From b68821d4d384b0518b896767e92a42ce45637a39 Mon Sep 17 00:00:00 2001
From: Sravan Pannala <57152030+sravanpannala@users.noreply.github.com>
Date: Tue, 3 Oct 2023 12:43:53 -0400
Subject: [PATCH] update effect_pressure

---
 degradation_model/effect_pressure.ipynb | 339 ++++++++++++++++++++++++
 1 file changed, 339 insertions(+)

diff --git a/degradation_model/effect_pressure.ipynb b/degradation_model/effect_pressure.ipynb
index d1294d7a1e..ecfac15c51 100644
--- a/degradation_model/effect_pressure.ipynb
+++ b/degradation_model/effect_pressure.ipynb
@@ -352,6 +352,345 @@
     "fig.tight_layout()\n",
     "fig.savefig(fig_DIR +'lip_pot_'+sim_des+'.png')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60.605"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "121.21/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.125442"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "18*0.069*0.101"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.744988982281839"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.7*0.069*0.101*18*63.15e-6*96485.3*31927.3/3600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22349.10395676854"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "173.9/0.63*2.17/96485.3*3600*1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.30977258370099"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.7*0.125*63.15e-6*96485.3*22349/3600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8639472915265"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.8*0.125*60.6e-6*96485.3*23790.3/3600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1661631419939575"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.86/3.31"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2958934278731233"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.86/(0.63*4.728)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def NMC_ocp(sto):\n",
+    "    u_eq = (\n",
+    "        4.396\n",
+    "        - 1.538 * sto\n",
+    "        + 0.7194 * (sto ** 2)\n",
+    "        - 0.009979 * (sto ** 3)\n",
+    "        + 1.074 * (sto ** 4)\n",
+    "        - 1.075 * (sto ** 5)\n",
+    "        - 4.071 * np.exp(75 * sto - 80.9)\n",
+    "    )\n",
+    "\n",
+    "    return u_eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def NMC_ocp1(sto):\n",
+    "\n",
+    "    p1 =   -45.5219\n",
+    "    p2 =   101.8180\n",
+    "    p3 =  -14.9391\n",
+    "    p4 =   -133.1001\n",
+    "    p5 =  136.4325\n",
+    "    p6 =-51.8812\n",
+    "    p7 = 6.8811\n",
+    "    p8 = 1.0881\n",
+    "    p9 =  -1.5667\n",
+    "    p10 =   4.2864\n",
+    "\n",
+    "    u_eq = (p1*sto**9 + p2*sto**8 + p3*sto**7 + p4*sto**6 + p5*sto**5 + p6*sto**4 + p7*sto**3 + p8*sto**2 + p9*sto + p10)\n",
+    "\n",
+    "    return u_eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def NMC_ocp2(sto):\n",
+    "\n",
+    "    p1 =   -45.5219\n",
+    "    p2 =   101.8180\n",
+    "    p3 =  -14.9391\n",
+    "    p4 =   -133.1001\n",
+    "    p5 =  136.4325\n",
+    "    p6 =-51.8812\n",
+    "    p7 = 6.8811\n",
+    "    p8 = 1.0881\n",
+    "    p9 =  -1.5667\n",
+    "    p10 =   4.2864\n",
+    "    sto = sto-0.3\n",
+    "\n",
+    "    u_eq = (p1*sto**9 + p2*sto**8 + p3*sto**7 + p4*sto**6 + p5*sto**5 + p6*sto**4 + p7*sto**3 + p8*sto**2 + p9*sto + p10)\n",
+    "\n",
+    "    return u_eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0,1,1000)\n",
+    "OCP = 0*np.linspace(0,1,1000)\n",
+    "OCP1 = 0*np.linspace(0,1,1000)\n",
+    "OCP2 = 0*np.linspace(0,1,1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for a,xx in enumerate(x):\n",
+    "    # OCP[a] = NMC_ocp(xx);\n",
+    "    OCP1[a] = NMC_ocp1(xx);\n",
+    "    OCP2[a] = NMC_ocp2(xx);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2029c7b26a0>]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b4f04124aed4c769e3d058602033377",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=500.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,1,figsize=(5,4))\n",
+    "# ax.plot(x,OCP)\n",
+    "ax.plot(x,OCP1,'b--')\n",
+    "ax.plot(x,OCP2,'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.4971000000000068"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "OCP1[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lower voltage cut-off [V]\t2.8\n",
+      "Upper voltage cut-off [V]\t4.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_values.search(\"voltage\")"
+   ]
   }
  ],
  "metadata": {