+xapoxER0TOkd6GVmJiI1q1bIyUlBRs3bsTdu3cBABkZGYiMjKz2gES1WfsWjVARHodzJk1ghzvwiR+GpBWTud4WEVEdoXehNXnyZMyYMQO7du3Sed5ht27dkJycXK3hiIyBi1cLuH34B1LtXoZcJtDh4rc4OqcnCvJuSB2NiIgMTO9C6+jRo3jllVceaHd0dMTNmzerJRSRsVGq6iHo/Z9woM10lAoz+JWkoHBBJxy/dF3qaEREZEB6F1q2trbIzs5+oD09PR2urq7VEorIWD336vu4+upWZMmcsLa8M15ZeggxBy5LHYuIiAzkiZ51OGnSJOTk5EAmk0Gj0WDfvn348MMPMXToUENkJDIqTf2eh+X4ZJxpOgLllRpM2ngUX67djdLiu1JHIyKiaqb38g7l5eUYO3YsVqxYAbVaDVNTU6jVarz55ptYsWIFTExMDJXVKHF5h7pLoxH4NvE8Fv+WgY1mkTA1U0A1+Ce4Nm4pdTQiIvobVf3+fuJ1tC5fvoxjx47h7t27aNeuHZo1a/bEYesyFlqUnvoHPLe/CTsUohD1cOH5OWj70ptSxyIioscweKFF1YOFFgHA9avnkL9iMJpXngIAJDsOQLu3v4a50kLiZERE9DDVWmhFRERU+Y3nzZtX5b7EQov+X3lZKQ79MAHtr68FAJwzaQLloFVwa+orcTIiIvpfVf3+rtJDpdPT06v0pnxwLtGTU5gr0X7MEhyOfwEef3yIpurzSPzxPRx69Sf09XOROh4RET0BXjqUGEe06GGuXz2PSz+Ow/iCQchBA/zjOXdE9mkFlYI3mxAR1QQGfdYhAJw7dw47d+5ESUkJgHsP0SWi6uHk1gT+H/2KAd2CIZMB6w5cwY/z/oWLJw5IHY2IiPSgd6F169YtvPjii/D29kavXr20i5eOGDEC//rXv6o9IFFdZWoiR0RYc/w0Ihiv1DuKd0uXwzmmJ1I3zIPQaKSOR0REVaB3ofXBBx/AzMwMly9fhoXF/98RNXDgQMTFxVVrOCICQpra45N338IRZSCUsgoEHfs3Dn31Gu4U5EkdjYiI/obehdZvv/2GmTNnws3NTae9WbNmuHTp0hOFWLRoETw9PaFUKhEcHIzU1NRH9l26dCk6deoEOzs72NnZITQ09IH+d+/exbhx4+Dm5gaVSgUfHx8sWbJEp8+oUaPQpEkTqFQqODg4oF+/fjh16pROn/j4eISEhMDKygrOzs6YNGkSKisrta+fPn0aXbt2hZOTE5RKJRo3boxPPvkEFRUVT/R7IHqUBk5u8J34G5Ibj0eFMEHAnT24M789Th3YLXU0IiJ6DL0LraKiIp2RrPvy8vJgbm6ud4CYmBhEREQgMjIShw4dgp+fH7p3747c3NyH9t+7dy8GDRqEhIQEJCUlwd3dHWFhYcjKytL2iYiIQFxcHFavXo2TJ09iwoQJGDduHGJjY7V9AgICsHz5cpw8eRI7d+6EEAJhYWFQq9UAgIyMDPTq1Qs9evRAeno6YmJiEBsbi8mTJ2uPYWZmhqFDh+K3337D6dOnMX/+fCxduhSRkZF6/x6I/o7cxATth36O831+xjWZI1zEdTTd9gZ+XxGJSjUvJRIR1UhCTz179hSffPKJEEIIS0tLceHCBaFWq8Ubb7whXnvtNX0PJ4KCgsTYsWO1P6vVauHi4iKioqKqtH9lZaWwsrISK1eu1La1atVKTJ8+Xaefv7+/mDp16iOPk5GRIQCIc+fOCSGEmDJliggMDNTpExsbK5RKpSgsLHzkcT744APRsWPHKmUXQoiCggIBQBQUFFR5H6KC2zfFgbmvCRFpLUZNmSZeXbxPXL5VJHUsIqI6o6rf33qPaM2aNQvfffcdevbsifLycnz00Ufw9fXF77//jpkzZ+p1rPLycqSlpSE0NFTbJpfLERoaiqSkpCodo7i4GBUVFahfv762LSQkBLGxscjKyoIQAgkJCThz5gzCwsIeeoyioiIsX74cXl5ecHd3BwCUlZVBqVTq9FOpVCgtLUVaWtpDj3Pu3DnExcWhS5cuj8xbVlaGwsJCnY1IX9a2DRAYsQG/d16LfWYhSLt0Gz2//gM79qdxojwRUQ2id6Hl6+uLM2fOoGPHjujXrx+Kiorw6quvIj09HU2aNNHrWDdv3oRarYaTk5NOu5OTE3Jycqp0jEmTJsHFxUWnWFuwYAF8fHzg5uYGhUKBHj16YNGiRejcubPOvosXL4alpSUsLS2xY8cO7Nq1CwqFAgDQvXt37N+/H2vXroVarUZWVhamT58OANo7Le8LCQmBUqlEs2bN0KlTJ22/h4mKioKNjY12u1/YET2Jzt16Yfv7nfCcpx1UZTcQtLMfDn31Ggpu35Q6GhERAfpfOqxOWVlZAoDYv3+/TvvEiRNFUFDQ3+4fFRUl7OzsREZGhk777Nmzhbe3t4iNjRUZGRliwYIFwtLSUuzatUunX35+vjhz5oxITEwUffr0Ef7+/qKkpET7+ty5c4W1tbUwMTERFhYWIioqSgAQ69at0znO5cuXxfHjx8WaNWuEq6urmDlz5iMzl5aWioKCAu125coVXjqkp1ap1ogdaxeKimm2QkRai+zIJuL4/u1SxyIiMlpVvXQo6crw5eXlsLCwwIYNG9C/f39te3h4OPLz87F169ZH7jtnzhzMmDEDu3fvRmBgoLa9pKQENjY22Lx5M3r37q1tHzlyJK5evfrIJSjKy8thZ2eHZcuWYdCgQdp2IQSys7NhZ2eHixcvwsfHB6mpqXjuueceepzVq1fj3XffxZ07d2Bi8vereHNleKpOp9MSUG/baLiJHGiEDClu4QgMnwUzhf43qhAR0aMZfGX46qBQKBAQEID4+Hhtm0ajQXx8PDp06PDI/WbNmoXPP/8ccXFxOkUWAFRUVKCiogJyue6pmZiYQPOYuStCCAghUFZWptMuk8ng4uIClUqFtWvXwt3dHf7+/o88jkajQUVFxWPfi8hQmgd0he0HyUi17QW5TKBD1gpcnPU8Lp2u2vNKiYioelXpodKGFBERgfDwcAQGBiIoKAjz589HUVERhg8fDgAYOnQoXF1dERUVBQCYOXMmpk2bhjVr1sDT01M7l+v+XCtra2t06dIFEydOhEqlgoeHBxITE7Fq1SrMmzcPAHDhwgXExMQgLCwMDg4OuHr1KqKjo6FSqdCrVy9tttmzZ6NHjx6Qy+XYtGkToqOjsX79eu1I1U8//QQzMzO0bt0a5ubmOHjwIKZMmYKBAwfCzMzsWf4aibQsre0QNGEtDu1YjiYpU9Gs8ixW//QldofNwvAQT8jlfPg7EdEzY/irmH9vwYIFolGjRkKhUIigoCCRnJysfa1Lly4iPDxc+7OHh4cA8MAWGRmp7ZOdnS2GDRsmXFxchFKpFM2bNxdz584VGo1GCHFvbljPnj2Fo6OjMDMzE25ubuLNN98Up06d0snVtWtXYWNjI5RKpQgODhbbt+vOeVm3bp3w9/cXlpaWol69esLHx0d8+eWXOvO8/g6XdyBDun71gkiYNUC0nLRBeEzaJgb+Zz+XgSAiqga1Yo4WcY4WGZ4QAmtSL2PGtpMorajAUvNvYOP3MgL7vweZXNLZA0REtZbB5mhdv34dQ4YMgYuLC0xNTWFiYqKzEVHNIpPJMDjYA3ETOmG801GEylLx3JFpyJjdEzdzLksdj4jIqOk9otWzZ09cvnwZ48aNQ8OGDSGT6c736NevX7UGNHYc0aJnSV1ZiQNr/g3/84uhkFXiNqyQGfw5/HsOlzoaEVGtUtXvb70LLSsrK/zxxx9o27bt02YksNAiaWQeT4Fm02g0UV8AABy0DkWzYUtgU99B4mRERLWDwS4duru7g9O6iGo3r1bBcP8oCUmuw6EWMgQW7saJBa9jz6nrUkcjIjIqehda8+fPx+TJk3Hx4kUDxCGiZ0VhrkSHd+bjbJ+NuCh3x5elr+PtFQcREXMY+cXlUscjIjIKel86tLOzQ3FxMSorK2FhYfHAelF5eXnVGtDY8dIh1QQlpeWYF38Oy/7MhBDAPy3i8VJ7f7QLe0vqaERENVJVv7/1XrB0/vz5T5OLiGoglVKBqb190LN1Qyxc9wsmFK2AYv/3OHh0E5oMXQQ7h4ZSRyQiqpW4jpbEOKJFNU1pSRHSf5yMoKwfYSITuAUbXAr+N+9MJCL6i2q967CwsFB7kMLCwsf2ZbGgHxZaVFOdOZQIxbZx8NTcW2vrkGVneAz5Fg2c3CRORkQkvWottExMTJCdnQ1HR0fI5fIH1s4C7q0+LZPJoFarny55HcNCi2qystJiHFo9Fc9dWQFTmQY5aIADfXbjZX+vh/4dICKqK6p1jtaePXtQv359AEBCQkL1JCSiGs9caYEOI7/CuYzXIYsdi/Wl7fGfn09iy9Fb+Ly/L1xsVVJHJCKq0ThHS2Ic0aLaorysFEt+z8SCvZmoUAt0VJzFuJbFeO6Nj2Biqvd9NUREtZrBFiwlorpJYa7E+JdaYvv4TghuVA+R+A7tT8/EuegQZB5PlToeEVGNxEKLiPTSzMkKa0d1Qp7vMNwVKjSvPA239T2QvGwCSkuKpI5HRFSjsNAiIr3JTUwQPOAjFL+zH+kWITCTqdH+6nLcmBWIE/u3Sx2PiKjGYKFFRE/M0a0x2n74Kw61/xo3YQt3cQ0+vw3Ctz+uQUFJhdTxiIgkx0KLiJ6KTC6Hf49hMHs/DSn1++J3dWvMPG6Nl+YlIu5YttTxiIgkpXehdevWLYwdOxY+Pj6wt7dH/fr1dTYiqpts7OwRPP5HKMM3oLG9JXLvlOGj1X/gz7kDkZuVKXU8IiJJ6H1P9pAhQ3Du3DmMGDECTk5OXLSQiHQENXXG9vcdsCjhHJz/+Bgd7+zGne8SkeLzAQJf+xeXgiCiOkXvdbSsrKzw559/ws/Pz1CZ6hSuo0XGLPN4Kso3j0PzytMAgDOm3pD3+QpN/TpKnIyI6OkYbB2tFi1aoKSk5KnCEVHd4NUqCE0n70dKyym4I1TwrjwDr00vI2Xh2yjMvyV1PCIig9O70Fq8eDGmTp2KxMRE3Lp1C4WFhTobEdFfmZiaInjgZJSNScVB61CYyASCb27E9m/GYevhLPDhFERkzPSeLGFra4vCwkJ069ZNp50PlSaix7F3bgT7iI049sdWyBK+wKzivshbdxg/H7yK6X190NjRSuqIRETVTu9Ca/DgwTAzM8OaNWs4GZ6I9ObbqR/KOryM4YkXsDDhHP48dwNnFr6K3Ea+aPvm51BaWEodkYio2ug9Gd7CwgLp6elo3ry5oTLVKZwMT3XZ5VvF+HH9Oky9/gEAIEvmhJudv4Bf1zckTkZE9HgGmwwfGBiIK1euPFU4IiIAaNTAAh+PGoZD7b9GLurDVVyHX+JIHJrdB9evnpc6HhHRU9N7ROvnn3/GZ599hokTJ6J169YwMzPTeb1NmzbVGtDYcUSL6J67hbdx7KcpCMyJgalMgyKhxFHvfyJwwBSYmimkjkdEpKOq3996F1py+YODYDKZjJPhnxALLSJdF46loHzL+2hReRIXNU4YZ7sYU/u1Q4cmDaSORkSkVdXvb70nw2dm8lEaRGQ4jX2DoWm5D6lbvsGKYxU4lluGQUuT0ae1I6Z2rg9n9yZSRyQiqjK9Cy0PDw9D5CAi0pKbmCDotQ/g3bMcDX47g59SLqH+iR9hfWYdkr1Got0/PoG50kLqmEREf0vvS4erVq167OtDhw59qkB1DS8dEv2949cKULLyDQSWpQAArsqccavTv+HX7R8SJyOiuspgc7Ts7Ox0fq6oqEBxcTEUCgUsLCyQl5f3ZInrKBZaRFUjNBqkbfsOHoei4YDbAIAMVTAavDYPbk19JU5HRHWNwZZ3uH37ts529+5dnD59Gh07dsTatWufKjQR0aPI5HIE9h0NVUQ6kp0Ho1yYwK8kBY4/dsH2VbNRXF4pdUQiogfoXWg9TLNmzRAdHY3333+/Og5HRPRIltZ2aD96MXIG78ERZQBMoMaCk/Xw4txE/JJxjc9OJKIapVoKLQAwNTXFtWvXqutwRESP1ci7LVp/tBvJYVtwx7YFsgtK8d7adCybPw2Zx1OkjkdEBOAJ5mjFxsbq/CyEQHZ2NhYuXAh3d3fs2LGjWgMaO87RInp6pRVqLEk8j11792KrySTIIHDQ4RV4D/wSdg4NpY5HREbomS1YKpPJ4ODggG7dumHu3Llo2JB/1PTBQouo+ly7dBY56z+Af9EfAIBC1MMJ7zHwf+0jKMzNJU5HRMbEYJPhNRqNzqZWq5GTk4M1a9Y8cZG1aNEieHp6QqlUIjg4GKmpqY/su3TpUnTq1Al2dnaws7NDaGjoA/3v3r2LcePGwc3NDSqVCj4+PliyZIlOn1GjRqFJkyZQqVRwcHBAv379cOrUKZ0+8fHxCAkJgZWVFZydnTFp0iRUVv7/hNu9e/eiX79+aNiwIerVq4e2bdvip59+eqLfARE9PRePZvCfuA3HXlqN8yZesEYR2p+Zg5zodsjYsw5Co5E6IhHVMdU2R+tJxcTEICIiApGRkTh06BD8/PzQvXt35ObmPrT/3r17MWjQICQkJCApKQnu7u4ICwtDVlaWtk9ERATi4uKwevVqnDx5EhMmTMC4ceN0LnsGBARg+fLlOHnyJHbu3AkhBMLCwrSPEMrIyECvXr3Qo0cPpKenIyYmBrGxsZg8ebL2GPv370ebNm2wceNGHDlyBMOHD8fQoUOxbds2A/22iKgqfJ/vA88pB5HqG4k8WKORyIJX4gSM/n4Pzly/I3U8IqpDqnTpMCIiAp9//jnq1auHiIiIx/adN2+eXgGCg4Px3HPPYeHChQDujZi5u7vjvffe0ylqHkWtVsPOzg4LFy7ULpbq6+uLgQMH4tNPP9X2CwgIQM+ePTFjxoyHHufIkSPw8/PDuXPn0KRJE3z88cfYtWsXDhw4oO3zyy+/YMCAAcjNzYWVldVDj9O7d284OTnhhx9+eOjrZWVlKCsr0/5cWFgId3d3XjokMpDC/Fs4se5T7M4ywbKKHjCRyzA4uBE+6NQQdvX5/EQiejLVeukwPT0dFRUV2n8/ajt8+LBeIcvLy5GWlobQ0ND/DySXIzQ0FElJSVU6RnFxMSoqKlC/fn1tW0hICGJjY5GVlQUhBBISEnDmzBmEhYU99BhFRUVYvnw5vLy84O7uDuBeQaRUKnX6qVQqlJaWIi0t7ZF5CgoKdLL8r6ioKNjY2Gi3++9HRIZhbdsA7UcvxlvvRyPMxwlqjcCllFiYfNMayWtmoKK87O8PQkT0pISEsrKyBACxf/9+nfaJEyeKoKCgKh1jzJgxonHjxqKkpETbVlpaKoYOHSoACFNTU6FQKMTKlSsf2HfRokWiXr16AoBo3ry5OHfunPa1nTt3CrlcLtasWSMqKyvF1atXRadOnQQAsWbNmodmiYmJEQqFQhw7duyReUtLS0VBQYF2u3LligAgCgoKqnS+RPR09p29IfbO6CVEpLUQkdbi0r9bisPxMVLHIqJapqCgoErf35LP0Xoa0dHRWLduHTZv3qwz+rRgwQIkJycjNjYWaWlpmDt3LsaOHYvdu3fr7D948GCkp6cjMTER3t7eGDBgAEpLSwEAYWFhmD17NkaPHg1zc3N4e3ujV69eAB688xIAEhISMHz4cCxduhStWrV6ZGZzc3NYW1vrbET07IQ0tUfHSVuRcn/+liYLfr+/gyPRobh08tGj1URET0Lv5R2KiooQHR2N+Ph45ObmQvM/d/FcuHChyscqLy+HhYUFNmzYgP79+2vbw8PDkZ+fj61btz5y3zlz5mDGjBnYvXs3AgMDte0lJSWwsbHB5s2b0bt3b237yJEjcfXqVcTFxT0yi52dHZYtW4ZBgwZp28V/1wmzs7PDxYsX4ePjg9TUVDz33HPaPomJiejduzfmzZuHd999t8rnD3B5ByIpFebfwvF1nyIgex0UMjXUQoZ45xFo+9YXcLRS/v0BiKjOqur3t6m+Bx45ciQSExMxZMgQNGzYEDKZ7IlDKhQKBAQEID4+XltoaTQaxMfHY9y4cY/cb9asWfjiiy+wc+dOnSILuPeQ64qKigdGnUxMTB4oCv9KCAEhhM5EdeDeOmEuLi4AgLVr18Ld3R3+/v7a1/fu3YuXX34ZM2fO1LvIIiJpWds2QIfRi3H13D9xY/MktCv6ExuvWGHC7L14t3NjvNu5MSwUev+ZJCLS0vsvyI4dO/Drr7/i+eefr5YAERERCA8PR2BgIIKCgjB//nwUFRVh+PDhAIChQ4fC1dUVUVFRAICZM2di2rRpWLNmDTw9PZGTkwMAsLS0hKWlJaytrdGlSxdMnDgRKpUKHh4eSExMxKpVq7R3RF64cAExMTEICwuDg4MDrl69iujoaKhUKu3lQQCYPXs2evToAblcjk2bNiE6Ohrr16+HiYkJgHuXC19++WW8//77eO2117RZFArFYyfEE1HN4tbUF24Tf8XxtN9xPUmO4qsFmL/7LAqSVqCnjwMC+r0HE1MWXET0BPSd/OXp6SlOnDjxJPPGHmnBggWiUaNGQqFQiKCgIJGcnKx9rUuXLiI8PFz7s4eHhwDwwBYZGantk52dLYYNGyZcXFyEUqkUzZs3F3PnzhUajUYIcW8Sfs+ePYWjo6MwMzMTbm5u4s033xSnTp3SydW1a1dhY2MjlEqlCA4OFtu3b9d5PTw8/KFZunTpUuVzr+pkOiJ6NjQajfglI0v0jI4VedNchIi0Fpn/9hWH98QIjVotdTwiqiGq+v2t9xyt1atXY+vWrVi5ciUsLCyqs+arkzhHi6hmKisrQfrGOWh55lvYoAgAcMy8LVS9v0STNtUzok9EtVe1PuuwXbt2OnOxzp07ByEEPD09YWZmptP30KFDTxG77mGhRVSzFeTdwMn1kfDPjoFCVgmNkCHNNgzub0TD2a2x1PGISCLVOhn+r3cEEhHVJTb1HdB+9GJcyxyPa5s+RuCdePjn/4b+S15Ep45dMOaFJrBSmv39gYioTtL70iFVL45oEdUuZw4lIjnhF0y70RUAUL+eAlH+Bej2Uh+YKcwlTkdEz0q1PoLnrxo3boxbt2490J6fn4/GjTmMTkTGzdu/C4ZEzMbSoYFo4lAPtsUX8WLqO8iJaov031ZDPGYZGSKqe/QutC5evAi1Wv1Ae1lZGa5evVotoYiIajKZTIaXfJywc0JnTA2xQKHMEu7iGtrtH4vTUc/jZMpOqSMSUQ1R5YVhYmNjtf/euXMnbGxstD+r1WrEx8fDy8uretMREdVgpiZyvNhnMO526Ymk9Z+j7ZXVaFFxAtgxAIf3tofNy9Ph1SpY6phEJKEqz9G6v9K6TCbD/+5iZmYGT09PzJ07Fy+//HL1pzRinKNFZDxuXLuICxunIeDmLzCVaZAv6iGq5Sa8170N3Oy4HA6RManW5R3+ysvLCwcOHIC9vf1ThyQWWkTG6MrZDORunYbdt53xrbovFCZyDA52x3shDqhv7yR1PCKqBgZ71mFmZuZTBSMiMnbuzfzg/uFWmF25jSM7T2PfuVu4kBwLRdo3SPIYijZvfIx6VrZSxySiZ6BKI1rffPMN3n33XSiVSnzzzTeP7Tt+/PhqC1cXcESLyPj9cfYGyje8ixfL9gAAbsEG51qMQbtXPoDCXClxOiJ6EtV66dDLywsHDx5EgwYNHjvhXSaT4cKFC0+WuI5ioUVUN2jUaqTHLYfTwTlwE9kAgCyZE7L9/wX/XiMh/+/D6omodjDYHC2qXiy0iOqWivIyHNryNZqcWAR75AMAEs06ofLV79GthaPO486IqOYy2IKlRET05MwU5gge8BEsPjyCJM8xuCNUWFsUgBErD+LVb/dj39lcqSMSUTWq0ohWRERElQ84b968pwpU13BEi6huy795Hd+m3sTKpMsordBghMl2vFrvKExCP0WLoJekjkdEj1Ctlw67du1apTeVyWTYs2dP1VMSCy0iAgDkFpZiScJpvHOoPxrK8gAAGaog1OsRiaZ+HSVOR0T/i3O0agkWWkT0VzmXz+Ly5s/gn7cdprJ7z008VK8zGrz8GTxaBkicjoju4xwtIqJayLlRMwS9/xOyh/yOg9ah0AgZ/It+h/u6F7Hp209w6VaR1BGJSA8stIiIaiD3pq0RGLERlwbuwqF6nSAAfHfZFS/OTcSUTUdxLb9E6ohEVAV6rwxPRETPjpfPc/Dy2YZTJ4/CKakEp87cwNrUy2iRPgMtGlqhyauRsHd2lzomET0C52hJjHO0iEgfBy7m4Yftf+Kb68NgJlOjWJgjw3UgfF77BDYN+BxFomeFk+FrCRZaRKQvodHg2J+/wPz3GfCuPAMAuCNUONZoMHxenQIbO3uJExIZv2ottGJjY6v8xn379q1yX2KhRURPTmg0yNgTA+v90WisuQgAKIQFfvOdg7CXB8BaaSZtQCIjVq2FllxetTnzMpkMarW66imJhRYRPTWNWo3Dv62C3YGvYK/ORceyryGUthjRsTGGd/RkwUVkALx0WEuw0CKi6qJRq/FH0n58cUCDM9fvAhD4QTkfFh4BaPXqJFjZ1Jc6IpHRYKFVS7DQIqLqptEI/Ho0G3t2bsZXxR8DAApQDyc8h6L1qx/B0poFF9HTMmihVVRUhMTERFy+fBnl5eU6r40fP17/tHUYCy0iMhR1ZSUOxy2H/aH58NBcBQDkwxKnPMPh++pEWFrbSZyQqPYyWKGVnp6OXr16obi4GEVFRahfvz5u3rwJCwsLODo64sKFC08dvi5hoUVEhqaurET6ju/hmP41GmmyAAC3YYW4575H39AXUc+cSyoS6ctgj+D54IMP0KdPH9y+fRsqlQrJycm4dOkSAgICMGfOnKcKTURE1c/E1BSBfUbB9eMjOOgfjSsyF9zW1MMnf5Sh06wELEk8j+KyCqljEhklvUe0bG1tkZKSgubNm8PW1hZJSUlo2bIlUlJSEB4ejlOnThkqq1HiiBYRPWuVFeXYnXwI0ckluHirGApUYKvy3yho2het+32Aela2UkckqvEMNqJlZmamXe7B0dERly9fBgDY2NjgypUrTxiXiIieFVMzBXp0ao/dEV0w9w0/jLA+gJa4gPbn5qN8ri+SV3yMOwV5UsckMgp6X5hv164dDhw4gGbNmqFLly6YNm0abt68iR9//BG+vr6GyEhERAZgaiLHawFuqGwTidRtznA5sghuIhvtLy5C4VcrkOz+Jlq+Mgk29R2kjkpUa+l96fDgwYO4c+cOunbtitzcXAwdOhT79+9Hs2bN8P3336Nt27YGimqceOmQiGqKyopyHN7xAxwOL4SH5t4VijtChVWBGzGo23OoX08hcUKimoPraNUSLLSIqKZRV1Yi47dVsDv4NTIrbPF2xUewUJjgrfYeGNneCY71uQ4XkcHmaHXr1g35+fkPfcNu3brpezgiIqphTExN4d/rbXhMPQS8+h/4ulqjuFyNrb8fhPnXvkhZNAK5V7mUD1FV6D2iJZfLkZOTA0dHR5323NxcuLq6oqKCtwjrgyNaRFTTCSGw9/QNXNw2E8PvLgMAlAtTpNu/jEZ9P0ZDj+YSJyR69qr6/V3lyfBHjhzR/vvEiRPIycnR/qxWqxEXFwdXV9cnjEtERDWVTCZD1xaOEN6zcfTPzjD5czZ8yo8i+NYWVPzwC1LtusPl5alwa8obooj+V5UvHbZt2xbt2rWDTCZDt27d0LZtW+0WEBCAGTNmYNq0aXoHWLRoETw9PaFUKhEcHIzU1NRH9l26dCk6deoEOzs72NnZITQ09IH+d+/exbhx4+Dm5gaVSgUfHx8sWbJEp8+oUaPQpEkTqFQqODg4oF+/fg+s/xUfH4+QkBBYWVnB2dkZkyZNQmVlpfb10tJSDBs2DK1bt4apqSn69++v97kTEdUmMrkcrTv3g8/Hf+JE93U4at4OZjI1gvK3w+bHUExam4RzuXeljklUo1S50MrMzMT58+chhEBqaioyMzO1W1ZWFgoLC/H222/r9eYxMTGIiIhAZGQkDh06BD8/P3Tv3h25ubkP7b93714MGjQICQkJSEpKgru7O8LCwpCVlaXtExERgbi4OKxevRonT57EhAkTMG7cOMTGxmr7BAQEYPny5Th58iR27twJIQTCwsKgVqsBABkZGejVqxd69OiB9PR0xMTEIDY2FpMnT9YeQ61WQ6VSYfz48QgNDdXrvImIajufDj3RespenOq9ERmqIKxTd0VMRh5e+ioR//wpDWeOp0kdkahGkPSuw+DgYDz33HNYuHAhAECj0cDd3R3vvfeeTlHzKGq1GnZ2dli4cCGGDh0KAPD19cXAgQPx6aefavsFBASgZ8+emDFjxkOPc+TIEfj5+eHcuXNo0qQJPv74Y+zatQsHDhzQ9vnll18wYMAA5ObmwsrKSmf/YcOGIT8/H1u2bNH3V8A5WkRkFI5cvoUFezOx68R1BMhOY6P5v3FEGQjTLh+iZXB3yOR633tFVKMZ7K5DADh//jzee+89hIaGIjQ0FOPHj8f58+f1OkZ5eTnS0tJ0RoPkcjlCQ0ORlJRUpWMUFxejoqIC9f9yq3FISAhiY2ORlZUFIQQSEhJw5swZhIWFPfQYRUVFWL58Oby8vODu7g4AKCsrg1Kp1OmnUqlQWlqKtLSn+7+0srIyFBYW6mxERLVdm0YNsHRoIHZO6Iy33G+iUsjRpvQgfHb+A6ejnkfGnnUQGo3UMYmeOb0LrZ07d8LHxwepqalo06YN2rRpg5SUFLRq1Qq7du2q8nFu3rwJtVoNJycnnXYnJyedifaPM2nSJLi4uOgUawsWLICPjw/c3NygUCjQo0cPLFq0CJ07d9bZd/HixbC0tISlpSV27NiBXbt2QaG4txhf9+7dsX//fqxduxZqtRpZWVmYPn06ACA7O7vK5/gwUVFRsLGx0W73izsiImPQ3NkKr/zzS1wP34+UBv1RJszQouIE/H4fhcwZ7XDw16VQ/2W+K5Gx07vQmjx5Mj744AOkpKRg3rx5mDdvHlJSUjBhwgRMmjTJEBkfKjo6GuvWrcPmzZt1Rp8WLFiA5ORkxMbGIi0tDXPnzsXYsWOxe/dunf0HDx6M9PR0JCYmwtvbGwMGDEBpaSkAICwsDLNnz8bo0aNhbm4Ob29v9OrVCwC0z3l8UlOmTEFBQYF24/MhicgYuTZuieD3VuLOqDQkOw9GkVCiseYiHFNmImzeXqxLvYyySrXUMYkMTu85WkqlEkePHkWzZs102s+cOYM2bdpoi5W/U15eDgsLC2zYsEHnjr3w8HDk5+dj69atj9x3zpw5mDFjBnbv3o3AwEBte0lJCWxsbLB582b07t1b2z5y5EhcvXoVcXFxj8xiZ2eHZcuWYdCgQdp2IQSys7NhZ2eHixcvakfynnvuOZ39OUeLiOjxCvJycWLLXGy7YoqfSkIAAK5WcsxqnIG2L49BPSsbiRMS6cdgc7QcHBxw+PDhB9oPHz78wCKmj6NQKBAQEID4+Hhtm0ajQXx8PDp06PDI/WbNmoXPP/8ccXFxOkUWAFRUVKCiouKBUScTExNoHjM3QAgBIQTKysp02mUyGVxcXKBSqbB27Vq4u7vD39+/yudIRET32NR3RIe3Z+Ljyf/GJ71bwtlaiZDiPXj+dBTK57ZC0g8foeDWdaljElW7Ki9YOn36dHz44Yd455138O677+LChQsICbn3fyX79u3DzJkzERERodebR0REIDw8HIGBgQgKCsL8+fNRVFSE4cOHAwCGDh0KV1dXREVFAQBmzpyJadOmYc2aNfD09NTO5bo/18ra2hpdunTBxIkToVKp4OHhgcTERKxatQrz5s0DAFy4cAExMTEICwuDg4MDrl69iujoaKhUKu3lQQCYPXs2evToAblcjk2bNiE6Ohrr16+HiYmJts+JEydQXl6OvLw83LlzR1uA8sHaREQPV8/cFCM7NcaQDh44sP0SrqY3hJvIRofL/0HRNyuR3PA1NO07CfYuHlJHJaoeoorkcrm4fv260Gg0Yt68ecLV1VXIZDIhk8mEq6urmD9/vtBoNFU9nNaCBQtEo0aNhEKhEEFBQSI5OVn7WpcuXUR4eLj2Zw8PDwHggS0yMlLbJzs7WwwbNky4uLgIpVIpmjdvLubOnavNlpWVJXr27CkcHR2FmZmZcHNzE2+++aY4deqUTq6uXbsKGxsboVQqRXBwsNi+ffsD2R+VRx8FBQUCgCgoKNBrPyIiY1BZUSEOblsqzv+7jRCR1kJEWouyafVFyteDxfmc21LHI3qkqn5/V3mO1sOecXjnzh0AeGBdKao6ztEiIgKERoMje9fDPOlrtKg4gT/Uvhha+TG6+zhjVJfGaNfITuqIRDqq+v2tV6F1/fp1ODg4VFtIYqFFRPS/TqX8hp8zbuD7C7YAAAfk43vrpRDtR6N1lwGQm3DxU5KeQQotGxsbyGSyx/bLy8vTL2kdx0KLiOjhzl6/g+9+v4DGR+ZijMm9O9Ez5R642WYU/HqOgMJc+TdHIDIcgxRa8+fPh43N42/BDQ8P1y9pHcdCi4jo8XKzMnH+l9lonb0JlrISAMB1NEBms2Hw7fMeLK15WZGePYMUWv87R4ueHgstIqKqKbh9Eyd++RrNLqyCPfIBANdhh9XBv2BoR284WJlLG5DqlGovtExMTJCdnc1Cq5qx0CIi0k9pSRGObP8ODY99h4SKVphWORwKUzle83fDmHbmaOTlLXVEqgOqfcHSKtZjREREBqVU1UPQax/A9ZNjaPj6TLR1t0V5pQYnD+yB24ogHJrdB6cPJUodkwjAEzyCh6oXR7SIiJ6OEAIHLt7G5a0z8Hr+99r244o2qOwwHm26vAbZUz6nluh/VfulQzIMFlpERNUn88QB3Nw5B23zd8FMdu+h1ZlyT9xs8w78er0LhUIhcUIyFiy0agkWWkRE1S/nyjlc3DYHrXM2o56sFFeFPd4wW4y3nm+CwcGNYGvBgoueDgutWoKFFhGR4RTk3cCJX+Zj12WBH4qeBwBYmQksdtmBxt3/CdfGrSROSLUVC61agoUWEZHhlVdq8EvGNSz94wKa5+7A14rF0AgZDlt2hEWX8Wjx3EvA3yzITfRXLLRqCRZaRETPjhACR5J3A4kz4Vd6QNt+2rQ57vqPgd9Lg2FqxsuK9PdYaNUSLLSIiKRx8eRB5P42D23zdkIhqwQAXJM5YU/n9egf4gtLc1OJE1JNxkKrlmChRUQkrZs5V3Bu21dofnU9zmhcMbB8GqyUpngzuBGGB9rD2cFB6ohUA7HQqiVYaBER1QwlRXcQl3IE3xyqQObNItRHIX43/wAnbTrCLjQCTduESB2RahAWWrUECy0ioppFoxGIP5WLczu/xZiCr7TtxxRtoW7/T7Tu8jrkJiYSJqSagIVWLcFCi4io5jqb/jsK9sxH28IEmMo0AIBLcnfktBwOv96joLSwlDghSYWFVi3BQouIqObLuXwWF3+dC9+cLbCUlaBMmKKnfAl6BLfG0A6ecLZRSh2RnjEWWrUECy0iotrjTkEejm9bgNOZlxF591UAgKlchq8b7kKzkL7wDugqcUJ6Vlho1RIstIiIah+1RmDXiev4YV8m7l48hO3mHwMATpu2wN1276DNS0NgpjCXOCUZEgutWoKFFhFR7XbmRAYKdn4Jv/zd2vW4rqMBLjQeDJ/e42DTwEnihGQILLRqCRZaRETG4WbOZZz99Rt4X1mPBigAAJQIBZY3W4CwsN5o6mglcUKqTiy0agkWWkRExqW0pBhH4n5Ag2Pfw7IyDx3LvkEFTNHZ2wFj2ioQ7Neay0MYARZatQQLLSIi4yQ0GqQfP4El6aXYdfI6ZEKDRMUH0JiaI7vFMLTp9S4sLG2kjklPiIVWLcFCi4jI+F2+VYwd8bsx+Pi7sJSVAAAKUQ/HG74Cr54T4NyomcQJSV8stGoJFlpERHXHnYI8HN/+LdzOrIKbyAEAVAo5jlh1glm3j+HbLhgymUzilFQVLLRqCRZaRER1j7qyEkf3/gzTA0vgW3YYAPBK2b9R5hyA8BAP9GvrCqUZ53HVZCy0agkWWkREdduFYyk493sMxmeHorTi3lfyVOUG+Da0hGePcWjo0VzihPQwLLRqCRZaREQEAPnF5Yg5cAUb95/A5tIRqCcrg1rIcMTyeZh1GIVWIS9DJpdLHZP+i4VWLcFCi4iI/kpdWYkje9bBLG2p9rIiAFyUN8L1FkPRmncr1ggstGoJFlpERPQol06mIWf3N2h9cwcsZGUAgMV4HbcC/4WhHTzg0aCexAnrLhZatQQLLSIi+jsFt2/i5I5v0fDsOgwsmYQcNIBMBozwzEO/FvXQqmM/LoL6jLHQqiVYaBERUVVp1Boknr2JFfsvIvHMDawwm4kXTDJwWe6Ka82GoFWvUbCyqS91zDqBhVYtwUKLiIiexIXcQlzf8CF8r8fC6r+LoN4VKhxz7A2Xl95DI++20gY0ciy0agkWWkRE9DTuFt7G8R3fwfn0j/DQXNG277TsD1nPmejWwhGmJrxbsbqx0KolWGgREVF1EBoNjv35CyqTl8CvKAlTKkciRt0VLjZKhAfa47XWdrB3biR1TKNR1e9v02eYiYiIiAxEJpejded+QOd+uHbxNByPl6D+oRu4VlCKq3t/gPWfPyLNugtUz49Cy6Awrsn1jNSI3/KiRYvg6ekJpVKJ4OBgpKamPrLv0qVL0alTJ9jZ2cHOzg6hoaEP9L979y7GjRsHNzc3qFQq+Pj4YMmSJTp9Ro0ahSZNmkClUsHBwQH9+vXDqVOndPrEx8cjJCQEVlZWcHZ2xqRJk1BZWanT58iRI+jUqROUSiXc3d0xa9asp/xtEBERPR0Xz+b4V++22D+5G74a6IeuVlehkKkRcGcPfOIG4uKMtkhZPwt3C29LHdXoSV5oxcTEICIiApGRkTh06BD8/PzQvXt35ObmPrT/3r17MWjQICQkJCApKQnu7u4ICwtDVlaWtk9ERATi4uKwevVqnDx5EhMmTMC4ceMQGxur7RMQEIDly5fj5MmT2LlzJ4QQCAsLg1qtBgBkZGSgV69e6NGjB9LT0xETE4PY2FhMnjxZe4zCwkKEhYXBw8MDaWlpmD17Nj777DN89913BvptERERVZ3SzASvtHNDt8kbce6V7Uit3wfFwhxemksIPvEFZHNbIHnRCJzJKZQ6qvESEgsKChJjx47V/qxWq4WLi4uIioqq0v6VlZXCyspKrFy5UtvWqlUrMX36dJ1+/v7+YurUqY88TkZGhgAgzp07J4QQYsqUKSIwMFCnT2xsrFAqlaKwsFAIIcTixYuFnZ2dKCsr0/aZNGmSaN68eZWyCyFEQUGBACAKCgqqvA8REdGTys+7IZLXfC4uf9ZCiEhrEfdJN+ExaZsYsGS/+CUjS5SVlUsdsVao6ve3pCNa5eXlSEtLQ2hoqLZNLpcjNDQUSUlJVTpGcXExKioqUL/+/68bEhISgtjYWGRlZUEIgYSEBJw5cwZhYWEPPUZRURGWL18OLy8vuLu7AwDKysqgVCp1+qlUKpSWliItLQ0AkJSUhM6dO0OhUGj7dO/eHadPn8bt2w8fji0rK0NhYaHORkRE9KzY2NkjeNAncPv0OI69uAoHPUbCRC5DSmYeZq2NQ/6X3kheFoHrV89LHdUoSFpo3bx5E2q1Gk5OTjrtTk5OyMnJqdIxJk2aBBcXF51ibcGCBfDx8YGbmxsUCgV69OiBRYsWoXPnzjr7Ll68GJaWlrC0tMSOHTuwa9cubdHUvXt37N+/H2vXroVarUZWVhamT58OAMjOzgYA5OTkPDT7/dceJioqCjY2NtrtfmFHRET0LMnkcvh26oepIwfhz0ldMf7FZhiq2gdH5KH91e/RYGkgDs1+Gcf+2Aqh0Ugdt9aSfI7W04iOjsa6deuwefNmndGnBQsWIDk5GbGxsUhLS8PcuXMxduxY7N69W2f/wYMHIz09HYmJifD29saAAQNQWloKAAgLC8Ps2bMxevRomJubw9vbG7169QJwb9TtSU2ZMgUFBQXa7cqVK3+/ExERkQE1tFEh4iVvDJ20CGlB83Bc0RqmMg38i/6Ab/xQXJnhi+Q1M1BQWCB11FpH0nW0ysvLYWFhgQ0bNqB///7a9vDwcOTn52Pr1q2P3HfOnDmYMWMGdu/ejcDAQG17SUkJbGxssHnzZvTu3VvbPnLkSFy9ehVxcXGPzGJnZ4dly5Zh0KBB2nYhBLKzs2FnZ4eLFy/Cx8cHqampeO655zB06FAUFhZiy5Yt2v4JCQno1q0b8vLyYGdn97e/A66jRURENdHFkwdxPX4RfG9sRz1ZKQqFBV7QfIuwto3xVnsP+LraSB1RUlX9/pZ0REuhUCAgIADx8fHaNo1Gg/j4eHTo0OGR+82aNQuff/454uLidIosAKioqEBFRcUDo04mJibQPGboUwgBIQTKysp02mUyGVxcXKBSqbB27Vq4u7vD398fANChQwf8/vvvqKio0PbftWsXmjdvXqUii4iIqKbybBmI4HHLIf51Cik+U7FWNQh5FWZYd+AKXl7wBxKj+iF18zcoKbojddSazeDT8v/GunXrhLm5uVixYoU4ceKEePfdd4Wtra3IyckRQggxZMgQMXnyZG3/6OhooVAoxIYNG0R2drZ2u3PnjrZPly5dRKtWrURCQoK4cOGCWL58uVAqlWLx4sVCCCHOnz8vvvzyS3Hw4EFx6dIlsW/fPtGnTx9Rv359cf36de1xZs2aJY4cOSKOHTsmpk+fLszMzMTmzZu1r+fn5wsnJycxZMgQcezYMbFu3TphYWEh/vOf/1T5/HnXIRER1QYajUakXLglxq05JAZNnSNEpLUQkdaiINJZJC18W1w4fkDqiM9UVb+/JS+0hBBiwYIFolGjRkKhUIigoCCRnJysfa1Lly4iPDxc+7OHh4cA8MAWGRmp7ZOdnS2GDRsmXFxchFKpFM2bNxdz584VGo1GCCFEVlaW6Nmzp3B0dBRmZmbCzc1NvPnmm+LUqVM6ubp27SpsbGyEUqkUwcHBYvv27Q9kz8jIEB07dhTm5ubC1dVVREdH63XuLLSIiKi2uZGTJfavnCqyPmumLbhEpLU4MaO9OLB1sSgpvit1RIOr6vc3n3UoMc7RIiKi2kqjVuP4n1tRkfoD2tzdB1PZvSk6o2Sfwj2gFwYFN0ITB0uJUxoGHypdS7DQIiIiY3Dj2kWc2/ktzC7vwxslkyD+Ow18ilMKAr0boXXoW1CYK//mKLUHC61agoUWEREZE7VGIPFMLtakXMafp7KwXzEW9WV3kQdrnG7YH41eGg3Xxq2kjvnUWGjVEiy0iIjIWGXn3sDFX2aiyZWNcESetv2ouT8q/YfBt+s/YKYwlzDhk2OhVUuw0CIiImNXWVGOIwnrYXpoBXxLDkIuu1d6LJe/ilvBk/GPIHe42VlInFI/LLRqCRZaRERUl1zLPIVLu75Fs2tbMLBsKs4LV8hkwBDPArzaGPB94XWYmin+/kASY6FVS7DQIiKiuqi8vBy7Tt3CmtRL2HfuFhaYfYM+Jsm4jga40Og1eL00Cs7uTaWO+UgstGoJFlpERFTXZd4swrVNH8Pn2kbY4d5K82ohwzGLIGjaDYXvC2/UuLlcLLRqCRZaRERE95SVFuPo7tVQHfkRrcqPaNvTZK0QH/wD/vFcIzRqUDPmcrHQqiVYaBERET3oytkjyNqzBN7Zv+DbipexVP0yAKBrEyuMcz0L326DYK6UruhioVVLsNAiIiJ6tPKyUiScuIaf0m/ij7M30F/2B75SfIvbsMZpp95o2G00PJq3fea5WGjVEiy0iIiIquZKXjGOb/8W/ucW6qzLddKsFYp8B8P3pXAoLZ7NI39YaNUSLLSIiIj0U1lRjmOJG4FDK9G6KBkm/12XqwD1sLj1BvQP8UXLhob9TmWhVUuw0CIiInpyuVmZOP/bEnhc3oSLlfZ4s+ITAICfuy0meF5BUOeeqGdlU+3vW9Xvb3m1vzMRERHRM+Lo6oUOw2fC+ZNTkL2xHL1aO8PMRIasK5fwfOo/MWD2RpzLvStZPlPJ3pmIiIiomshNTNChTQt0aAPcvFuGP/bsQPyRLihUeKKxfT3JcvHSocR46ZCIiMgwhBDIKSxFQxtVtR+blw6JiIioTpPJZAYpsvTBQouIiIjIQFhoERERERkICy0iIiIiA2GhRURERGQgLLSIiIiIDISFFhEREZGBsNAiIiIiMhAWWkREREQGwkKLiIiIyEBYaBEREREZCAstIiIiIgNhoUVERERkICy0iIiIiAzEVOoAdZ0QAgBQWFgocRIiIiKqqvvf2/e/xx+FhZbE7ty5AwBwd3eXOAkRERHp686dO7CxsXnk6zLxd6UYGZRGo8G1a9dgZWUFmUxWbcctLCyEu7s7rly5Amtr62o7bk1i7Odo7OcHGP85Gvv5ATxHY2Ds5wcY5hyFELhz5w5cXFwglz96JhZHtCQml8vh5uZmsONbW1sb7X849xn7ORr7+QHGf47Gfn4Az9EYGPv5AdV/jo8bybqPk+GJiIiIDISFFhEREZGBsNAyUubm5oiMjIS5ubnUUQzG2M/R2M8PMP5zNPbzA3iOxsDYzw+Q9hw5GZ6IiIjIQDiiRURERGQgLLSIiIiIDISFFhEREZGBsNAiIiIiMhAWWkZq0aJF8PT0hFKpRHBwMFJTU6WO9FC///47+vTpAxcXF8hkMmzZskXndSEEpk2bhoYNG0KlUiE0NBRnz57V6ZOXl4fBgwfD2toatra2GDFiBO7evavT58iRI+jUqROUSiXc3d0xa9YsQ58aACAqKgrPPfccrKys4OjoiP79++P06dM6fUpLSzF27Fg0aNAAlpaWeO2113D9+nWdPpcvX0bv3r1hYWEBR0dHTJw4EZWVlTp99u7dC39/f5ibm6Np06ZYsWKFoU8P3377Ldq0aaNdBLBDhw7YsWOHUZzbo0RHR0Mmk2HChAnattp+np999hlkMpnO1qJFC+3rtf38ACArKwtvvfUWGjRoAJVKhdatW+PgwYPa12v73xpPT88HPkOZTIaxY8cCqP2foVqtxqeffgovLy+oVCo0adIEn3/+uc5zBmvsZyjI6Kxbt04oFArxww8/iOPHj4t33nlH2NraiuvXr0sd7QHbt28XU6dOFZs2bRIAxObNm3Vej46OFjY2NmLLli0iIyND9O3bV3h5eYmSkhJtnx49egg/Pz+RnJws/vjjD9G0aVMxaNAg7esFBQXCyclJDB48WBw7dkysXbtWqFQq8Z///Mfg59e9e3exfPlycezYMXH48GHRq1cv0ahRI3H37l1tn9GjRwt3d3cRHx8vDh48KNq3by9CQkK0r1dWVgpfX18RGhoq0tPTxfbt24W9vb2YMmWKts+FCxeEhYWFiIiIECdOnBALFiwQJiYmIi4uzqDnFxsbK3799Vdx5swZcfr0afHxxx8LMzMzcezYsVp/bg+TmpoqPD09RZs2bcT777+vba/t5xkZGSlatWolsrOztduNGzeM5vzy8vKEh4eHGDZsmEhJSREXLlwQO3fuFOfOndP2qe1/a3Jzc3U+v127dgkAIiEhQQhR+z/DL774QjRo0EBs27ZNZGZmip9//llYWlqKr7/+Wtunpn6GLLSMUFBQkBg7dqz2Z7VaLVxcXERUVJSEqf7e/xZaGo1GODs7i9mzZ2vb8vPzhbm5uVi7dq0QQogTJ04IAOLAgQPaPjt27BAymUxkZWUJIYRYvHixsLOzE2VlZdo+kyZNEs2bNzfwGT0oNzdXABCJiYlCiHvnY2ZmJn7++Wdtn5MnTwoAIikpSQhxrxiVy+UiJydH2+fbb78V1tbW2nP66KOPRKtWrXTea+DAgaJ79+6GPqUH2NnZiWXLlhndud25c0c0a9ZM7Nq1S3Tp0kVbaBnDeUZGRgo/P7+HvmYM5zdp0iTRsWPHR75ujH9r3n//fdGkSROh0WiM4jPs3bu3ePvtt3XaXn31VTF48GAhRM3+DHnp0MiUl5cjLS0NoaGh2ja5XI7Q0FAkJSVJmEx/mZmZyMnJ0TkXGxsbBAcHa88lKSkJtra2CAwM1PYJDQ2FXC5HSkqKtk/nzp2hUCi0fbp3747Tp0/j9u3bz+hs7ikoKAAA1K9fHwCQlpaGiooKnXNs0aIFGjVqpHOOrVu3hpOTk7ZP9+7dUVhYiOPHj2v7/PUY9/s8y89crVZj3bp1KCoqQocOHYzq3ABg7Nix6N279wNZjOU8z549CxcXFzRu3BiDBw/G5cuXARjH+cXGxiIwMBBvvPEGHB0d0a5dOyxdulT7urH9rSkvL8fq1avx9ttvQyaTGcVnGBISgvj4eJw5cwYAkJGRgT///BM9e/YEULM/QxZaRubmzZtQq9U6/7EAgJOTE3JyciRK9WTu533cueTk5MDR0VHndVNTU9SvX1+nz8OO8df3eBY0Gg0mTJiA559/Hr6+vtr3VygUsLW1fSCfPvkf1aewsBAlJSWGOB2to0ePwtLSEubm5hg9ejQ2b94MHx8fozi3+9atW4dDhw4hKirqgdeM4TyDg4OxYsUKxMXF4dtvv0VmZiY6deqEO3fuGMX5XbhwAd9++y2aNWuGnTt3YsyYMRg/fjxWrlypk9FY/tZs2bIF+fn5GDZsmPa9a/tnOHnyZPzjH/9AixYtYGZmhnbt2mHChAkYPHiwTsaa+BmaPtFeRKS3sWPH4tixY/jzzz+ljlKtmjdvjsOHD6OgoAAbNmxAeHg4EhMTpY5Vba5cuYL3338fu3btglKplDqOQdwfFQCANm3aIDg4GB4eHli/fj1UKpWEyaqHRqNBYGAgvvzySwBAu3btcOzYMSxZsgTh4eESp6t+33//PXr27AkXFxepo1Sb9evX46effsKaNWvQqlUrHD58GBMmTICLi0uN/ww5omVk7O3tYWJi8sDdJNevX4ezs7NEqZ7M/byPOxdnZ2fk5ubqvF5ZWYm8vDydPg87xl/fw9DGjRuHbdu2ISEhAW5ubtp2Z2dnlJeXIz8//4F8+uR/VB9ra2uDf1EqFAo0bdoUAQEBiIqKgp+fH77++mujODfg3qWz3Nxc+Pv7w9TUFKampkhMTMQ333wDU1NTODk5GcV5/pWtrS28vb1x7tw5o/gcGzZsCB8fH522li1bai+PGtPfmkuXLmH37t0YOXKkts0YPsOJEydqR7Vat26NIUOG4IMPPtCOMtfkz5CFlpFRKBQICAhAfHy8tk2j0SA+Ph4dOnSQMJn+vLy84OzsrHMuhYWFSElJ0Z5Lhw4dkJ+fj7S0NG2fPXv2QKPRIDg4WNvn999/R0VFhbbPrl270Lx5c9jZ2Rn0HIQQGDduHDZv3ow9e/bAy8tL5/WAgACYmZnpnOPp06dx+fJlnXM8evSozh+IXbt2wdraWvvl0aFDB51j3O8jxWeu0WhQVlZmNOf24osv4ujRozh8+LB2CwwMxODBg7X/Nobz/Ku7d+/i/PnzaNiwoVF8js8///wDy6qcOXMGHh4eAIzjb819y5cvh6OjI3r37q1tM4bPsLi4GHK5bsliYmICjUYDoIZ/hk88jZ5qrHXr1glzc3OxYsUKceLECfHuu+8KW1tbnbtJaoo7d+6I9PR0kZ6eLgCIefPmifT0dHHp0iUhxL3bdW1tbcXWrVvFkSNHRL9+/R56u267du1ESkqK+PPPP0WzZs10btfNz88XTk5OYsiQIeLYsWNi3bp1wsLC4pnccj1mzBhhY2Mj9u7dq3PrdXFxsbbP6NGjRaNGjcSePXvEwYMHRYcOHUSHDh20r9+/7TosLEwcPnxYxMXFCQcHh4fedj1x4kRx8uRJsWjRomdy2/XkyZNFYmKiyMzMFEeOHBGTJ08WMplM/Pbbb7X+3B7nr3cdClH7z/Nf//qX2Lt3r8jMzBT79u0ToaGhwt7eXuTm5hrF+aWmpgpTU1PxxRdfiLNnz4qffvpJWFhYiNWrV2v71Pa/NULcu8O8UaNGYtKkSQ+8Vts/w/DwcOHq6qpd3mHTpk3C3t5efPTRR9o+NfUzZKFlpBYsWCAaNWokFAqFCAoKEsnJyVJHeqiEhAQB4IEtPDxcCHHvlt1PP/1UODk5CXNzc/Hiiy+K06dP6xzj1q1bYtCgQcLS0lJYW1uL4cOHizt37uj0ycjIEB07dhTm5ubC1dVVREdHP5Pze9i5ARDLly/X9ikpKRH//Oc/hZ2dnbCwsBCvvPKKyM7O1jnOxYsXRc+ePYVKpRL29vbiX//6l6ioqNDpk5CQINq2bSsUCoVo3LixznsYyttvvy08PDyEQqEQDg4O4sUXX9QWWbX93B7nfwut2n6eAwcOFA0bNhQKhUK4urqKgQMH6qwxVdvPTwghfvnlF+Hr6yvMzc1FixYtxHfffafzem3/WyOEEDt37hQAHsgtRO3/DAsLC8X7778vGjVqJJRKpWjcuLGYOnWqzjIMNfUzlAnxl2VViYiIiKjacI4WERERkYGw0CIiIiIyEBZaRERERAbCQouIiIjIQFhoERERERkICy0iIiIiA2GhRURERGQgLLSIiIiIDISFFhHRXwwbNgz9+/d/5u+7YsUKyGQyyGQyTJgwoUr7DBs2TLvPli1bDJqPiJ6MqdQBiIieFZlM9tjXIyMj8fXXX0OqB2ZYW1vj9OnTqFevXpX6f/3114iOjkbDhg0NnIyInhQLLSKqM7Kzs7X/jomJwbRp03D69Gltm6WlJSwtLaWIBuBeIejs7Fzl/jY2NrCxsTFgIiJ6Wrx0SER1hrOzs3azsbHRFjb3N0tLywcuHb7wwgt47733MGHCBNjZ2cHJyQlLly5FUVERhg8fDisrKzRt2hQ7duzQea9jx46hZ8+esLS0hJOTE4YMGYKbN2/qnXnx4sVo1qwZlEolnJyc8Prrrz/tr4GIniEWWkREf2PlypWwt7dHamoq3nvvPYwZMwZvvPEGQkJCcOjQIYSFhWHIkCEoLi4GAOTn56Nbt25o164dDh48iLi4OFy/fh0DBgzQ630PHjyI8ePHY/r06Th9+jTi4uLQuXNnQ5wiERkILx0SEf0NPz8/fPLJJwCAKVOmIDo6Gvb29njnnXcAANOmTcO3336LI0eOoH379li4cCHatWuHL7/8UnuMH374Ae7u7jhz5gy8vb2r9L6XL19GvXr18PLLL8PKygoeHh5o165d9Z8gERkMR7SIiP5GmzZttP82MTFBgwYN0Lp1a22bk5MTACA3NxcAkJGRgYSEBO2cL0tLS7Ro0QIAcP78+Sq/70svvQQPDw80btwYQ4YMwU8//aQdNSOi2oGFFhHR3zAzM9P5WSaT6bTdv5tRo9EAAO7evYs+ffrg8OHDOtvZs2f1uvRnZWWFQ4cOYe3atWjYsCGmTZsGPz8/5OfnP/1JEdEzwUuHRETVzN/fHxs3boSnpydMTZ/uz6ypqSlCQ0MRGhqKyMhI2NraYs+ePXj11VerKS0RGRJHtIiIqtnYsWORl5eHQYMG4cCBAzh//jx27tyJ4cOHQ61WV/k427ZtwzfffIPDhw/j0qVLWLVqFTQaDZo3b27A9ERUnVhoERFVMxcXF+zbtw9qtRphYWFo3bo1JkyYAFtbW8jlVf+za2tri02bNqFbt25o2bIllixZgrVr16JVq1YGTE9E1UkmpFoCmYiItFasWIEJEyY80fwrmUyGzZs3S/LoICJ6PI5oERHVEAUFBbC0tMSkSZOq1H/06NGSrmRPRH+PI1pERDXAnTt3cP36dQD3Lhna29v/7T65ubkoLCwEADRs2LDKz0gkomeHhRYRERGRgfDSIREREZGBsNAiIiIiMhAWWkREREQGwkKLiIiIyEBYaBEREREZCAstIiIiIgNhoUVERERkICy0iIiIiAzk/wAMTS/eraYgxQAAAABJRU5ErkJggg==", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -228,14 +221,15 @@ "text": [ "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", - "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[3] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[6] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[7] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[8] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", + "[9] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[10] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[11] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -261,7 +255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.3" }, "vscode": { "interpreter": { From 062f5bc67202a1e468c1caa6deca1a04c779f9c7 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 5 Jun 2024 16:50:26 +0100 Subject: [PATCH 2/8] Updated Ecker2015 diffusivity to better match the original paper --- pybamm/CITATIONS.bib | 8 +++++ .../input/parameters/lithium_ion/Ecker2015.py | 18 +++++++++-- .../Ecker2015_graphite_halfcell.py | 32 ++++++++++++------- 3 files changed, 45 insertions(+), 13 deletions(-) diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index 00d41fadd3..c8b8c278df 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -783,3 +783,11 @@ @article{Wycisk2022 author = {Dominik Wycisk and Marc Oldenburger and Marc Gerry Stoye and Toni Mrkonjic and Arnulf Latz}, keywords = {Lithium-ion battery, Voltage hysteresis, Plett-model, Silicon–graphite anode}, } +@article{Yuan2023, + title = {A electrochemical-electro-thermal coupled computational framework to simulate the performance of Li-ion batteries at cell-level: Analysis on the thermal effects}, + journal = {arXiv preprint}, + year = {2023}, + doi = {https://doi.org/10.48550/arXiv.2303.09838}, + url = {https://arxiv.org/abs/2303.09838}, + author = {Han Yuan and Shen Li and Tao Zhu and Simon O'Kane and Carlos Garcia and Gregory Offer and Monica Marinescu}, +} diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index 32cc631293..afc2bfb12c 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -31,7 +31,15 @@ def graphite_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - D_ref = 8.4e-13 * np.exp(-11.3 * sto) + 8.2e-15 + log_D_ref = ( + -15.06 + + 2.722 * np.exp(-((sto / 0.0777) ** 2)) + + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) + + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) + + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) + + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) + ) + D_ref = 10**log_D_ref E_D_s = 3.03e4 arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( E_D_s / (pybamm.constants.R * 296) @@ -179,7 +187,12 @@ def nco_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - D_ref = 3.7e-13 - 3.4e-13 * np.exp(-12 * (sto - 0.62) * (sto - 0.62)) + log_D_ref = ( + -12.67 + - 1.597 * np.exp(-(((sto - 0.6169) / 0.07859) ** 2)) + - 1.608 * np.exp(-(((sto - 1) / 0.0931) ** 2)) + ) + D_ref = 10**log_D_ref E_D_s = 8.06e4 arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( E_D_s / (pybamm.constants.R * 296.15) @@ -634,5 +647,6 @@ def get_parameter_values(): "Hales2019", "Richardson2020", "OKane2020", + "Yuan2023", ], } diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index 365bb6386c..02c5b21f79 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -1,4 +1,5 @@ import pybamm +import numpy as np def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T): @@ -61,9 +62,17 @@ def graphite_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - D_ref = 8.4e-13 * pybamm.exp(-11.3 * sto) + 8.2e-15 + log_D_ref = ( + -15.06 + + 2.722 * np.exp(-((sto / 0.0777) ** 2)) + + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) + + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) + + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) + + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) + ) + D_ref = 10**log_D_ref E_D_s = 3.03e4 - arrhenius = pybamm.exp(-E_D_s / (pybamm.constants.R * T)) * pybamm.exp( + arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( E_D_s / (pybamm.constants.R * 296) ) @@ -119,12 +128,12 @@ def graphite_ocp_Ecker2015(sto): t = 0.196176 u_eq = ( - a * pybamm.exp(-b * sto) - + c * pybamm.exp(-d * (sto - e)) - - r * pybamm.tanh(s * (sto - t)) - - g * pybamm.tanh(h * (sto - i)) - - j * pybamm.tanh(k * (sto - m)) - - n * pybamm.exp(o * (sto - p)) + a * np.exp(-b * sto) + + c * np.exp(-d * (sto - e)) + - r * np.tanh(s * (sto - t)) + - g * np.tanh(h * (sto - i)) + - j * np.tanh(k * (sto - m)) + - n * np.exp(o * (sto - p)) + q ) @@ -173,7 +182,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations E_r = 53400 - arrhenius = pybamm.exp(-E_r / (pybamm.constants.R * T)) * pybamm.exp( + arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( E_r / (pybamm.constants.R * 296.15) ) @@ -351,8 +360,8 @@ def electrolyte_conductivity_Ecker2015(c_e, T): # add temperature dependence E_k_e = 1.71e4 - C = 296 * pybamm.exp(E_k_e / (pybamm.constants.R * 296)) - sigma_e = C * sigma_e_296 * pybamm.exp(-E_k_e / (pybamm.constants.R * T)) / T + C = 296 * np.exp(E_k_e / (pybamm.constants.R * 296)) + sigma_e = C * sigma_e_296 * np.exp(-E_k_e / (pybamm.constants.R * T)) / T return sigma_e @@ -538,5 +547,6 @@ def get_parameter_values(): "Xu2019", "Richardson2020", "OKane2020", + "Yuan2023", ], } From 116b8daca33da94e80b5e65ee0e873aacfafd144 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 5 Jun 2024 17:48:03 +0100 Subject: [PATCH 3/8] changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 717ed74085..f93005b629 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,7 @@ ## Features +- Updated solid-phase diffusivity functions in `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets to better match the original papers ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) - Added `pybamm.DataLoader` class to fetch data files from [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0) and store it under local cache. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) @@ -46,6 +47,7 @@ ## Breaking changes +- Updated solid-phase diffusivity functions in `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets to better match the original papers ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) - Removed data files under `pybamm/input` and released them in a separate repository upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0). Note that data files under `pybamm/input/parameters` have not been removed. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Removed `check_model` argument from `Simulation.solve`. To change the `check_model` option, use `Simulation(..., discretisation_kwargs={"check_model": False})`. ([#4020](https://github.com/pybamm-team/PyBaMM/pull/4020)) - Removed multiple Docker images. Here on, a single Docker image tagged `pybamm/pybamm:latest` will be provided with both solvers (`IDAKLU` and `JAX`) pre-installed. ([#3992](https://github.com/pybamm-team/PyBaMM/pull/3992)) From 4a97f12f9786efcfd318385b399f4271264031fd Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 7 Jun 2024 17:54:49 +0100 Subject: [PATCH 4/8] Moved new parameters into separate Yuan2023 file --- CHANGELOG.md | 3 +- .../input/parameters/lithium_ion/Ecker2015.py | 18 +- .../Ecker2015_graphite_halfcell.py | 32 +- .../input/parameters/lithium_ion/Yuan2023.py | 652 ++++++++++++++++++ 4 files changed, 666 insertions(+), 39 deletions(-) create mode 100644 pybamm/input/parameters/lithium_ion/Yuan2023.py diff --git a/CHANGELOG.md b/CHANGELOG.md index b9bc8cedb2..f2f7c09249 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,7 @@ ## Features -- Updated solid-phase diffusivity functions in `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets to better match the original papers ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) +- Added new parameter set `Yuan2023` ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) - Added `pybamm.DataLoader` class to fetch data files from [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0) and store it under local cache. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) @@ -48,7 +48,6 @@ ## Breaking changes -- Updated solid-phase diffusivity functions in `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets to better match the original papers ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) - Removed data files under `pybamm/input` and released them in a separate repository upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0). Note that data files under `pybamm/input/parameters` have not been removed. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Removed `check_model` argument from `Simulation.solve`. To change the `check_model` option, use `Simulation(..., discretisation_kwargs={"check_model": False})`. ([#4020](https://github.com/pybamm-team/PyBaMM/pull/4020)) - Removed multiple Docker images. Here on, a single Docker image tagged `pybamm/pybamm:latest` will be provided with both solvers (`IDAKLU` and `JAX`) pre-installed. ([#3992](https://github.com/pybamm-team/PyBaMM/pull/3992)) diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index afc2bfb12c..32cc631293 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -31,15 +31,7 @@ def graphite_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - log_D_ref = ( - -15.06 - + 2.722 * np.exp(-((sto / 0.0777) ** 2)) - + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) - + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) - + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) - + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) - ) - D_ref = 10**log_D_ref + D_ref = 8.4e-13 * np.exp(-11.3 * sto) + 8.2e-15 E_D_s = 3.03e4 arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( E_D_s / (pybamm.constants.R * 296) @@ -187,12 +179,7 @@ def nco_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - log_D_ref = ( - -12.67 - - 1.597 * np.exp(-(((sto - 0.6169) / 0.07859) ** 2)) - - 1.608 * np.exp(-(((sto - 1) / 0.0931) ** 2)) - ) - D_ref = 10**log_D_ref + D_ref = 3.7e-13 - 3.4e-13 * np.exp(-12 * (sto - 0.62) * (sto - 0.62)) E_D_s = 8.06e4 arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( E_D_s / (pybamm.constants.R * 296.15) @@ -647,6 +634,5 @@ def get_parameter_values(): "Hales2019", "Richardson2020", "OKane2020", - "Yuan2023", ], } diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index 02c5b21f79..365bb6386c 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -1,5 +1,4 @@ import pybamm -import numpy as np def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T): @@ -62,17 +61,9 @@ def graphite_diffusivity_Ecker2015(sto, T): Solid diffusivity """ - log_D_ref = ( - -15.06 - + 2.722 * np.exp(-((sto / 0.0777) ** 2)) - + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) - + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) - + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) - + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) - ) - D_ref = 10**log_D_ref + D_ref = 8.4e-13 * pybamm.exp(-11.3 * sto) + 8.2e-15 E_D_s = 3.03e4 - arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( + arrhenius = pybamm.exp(-E_D_s / (pybamm.constants.R * T)) * pybamm.exp( E_D_s / (pybamm.constants.R * 296) ) @@ -128,12 +119,12 @@ def graphite_ocp_Ecker2015(sto): t = 0.196176 u_eq = ( - a * np.exp(-b * sto) - + c * np.exp(-d * (sto - e)) - - r * np.tanh(s * (sto - t)) - - g * np.tanh(h * (sto - i)) - - j * np.tanh(k * (sto - m)) - - n * np.exp(o * (sto - p)) + a * pybamm.exp(-b * sto) + + c * pybamm.exp(-d * (sto - e)) + - r * pybamm.tanh(s * (sto - t)) + - g * pybamm.tanh(h * (sto - i)) + - j * pybamm.tanh(k * (sto - m)) + - n * pybamm.exp(o * (sto - p)) + q ) @@ -182,7 +173,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations E_r = 53400 - arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( + arrhenius = pybamm.exp(-E_r / (pybamm.constants.R * T)) * pybamm.exp( E_r / (pybamm.constants.R * 296.15) ) @@ -360,8 +351,8 @@ def electrolyte_conductivity_Ecker2015(c_e, T): # add temperature dependence E_k_e = 1.71e4 - C = 296 * np.exp(E_k_e / (pybamm.constants.R * 296)) - sigma_e = C * sigma_e_296 * np.exp(-E_k_e / (pybamm.constants.R * T)) / T + C = 296 * pybamm.exp(E_k_e / (pybamm.constants.R * 296)) + sigma_e = C * sigma_e_296 * pybamm.exp(-E_k_e / (pybamm.constants.R * T)) / T return sigma_e @@ -547,6 +538,5 @@ def get_parameter_values(): "Xu2019", "Richardson2020", "OKane2020", - "Yuan2023", ], } diff --git a/pybamm/input/parameters/lithium_ion/Yuan2023.py b/pybamm/input/parameters/lithium_ion/Yuan2023.py new file mode 100644 index 0000000000..d9bbf059ea --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/Yuan2023.py @@ -0,0 +1,652 @@ +import pybamm +import numpy as np + + +def graphite_diffusivity_Ecker2015(sto, T): + """ + Graphite diffusivity as a function of stochiometry [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + log_D_ref = ( + -15.06 + + 2.722 * np.exp(-((sto / 0.0777) ** 2)) + + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) + + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) + + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) + + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) + ) + D_ref = 10**log_D_ref + E_D_s = 3.03e4 + arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( + E_D_s / (pybamm.constants.R * 296) + ) + + return D_ref * arrhenius + + +def graphite_ocp_Ecker2015(sto): + """ + Graphite OCP as a function of stochiometry [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + + Returns + ------- + :class:`pybamm.Symbol` + Open-circuit potential + """ + + # Graphite negative electrode from Ecker, Kabitz, Laresgoiti et al. + # Analytical fit (WebPlotDigitizer + gnuplot) + a = 0.716502 + b = 369.028 + c = 0.12193 + d = 35.6478 + e = 0.0530947 + g = 0.0169644 + h = 27.1365 + i = 0.312832 + j = 0.0199313 + k = 28.5697 + m = 0.614221 + n = 0.931153 + o = 36.328 + p = 1.10743 + q = 0.140031 + r = 0.0189193 + s = 21.1967 + t = 0.196176 + + u_eq = ( + a * np.exp(-b * sto) + + c * np.exp(-d * (sto - e)) + - r * np.tanh(s * (sto - t)) + - g * np.tanh(h * (sto - i)) + - j * np.tanh(k * (sto - m)) + - n * np.exp(o * (sto - p)) + + q + ) + + return u_eq + + +def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T): + """ + Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in + EC:DMC. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_s_surf : :class:`pybamm.Symbol` + Particle concentration [mol.m-3] + c_s_max : :class:`pybamm.Symbol` + Maximum particle concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_ref = 1.11 * 1e-10 + + # multiply by Faraday's constant to get correct units + m_ref = ( + pybamm.constants.F * k_ref + ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations + E_r = 53400 + + arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( + E_r / (pybamm.constants.R * 296.15) + ) + + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 + + +def nco_diffusivity_Ecker2015(sto, T): + """ + NCO diffusivity as a function of stochiometry [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + log_D_ref = ( + -12.67 + - 1.597 * np.exp(-(((sto - 0.6169) / 0.07859) ** 2)) + - 1.608 * np.exp(-(((sto - 1) / 0.0931) ** 2)) + ) + D_ref = 10**log_D_ref + E_D_s = 8.06e4 + arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( + E_D_s / (pybamm.constants.R * 296.15) + ) + + return D_ref * arrhenius + + +def nco_ocp_Ecker2015(sto): + """ + NCO OCP as a function of stochiometry [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + sto : :class:`pybamm.Symbol` + Stochiometry of material (li-fraction) + + """ + + # LiNiCo from Ecker, Kabitz, Laresgoiti et al. + # Analytical fit (WebPlotDigitizer + gnuplot) + # Parameter m modified by Simon O'Kane to improve fit + a = -2.35211 + c = 0.0747061 + d = 31.886 + e = 0.0219921 + g = 0.640243 + h = 5.48623 + i = 0.439245 + j = 3.82383 + k = 4.12167 + m = 0.176187 + n = 0.0542123 + o = 18.2919 + p = 0.762272 + q = 4.23285 + r = -6.34984 + s = 2.66395 + t = 0.174352 + + u_eq = ( + a * sto + - c * np.tanh(d * (sto - e)) + - r * np.tanh(s * (sto - t)) + - g * np.tanh(h * (sto - i)) + - j * np.tanh(k * (sto - m)) + - n * np.tanh(o * (sto - p)) + + q + ) + return u_eq + + +def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T): + """ + Exchange-current density for Butler-Volmer reactions between NCO and LiPF6 in + EC:DMC [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_s_surf : :class:`pybamm.Symbol` + Particle concentration [mol.m-3] + c_s_max : :class:`pybamm.Symbol` + Maximum particle concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_ref = 3.01e-11 + + # multiply by Faraday's constant to get correct units + m_ref = ( + pybamm.constants.F * k_ref + ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations + + E_r = 4.36e4 + arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( + E_r / (pybamm.constants.R * 296.15) + ) + + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 + + +def plating_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li plating reaction [A.m-2]. + References + ---------- + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_e + + +def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li stripping reaction [A.m-2]. + + References + ---------- + + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + + Parameters + ---------- + + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_Li + + +def SEI_limited_dead_lithium_OKane2022(L_sei): + """ + Decay rate for dead lithium formation [s-1]. + References + ---------- + .. [1] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diega Alonso-Alvarez, + Robert Timms, Valentin Sulzer, Jaqueline Sophie Edge, Billy Wu, Gregory J. Offer + and Monica Marinescu. "Lithium-ion battery degradation: how to model it." + Physical Chemistry: Chemical Physics 24, no. 13 (2022): 7909-7922. + Parameters + ---------- + L_sei : :class:`pybamm.Symbol` + Total SEI thickness [m] + Returns + ------- + :class:`pybamm.Symbol` + Dead lithium decay rate [s-1] + """ + + gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + L_sei_0 = L_inner_0 + L_outer_0 + + gamma = gamma_0 * L_sei_0 / L_sei + + return gamma + + +def electrolyte_diffusivity_Ecker2015(c_e, T): + """ + Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + # The diffusivity epends on the electrolyte conductivity + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + sigma_e = pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) + + D_c_e = ( + (pybamm.constants.k_b / (pybamm.constants.F * pybamm.constants.q_e)) + * sigma_e + * T + / c_e + ) + + return D_c_e + + +def electrolyte_conductivity_Ecker2015(c_e, T): + """ + Conductivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. + + References + ---------- + .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery i. determination of parameters." Journal of the + Electrochemical Society 162.9 (2015): A1836-A1848. + .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of + a lithium-ion battery ii. model validation." Journal of The Electrochemical + Society 162.9 (2015): A1849-A1857. + .. [3] Richardson, Giles, et. al. "Generalised single particle models for + high-rate operation of graded lithium-ion electrodes: Systematic derivation + and validation." Electrochemica Acta 339 (2020): 135862 + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + # mol/m^3 to mol/l + cm = 1e-3 * c_e + + # value at T = 296K + sigma_e_296 = 0.2667 * cm**3 - 1.2983 * cm**2 + 1.7919 * cm + 0.1726 + + # add temperature dependence + E_k_e = 1.71e4 + C = 296 * np.exp(E_k_e / (pybamm.constants.R * 296)) + sigma_e = C * sigma_e_296 * np.exp(-E_k_e / (pybamm.constants.R * T)) / T + + return sigma_e + + +# Call dict via a function to avoid errors when editing in place +def get_parameter_values(): + """ + Parameters for a Kokam SLPB 75106100 cell, from the papers :footcite:t:`Ecker2015i` + and :footcite:t:`Ecker2015ii` + + The tab placement parameters are taken from measurements in :footcite:t:`Hales2019` + + The thermal material properties are for a 5 Ah power pouch cell by Kokam. The data + are extracted from :footcite:t:`Zhao2018` + + Graphite negative electrode parameters + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + The fits to data for the electrode and electrolyte properties are those provided + by Dr. Simon O'Kane in the paper :footcite:t:`Richardson2020` + + SEI parameters are example parameters for SEI growth from the papers + :footcite:t:`Ramadass2004`, :footcite:t:`ploehn2004solvent`, + :footcite:t:`single2018identifying`, :footcite:t:`safari2008multimodal`, and + :footcite:t:`Yang2017` + + .. note:: + This parameter set does not claim to be representative of the true parameter + values. Instead these are parameter values that were used to fit SEI models to + observed experimental data in the referenced papers. + """ + + return { + "chemistry": "lithium_ion", + # lithium plating + "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, + "Lithium plating kinetic rate constant [m.s-1]": 1e-10, + "Exchange-current density for plating [A.m-2]" + "": plating_exchange_current_density_OKane2020, + "Exchange-current density for stripping [A.m-2]" + "": stripping_exchange_current_density_OKane2020, + "Initial plated lithium concentration [mol.m-3]": 0.0, + "Typical plated lithium concentration [mol.m-3]": 1000.0, + "Lithium plating transfer coefficient": 0.5, + "Dead lithium decay constant [s-1]": 1e-06, + "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, + # sei + "Ratio of lithium moles to SEI moles": 2.0, + "Inner SEI reaction proportion": 0.5, + "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "SEI reaction exchange current density [A.m-2]": 1.5e-07, + "SEI resistivity [Ohm.m]": 200000.0, + "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration [mol.m-3]": 2636.0, + "Inner SEI open-circuit potential [V]": 0.1, + "Outer SEI open-circuit potential [V]": 0.8, + "Inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial inner SEI thickness [m]": 2.5e-09, + "Initial outer SEI thickness [m]": 2.5e-09, + "EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity [m2.s-1]": 2e-18, + "SEI kinetic rate constant [m.s-1]": 1e-12, + "SEI open-circuit potential [V]": 0.4, + "SEI growth activation energy [J.mol-1]": 0.0, + "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, + "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, + # cell + "Negative current collector thickness [m]": 1.4e-05, + "Negative electrode thickness [m]": 7.4e-05, + "Separator thickness [m]": 2e-05, + "Positive electrode thickness [m]": 5.4e-05, + "Positive current collector thickness [m]": 1.5e-05, + "Electrode height [m]": 0.101, + "Electrode width [m]": 0.085, + "Negative tab width [m]": 0.007, + "Negative tab centre y-coordinate [m]": 0.0045, + "Negative tab centre z-coordinate [m]": 0.101, + "Positive tab width [m]": 0.0069, + "Positive tab centre y-coordinate [m]": 0.0309, + "Positive tab centre z-coordinate [m]": 0.101, + "Cell cooling surface area [m2]": 0.0172, + "Cell volume [m3]": 1.52e-06, + "Negative current collector conductivity [S.m-1]": 58411000.0, + "Positive current collector conductivity [S.m-1]": 36914000.0, + "Negative current collector density [kg.m-3]": 8933.0, + "Positive current collector density [kg.m-3]": 2702.0, + "Negative current collector specific heat capacity [J.kg-1.K-1]": 385.0, + "Positive current collector specific heat capacity [J.kg-1.K-1]": 903.0, + "Negative current collector thermal conductivity [W.m-1.K-1]": 398.0, + "Positive current collector thermal conductivity [W.m-1.K-1]": 238.0, + "Nominal cell capacity [A.h]": 0.15625, + "Current function [A]": 0.15652, + "Contact resistance [Ohm]": 0, + # negative electrode + "Negative electrode conductivity [S.m-1]": 14.0, + "Maximum concentration in negative electrode [mol.m-3]": 31920.0, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, + "Negative electrode OCP [V]": graphite_ocp_Ecker2015, + "Negative electrode porosity": 0.329, + "Negative electrode active material volume fraction": 0.372403, + "Negative particle radius [m]": 1.37e-05, + "Negative electrode Bruggeman coefficient (electrolyte)": 1.6372789338386007, + "Negative electrode Bruggeman coefficient (electrode)": 0.0, + "Negative electrode exchange-current density [A.m-2]" + "": graphite_electrolyte_exchange_current_density_Ecker2015, + "Negative electrode density [kg.m-3]": 1555.0, + "Negative electrode specific heat capacity [J.kg-1.K-1]": 1437.0, + "Negative electrode thermal conductivity [W.m-1.K-1]": 1.58, + "Negative electrode OCP entropic change [V.K-1]": 0.0, + # positive electrode + "Positive electrode conductivity [S.m-1]": 68.1, + "Maximum concentration in positive electrode [mol.m-3]": 48580.0, + "Positive particle diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, + "Positive electrode OCP [V]": nco_ocp_Ecker2015, + "Positive electrode porosity": 0.296, + "Positive electrode active material volume fraction": 0.40832, + "Positive particle radius [m]": 6.5e-06, + "Positive electrode Bruggeman coefficient (electrolyte)": 1.5442267190786427, + "Positive electrode Bruggeman coefficient (electrode)": 0.0, + "Positive electrode exchange-current density [A.m-2]" + "": nco_electrolyte_exchange_current_density_Ecker2015, + "Positive electrode density [kg.m-3]": 2895.0, + "Positive electrode specific heat capacity [J.kg-1.K-1]": 1270.0, + "Positive electrode thermal conductivity [W.m-1.K-1]": 1.04, + "Positive electrode OCP entropic change [V.K-1]": 0.0, + # separator + "Separator porosity": 0.508, + "Separator Bruggeman coefficient (electrolyte)": 1.9804586773134945, + "Separator density [kg.m-3]": 1017.0, + "Separator specific heat capacity [J.kg-1.K-1]": 1978.0, + "Separator thermal conductivity [W.m-1.K-1]": 0.34, + # electrolyte + "Initial concentration in electrolyte [mol.m-3]": 1000.0, + "Cation transference number": 0.26, + "Thermodynamic factor": 1.0, + "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Ecker2015, + "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Ecker2015, + # experiment + "Reference temperature [K]": 296.15, + "Negative current collector surface heat transfer coefficient [W.m-2.K-1]" + "": 35.0, + "Positive current collector surface heat transfer coefficient [W.m-2.K-1]" + "": 35.0, + "Negative tab heat transfer coefficient [W.m-2.K-1]": 35.0, + "Positive tab heat transfer coefficient [W.m-2.K-1]": 35.0, + "Edge heat transfer coefficient [W.m-2.K-1]": 35.0, + "Total heat transfer coefficient [W.m-2.K-1]": 35.0, + "Ambient temperature [K]": 298.15, + "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of cells connected in series to make a battery": 1.0, + "Lower voltage cut-off [V]": 2.5, + "Upper voltage cut-off [V]": 4.2, + "Open-circuit voltage at 0% SOC [V]": 2.5, + "Open-circuit voltage at 100% SOC [V]": 4.2, + "Initial concentration in negative electrode [mol.m-3]": 26120.05, + "Initial concentration in positive electrode [mol.m-3]": 12630.8, + "Initial temperature [K]": 298.15, + # citations + "citations": [ + "Ecker2015i", + "Ecker2015ii", + "Zhao2018", + "Hales2019", + "Richardson2020", + "OKane2020", + "Yuan2023", + ], + } From 637a19ff03436ede2a3884f929603d8e1b5f96ba Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Mon, 10 Jun 2024 18:54:30 +0100 Subject: [PATCH 5/8] Updated Yuan2023 parameter set --- .../input/parameters/lithium_ion/Yuan2023.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/pybamm/input/parameters/lithium_ion/Yuan2023.py b/pybamm/input/parameters/lithium_ion/Yuan2023.py index d9bbf059ea..63f40f2386 100644 --- a/pybamm/input/parameters/lithium_ion/Yuan2023.py +++ b/pybamm/input/parameters/lithium_ion/Yuan2023.py @@ -572,8 +572,8 @@ def get_parameter_values(): "Positive current collector specific heat capacity [J.kg-1.K-1]": 903.0, "Negative current collector thermal conductivity [W.m-1.K-1]": 398.0, "Positive current collector thermal conductivity [W.m-1.K-1]": 238.0, - "Nominal cell capacity [A.h]": 0.15625, - "Current function [A]": 0.15652, + "Nominal cell capacity [A.h]": 7.5, + "Current function [A]": 7.5, "Contact resistance [Ohm]": 0, # negative electrode "Negative electrode conductivity [S.m-1]": 14.0, @@ -622,15 +622,15 @@ def get_parameter_values(): # experiment "Reference temperature [K]": 296.15, "Negative current collector surface heat transfer coefficient [W.m-2.K-1]" - "": 35.0, + "": 10.0, "Positive current collector surface heat transfer coefficient [W.m-2.K-1]" - "": 35.0, - "Negative tab heat transfer coefficient [W.m-2.K-1]": 35.0, - "Positive tab heat transfer coefficient [W.m-2.K-1]": 35.0, - "Edge heat transfer coefficient [W.m-2.K-1]": 35.0, - "Total heat transfer coefficient [W.m-2.K-1]": 35.0, + "": 10.0, + "Negative tab heat transfer coefficient [W.m-2.K-1]": 10.0, + "Positive tab heat transfer coefficient [W.m-2.K-1]": 10.0, + "Edge heat transfer coefficient [W.m-2.K-1]": 10.0, + "Total heat transfer coefficient [W.m-2.K-1]": 10.0, "Ambient temperature [K]": 298.15, - "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of electrodes connected in parallel to make a cell": 48.0, "Number of cells connected in series to make a battery": 1.0, "Lower voltage cut-off [V]": 2.5, "Upper voltage cut-off [V]": 4.2, From abcbf9aeb080b16735c847b2c0bbf5c12ad54931 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 11 Jun 2024 13:43:35 +0100 Subject: [PATCH 6/8] Removed Yuan2023 parameters for now --- CHANGELOG.md | 1 - pybamm/CITATIONS.bib | 8 - .../input/parameters/lithium_ion/Yuan2023.py | 652 ------------------ 3 files changed, 661 deletions(-) delete mode 100644 pybamm/input/parameters/lithium_ion/Yuan2023.py diff --git a/CHANGELOG.md b/CHANGELOG.md index f2f7c09249..4fcd5acb81 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,6 @@ ## Features -- Added new parameter set `Yuan2023` ([#4141](https://github.com/pybamm-team/PyBaMM/pull/4141)) - Added `pybamm.DataLoader` class to fetch data files from [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0) and store it under local cache. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index c8b8c278df..00d41fadd3 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -783,11 +783,3 @@ @article{Wycisk2022 author = {Dominik Wycisk and Marc Oldenburger and Marc Gerry Stoye and Toni Mrkonjic and Arnulf Latz}, keywords = {Lithium-ion battery, Voltage hysteresis, Plett-model, Silicon–graphite anode}, } -@article{Yuan2023, - title = {A electrochemical-electro-thermal coupled computational framework to simulate the performance of Li-ion batteries at cell-level: Analysis on the thermal effects}, - journal = {arXiv preprint}, - year = {2023}, - doi = {https://doi.org/10.48550/arXiv.2303.09838}, - url = {https://arxiv.org/abs/2303.09838}, - author = {Han Yuan and Shen Li and Tao Zhu and Simon O'Kane and Carlos Garcia and Gregory Offer and Monica Marinescu}, -} diff --git a/pybamm/input/parameters/lithium_ion/Yuan2023.py b/pybamm/input/parameters/lithium_ion/Yuan2023.py deleted file mode 100644 index 63f40f2386..0000000000 --- a/pybamm/input/parameters/lithium_ion/Yuan2023.py +++ /dev/null @@ -1,652 +0,0 @@ -import pybamm -import numpy as np - - -def graphite_diffusivity_Ecker2015(sto, T): - """ - Graphite diffusivity as a function of stochiometry [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - sto: :class:`pybamm.Symbol` - Electrode stochiometry - T: :class:`pybamm.Symbol` - Dimensional temperature - - Returns - ------- - :class:`pybamm.Symbol` - Solid diffusivity - """ - - log_D_ref = ( - -15.06 - + 2.722 * np.exp(-((sto / 0.0777) ** 2)) - + 1.503 * np.exp(-(((sto - 0.17) / 0.1153) ** 2)) - + 1.055 * np.exp(-(((sto - 0.3118) / 0.04089) ** 2)) - + 1.555 * np.exp(-(((sto - 0.6061) / 0.04545) ** 2)) - + 1.215 * np.exp(-(((sto - 1) / 0.02759) ** 2)) - ) - D_ref = 10**log_D_ref - E_D_s = 3.03e4 - arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( - E_D_s / (pybamm.constants.R * 296) - ) - - return D_ref * arrhenius - - -def graphite_ocp_Ecker2015(sto): - """ - Graphite OCP as a function of stochiometry [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - sto: :class:`pybamm.Symbol` - Electrode stochiometry - - Returns - ------- - :class:`pybamm.Symbol` - Open-circuit potential - """ - - # Graphite negative electrode from Ecker, Kabitz, Laresgoiti et al. - # Analytical fit (WebPlotDigitizer + gnuplot) - a = 0.716502 - b = 369.028 - c = 0.12193 - d = 35.6478 - e = 0.0530947 - g = 0.0169644 - h = 27.1365 - i = 0.312832 - j = 0.0199313 - k = 28.5697 - m = 0.614221 - n = 0.931153 - o = 36.328 - p = 1.10743 - q = 0.140031 - r = 0.0189193 - s = 21.1967 - t = 0.196176 - - u_eq = ( - a * np.exp(-b * sto) - + c * np.exp(-d * (sto - e)) - - r * np.tanh(s * (sto - t)) - - g * np.tanh(h * (sto - i)) - - j * np.tanh(k * (sto - m)) - - n * np.exp(o * (sto - p)) - + q - ) - - return u_eq - - -def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T): - """ - Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in - EC:DMC. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - c_e : :class:`pybamm.Symbol` - Electrolyte concentration [mol.m-3] - c_s_surf : :class:`pybamm.Symbol` - Particle concentration [mol.m-3] - c_s_max : :class:`pybamm.Symbol` - Maximum particle concentration [mol.m-3] - T : :class:`pybamm.Symbol` - Temperature [K] - - Returns - ------- - :class:`pybamm.Symbol` - Exchange-current density [A.m-2] - """ - - k_ref = 1.11 * 1e-10 - - # multiply by Faraday's constant to get correct units - m_ref = ( - pybamm.constants.F * k_ref - ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations - E_r = 53400 - - arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( - E_r / (pybamm.constants.R * 296.15) - ) - - return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - - -def nco_diffusivity_Ecker2015(sto, T): - """ - NCO diffusivity as a function of stochiometry [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - sto: :class:`pybamm.Symbol` - Electrode stochiometry - T: :class:`pybamm.Symbol` - Dimensional temperature - - Returns - ------- - :class:`pybamm.Symbol` - Solid diffusivity - """ - - log_D_ref = ( - -12.67 - - 1.597 * np.exp(-(((sto - 0.6169) / 0.07859) ** 2)) - - 1.608 * np.exp(-(((sto - 1) / 0.0931) ** 2)) - ) - D_ref = 10**log_D_ref - E_D_s = 8.06e4 - arrhenius = np.exp(-E_D_s / (pybamm.constants.R * T)) * np.exp( - E_D_s / (pybamm.constants.R * 296.15) - ) - - return D_ref * arrhenius - - -def nco_ocp_Ecker2015(sto): - """ - NCO OCP as a function of stochiometry [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - sto : :class:`pybamm.Symbol` - Stochiometry of material (li-fraction) - - """ - - # LiNiCo from Ecker, Kabitz, Laresgoiti et al. - # Analytical fit (WebPlotDigitizer + gnuplot) - # Parameter m modified by Simon O'Kane to improve fit - a = -2.35211 - c = 0.0747061 - d = 31.886 - e = 0.0219921 - g = 0.640243 - h = 5.48623 - i = 0.439245 - j = 3.82383 - k = 4.12167 - m = 0.176187 - n = 0.0542123 - o = 18.2919 - p = 0.762272 - q = 4.23285 - r = -6.34984 - s = 2.66395 - t = 0.174352 - - u_eq = ( - a * sto - - c * np.tanh(d * (sto - e)) - - r * np.tanh(s * (sto - t)) - - g * np.tanh(h * (sto - i)) - - j * np.tanh(k * (sto - m)) - - n * np.tanh(o * (sto - p)) - + q - ) - return u_eq - - -def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T): - """ - Exchange-current density for Butler-Volmer reactions between NCO and LiPF6 in - EC:DMC [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - c_e : :class:`pybamm.Symbol` - Electrolyte concentration [mol.m-3] - c_s_surf : :class:`pybamm.Symbol` - Particle concentration [mol.m-3] - c_s_max : :class:`pybamm.Symbol` - Maximum particle concentration [mol.m-3] - T : :class:`pybamm.Symbol` - Temperature [K] - - Returns - ------- - :class:`pybamm.Symbol` - Exchange-current density [A.m-2] - """ - - k_ref = 3.01e-11 - - # multiply by Faraday's constant to get correct units - m_ref = ( - pybamm.constants.F * k_ref - ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations - - E_r = 4.36e4 - arrhenius = np.exp(-E_r / (pybamm.constants.R * T)) * np.exp( - E_r / (pybamm.constants.R * 296.15) - ) - - return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - - -def plating_exchange_current_density_OKane2020(c_e, c_Li, T): - """ - Exchange-current density for Li plating reaction [A.m-2]. - References - ---------- - .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and - Monica Marinescu. "Physical origin of the differential voltage minimum associated - with lithium plating in Li-ion batteries." Journal of The Electrochemical Society - 167, no. 9 (2020): 090540. - Parameters - ---------- - c_e : :class:`pybamm.Symbol` - Electrolyte concentration [mol.m-3] - c_Li : :class:`pybamm.Symbol` - Plated lithium concentration [mol.m-3] - T : :class:`pybamm.Symbol` - Temperature [K] - Returns - ------- - :class:`pybamm.Symbol` - Exchange-current density [A.m-2] - """ - - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") - - return pybamm.constants.F * k_plating * c_e - - -def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): - """ - Exchange-current density for Li stripping reaction [A.m-2]. - - References - ---------- - - .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and - Monica Marinescu. "Physical origin of the differential voltage minimum associated - with lithium plating in Li-ion batteries." Journal of The Electrochemical Society - 167, no. 9 (2020): 090540. - - Parameters - ---------- - - c_e : :class:`pybamm.Symbol` - Electrolyte concentration [mol.m-3] - c_Li : :class:`pybamm.Symbol` - Plated lithium concentration [mol.m-3] - T : :class:`pybamm.Symbol` - Temperature [K] - - Returns - ------- - - :class:`pybamm.Symbol` - Exchange-current density [A.m-2] - """ - - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") - - return pybamm.constants.F * k_plating * c_Li - - -def SEI_limited_dead_lithium_OKane2022(L_sei): - """ - Decay rate for dead lithium formation [s-1]. - References - ---------- - .. [1] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diega Alonso-Alvarez, - Robert Timms, Valentin Sulzer, Jaqueline Sophie Edge, Billy Wu, Gregory J. Offer - and Monica Marinescu. "Lithium-ion battery degradation: how to model it." - Physical Chemistry: Chemical Physics 24, no. 13 (2022): 7909-7922. - Parameters - ---------- - L_sei : :class:`pybamm.Symbol` - Total SEI thickness [m] - Returns - ------- - :class:`pybamm.Symbol` - Dead lithium decay rate [s-1] - """ - - gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") - L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") - L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") - L_sei_0 = L_inner_0 + L_outer_0 - - gamma = gamma_0 * L_sei_0 / L_sei - - return gamma - - -def electrolyte_diffusivity_Ecker2015(c_e, T): - """ - Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - c_e: :class:`pybamm.Symbol` - Dimensional electrolyte concentration - T: :class:`pybamm.Symbol` - Dimensional temperature - - Returns - ------- - :class:`pybamm.Symbol` - Solid diffusivity - """ - - # The diffusivity epends on the electrolyte conductivity - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - sigma_e = pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) - - D_c_e = ( - (pybamm.constants.k_b / (pybamm.constants.F * pybamm.constants.q_e)) - * sigma_e - * T - / c_e - ) - - return D_c_e - - -def electrolyte_conductivity_Ecker2015(c_e, T): - """ - Conductivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. - - References - ---------- - .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery i. determination of parameters." Journal of the - Electrochemical Society 162.9 (2015): A1836-A1848. - .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of - a lithium-ion battery ii. model validation." Journal of The Electrochemical - Society 162.9 (2015): A1849-A1857. - .. [3] Richardson, Giles, et. al. "Generalised single particle models for - high-rate operation of graded lithium-ion electrodes: Systematic derivation - and validation." Electrochemica Acta 339 (2020): 135862 - - Parameters - ---------- - c_e: :class:`pybamm.Symbol` - Dimensional electrolyte concentration - T: :class:`pybamm.Symbol` - Dimensional temperature - - Returns - ------- - :class:`pybamm.Symbol` - Solid diffusivity - """ - - # mol/m^3 to mol/l - cm = 1e-3 * c_e - - # value at T = 296K - sigma_e_296 = 0.2667 * cm**3 - 1.2983 * cm**2 + 1.7919 * cm + 0.1726 - - # add temperature dependence - E_k_e = 1.71e4 - C = 296 * np.exp(E_k_e / (pybamm.constants.R * 296)) - sigma_e = C * sigma_e_296 * np.exp(-E_k_e / (pybamm.constants.R * T)) / T - - return sigma_e - - -# Call dict via a function to avoid errors when editing in place -def get_parameter_values(): - """ - Parameters for a Kokam SLPB 75106100 cell, from the papers :footcite:t:`Ecker2015i` - and :footcite:t:`Ecker2015ii` - - The tab placement parameters are taken from measurements in :footcite:t:`Hales2019` - - The thermal material properties are for a 5 Ah power pouch cell by Kokam. The data - are extracted from :footcite:t:`Zhao2018` - - Graphite negative electrode parameters - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - The fits to data for the electrode and electrolyte properties are those provided - by Dr. Simon O'Kane in the paper :footcite:t:`Richardson2020` - - SEI parameters are example parameters for SEI growth from the papers - :footcite:t:`Ramadass2004`, :footcite:t:`ploehn2004solvent`, - :footcite:t:`single2018identifying`, :footcite:t:`safari2008multimodal`, and - :footcite:t:`Yang2017` - - .. note:: - This parameter set does not claim to be representative of the true parameter - values. Instead these are parameter values that were used to fit SEI models to - observed experimental data in the referenced papers. - """ - - return { - "chemistry": "lithium_ion", - # lithium plating - "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, - "Lithium plating kinetic rate constant [m.s-1]": 1e-10, - "Exchange-current density for plating [A.m-2]" - "": plating_exchange_current_density_OKane2020, - "Exchange-current density for stripping [A.m-2]" - "": stripping_exchange_current_density_OKane2020, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.5, - "Dead lithium decay constant [s-1]": 1e-06, - "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, - # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "Inner SEI open-circuit potential [V]": 0.1, - "Outer SEI open-circuit potential [V]": 0.8, - "Inner SEI electron conductivity [S.m-1]": 8.95e-14, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, - "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, - "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, - # cell - "Negative current collector thickness [m]": 1.4e-05, - "Negative electrode thickness [m]": 7.4e-05, - "Separator thickness [m]": 2e-05, - "Positive electrode thickness [m]": 5.4e-05, - "Positive current collector thickness [m]": 1.5e-05, - "Electrode height [m]": 0.101, - "Electrode width [m]": 0.085, - "Negative tab width [m]": 0.007, - "Negative tab centre y-coordinate [m]": 0.0045, - "Negative tab centre z-coordinate [m]": 0.101, - "Positive tab width [m]": 0.0069, - "Positive tab centre y-coordinate [m]": 0.0309, - "Positive tab centre z-coordinate [m]": 0.101, - "Cell cooling surface area [m2]": 0.0172, - "Cell volume [m3]": 1.52e-06, - "Negative current collector conductivity [S.m-1]": 58411000.0, - "Positive current collector conductivity [S.m-1]": 36914000.0, - "Negative current collector density [kg.m-3]": 8933.0, - "Positive current collector density [kg.m-3]": 2702.0, - "Negative current collector specific heat capacity [J.kg-1.K-1]": 385.0, - "Positive current collector specific heat capacity [J.kg-1.K-1]": 903.0, - "Negative current collector thermal conductivity [W.m-1.K-1]": 398.0, - "Positive current collector thermal conductivity [W.m-1.K-1]": 238.0, - "Nominal cell capacity [A.h]": 7.5, - "Current function [A]": 7.5, - "Contact resistance [Ohm]": 0, - # negative electrode - "Negative electrode conductivity [S.m-1]": 14.0, - "Maximum concentration in negative electrode [mol.m-3]": 31920.0, - "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, - "Negative electrode OCP [V]": graphite_ocp_Ecker2015, - "Negative electrode porosity": 0.329, - "Negative electrode active material volume fraction": 0.372403, - "Negative particle radius [m]": 1.37e-05, - "Negative electrode Bruggeman coefficient (electrolyte)": 1.6372789338386007, - "Negative electrode Bruggeman coefficient (electrode)": 0.0, - "Negative electrode exchange-current density [A.m-2]" - "": graphite_electrolyte_exchange_current_density_Ecker2015, - "Negative electrode density [kg.m-3]": 1555.0, - "Negative electrode specific heat capacity [J.kg-1.K-1]": 1437.0, - "Negative electrode thermal conductivity [W.m-1.K-1]": 1.58, - "Negative electrode OCP entropic change [V.K-1]": 0.0, - # positive electrode - "Positive electrode conductivity [S.m-1]": 68.1, - "Maximum concentration in positive electrode [mol.m-3]": 48580.0, - "Positive particle diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, - "Positive electrode OCP [V]": nco_ocp_Ecker2015, - "Positive electrode porosity": 0.296, - "Positive electrode active material volume fraction": 0.40832, - "Positive particle radius [m]": 6.5e-06, - "Positive electrode Bruggeman coefficient (electrolyte)": 1.5442267190786427, - "Positive electrode Bruggeman coefficient (electrode)": 0.0, - "Positive electrode exchange-current density [A.m-2]" - "": nco_electrolyte_exchange_current_density_Ecker2015, - "Positive electrode density [kg.m-3]": 2895.0, - "Positive electrode specific heat capacity [J.kg-1.K-1]": 1270.0, - "Positive electrode thermal conductivity [W.m-1.K-1]": 1.04, - "Positive electrode OCP entropic change [V.K-1]": 0.0, - # separator - "Separator porosity": 0.508, - "Separator Bruggeman coefficient (electrolyte)": 1.9804586773134945, - "Separator density [kg.m-3]": 1017.0, - "Separator specific heat capacity [J.kg-1.K-1]": 1978.0, - "Separator thermal conductivity [W.m-1.K-1]": 0.34, - # electrolyte - "Initial concentration in electrolyte [mol.m-3]": 1000.0, - "Cation transference number": 0.26, - "Thermodynamic factor": 1.0, - "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Ecker2015, - "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Ecker2015, - # experiment - "Reference temperature [K]": 296.15, - "Negative current collector surface heat transfer coefficient [W.m-2.K-1]" - "": 10.0, - "Positive current collector surface heat transfer coefficient [W.m-2.K-1]" - "": 10.0, - "Negative tab heat transfer coefficient [W.m-2.K-1]": 10.0, - "Positive tab heat transfer coefficient [W.m-2.K-1]": 10.0, - "Edge heat transfer coefficient [W.m-2.K-1]": 10.0, - "Total heat transfer coefficient [W.m-2.K-1]": 10.0, - "Ambient temperature [K]": 298.15, - "Number of electrodes connected in parallel to make a cell": 48.0, - "Number of cells connected in series to make a battery": 1.0, - "Lower voltage cut-off [V]": 2.5, - "Upper voltage cut-off [V]": 4.2, - "Open-circuit voltage at 0% SOC [V]": 2.5, - "Open-circuit voltage at 100% SOC [V]": 4.2, - "Initial concentration in negative electrode [mol.m-3]": 26120.05, - "Initial concentration in positive electrode [mol.m-3]": 12630.8, - "Initial temperature [K]": 298.15, - # citations - "citations": [ - "Ecker2015i", - "Ecker2015ii", - "Zhao2018", - "Hales2019", - "Richardson2020", - "OKane2020", - "Yuan2023", - ], - } From 03c8622bf850a362a38ad90701f55e694fcf57ea Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 11 Jun 2024 14:19:56 +0100 Subject: [PATCH 7/8] Added separate parameter for initial SEI on cracks --- pybamm/input/parameters/lithium_ion/Ai2020.py | 2 ++ pybamm/input/parameters/lithium_ion/OKane2022.py | 2 ++ .../lithium_ion/OKane2022_graphite_SiOx_halfcell.py | 2 ++ pybamm/parameters/lithium_ion_parameters.py | 11 ++++++----- 4 files changed, 12 insertions(+), 5 deletions(-) diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py index 2bf1eeecdd..b45c04fa7f 100644 --- a/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -549,6 +549,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 2.5e-09, "Initial outer SEI thickness [m]": 2.5e-09, + "Initial inner SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero + "Initial outer SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py index d3e43c1e90..b1e852dbdf 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -536,6 +536,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 0.0, "Initial outer SEI thickness [m]": 5e-09, + "Initial inner SEI on cracks thickness [m]": 0, + "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py index fe9d444874..35533ba80e 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py @@ -422,6 +422,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 0.0, "Initial outer SEI thickness [m]": 5e-09, + "Initial inner SEI on cracks thickness [m]": 0, + "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 4ee8953c99..8d69ec49ca 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -416,11 +416,12 @@ def _set_parameters(self): ) self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]") self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]") - - # Dividing by 10000 makes initial condition effectively zero - # without triggering division by zero errors - self.L_inner_crack_0 = self.L_inner_0 / 10000 - self.L_outer_crack_0 = self.L_outer_0 / 10000 + self.L_inner_crack_0 = pybamm.Parameter( + f"{pref}Initial inner SEI on cracks thickness [m]" + ) + self.L_outer_crack_0 = pybamm.Parameter( + f"{pref}Initial outer SEI on cracks thickness [m]" + ) self.L_sei_0 = self.L_inner_0 + self.L_outer_0 self.E_sei = pybamm.Parameter(f"{pref}SEI growth activation energy [J.mol-1]") From 3676c51e28fd0bd0d960f1bad23488d446ee98de Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 11 Jun 2024 14:25:31 +0100 Subject: [PATCH 8/8] changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4fcd5acb81..ae4616a2d4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,7 @@ ## Features +- Added new parameters `"f{pref]Initial inner SEI on cracks thickness [m]"` and `"f{pref]Initial outer SEI on cracks thickness [m]"`, instead of hardcoding these to `L_inner_0 / 10000` and `L_outer_0 / 10000`. ([#4168](https://github.com/pybamm-team/PyBaMM/pull/4168)) - Added `pybamm.DataLoader` class to fetch data files from [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0) and store it under local cache. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) - Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021))