diff --git a/ChangeLog.md b/ChangeLog.md
index 801cb16..983056f 100644
--- a/ChangeLog.md
+++ b/ChangeLog.md
@@ -1,10 +1,10 @@
# Change Log
-## Next Version 2.3.0
+## Version 2.3.0 (July, 2023)
App
-- WIP: Add simple apps to the project.
+- Vehicle1D performance design app is added.
Files and folders organization
@@ -16,7 +16,7 @@ Files and folders organization
GitHub repository UX
- Sinced 2.2.0, Live Scripts were converted to Jupiter Notebooks
- so that they could be viewed directly in github web site in the browser.
+ so that they could be viewed directly in GitHub web site in the browser.
Now Markdown files are used instead of Jupyter Notebooks.
Markdown files are stored under the "Markdowns" folder
for the corresponding Live Scripts.
diff --git a/README.md b/README.md
index f1fecc7..8c08a2a 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
[![View Battery Electric Vehicle Model in Simscape on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/82250-battery-electric-vehicle-model-in-simscape)
-Version 2.2
+Version 2.3
## Introduction
@@ -41,6 +41,13 @@ Watch the [YouTube video][url_yt] introducing the model.
[url_yt]:https://www.youtube.com/watch?v=i07MNXZc42c
+## What's New in 2.3 (June, 2024)
+
+- The project has been updated to MATLAB R2024a.
+- Vehicle1D performance design app is added.
+- For veiwing Live Scripts in the GitHub web site in the browser,
+ they are converted to Markdown files and collected in the Markdown folder.
+
## What's New in 2.2 (September, 2023)
- The project has been updated to MATLAB R2023b.
diff --git a/Utility/AboutBEVProject.ipynb b/Utility/AboutBEVProject.ipynb
deleted file mode 100644
index 6b0587a..0000000
--- a/Utility/AboutBEVProject.ipynb
+++ /dev/null
@@ -1,445 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# About BEV Project\n",
- "\n",
- "This Live Script reports about some details of the BEV project. For information about MATLAB Project, see [Projects](https://www.mathworks.com/help/matlab/projects.html) in the documentation. For information about the object of MATLAB Project, see [matlab.project.Project](https://www.mathworks.com/help/matlab/ref/matlab.project.project.html).\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "source": [
- "% Check if any MATLAB Project is currently open.\n",
- "if not(isempty(matlab.project.rootProject))\n",
- " prj = currentProject;\n",
- " disp(prj.Name)\n",
- " rootfolder = prj.RootFolder;\n",
- "else\n",
- " disp(\"No project is open.\")\n",
- " return\n",
- "end"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Simscape Battery Electric Vehicle Model"
- ]
- },
- "metadata": {},
- "execution_count": 1,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Project Paths"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "source": [
- "paths = [prj.ProjectPath.StoredLocation]';\n",
- "disp(paths)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- " \"\"\n",
- " \"BEV\"\n",
- " \"BEV/SimulationCases\"\n",
- " \"BEV/Test\"\n",
- " \"BEV/Utility\"\n",
- " \"BEV/Utility/Configuration\"\n",
- " \"BEV/Utility/Images\"\n",
- " \"BEV/Utility/LocalTasks\"\n",
- " \"Components\"\n",
- " \"Components/BEVController\"\n",
- " \"Components/BEVController/Harness\"\n",
- " \"Components/BEVController/SimulationCases\"\n",
- " \"Components/BEVController/Test\"\n",
- " \"Components/BEVController/Utility\"\n",
- " \"Components/BEVController/Utility/Configuration\"\n",
- " \"Components/BatteryHighVoltage\"\n",
- " \"Components/BatteryHighVoltage/Harness\"\n",
- " \"Components/BatteryHighVoltage/Model-TabledBased\"\n",
- " \"Components/BatteryHighVoltage/SimulationCases\"\n",
- " \"Components/BatteryHighVoltage/Test\"\n",
- " \"Components/BatteryHighVoltage/Utility\"\n",
- " \"Components/BatteryHighVoltage/Utility/Configuration\"\n",
- " \"Components/BatteryHighVoltage/Utility/Images\"\n",
- " \"Components/BatteryHighVoltage/Utility/LocalTasks\"\n",
- " \"Components/ControllerAndEnvironment\"\n",
- " \"Components/ControllerAndEnvironment/Harness\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle/Harness\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle/SimulationCases\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle/Test\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle/Utility\"\n",
- " \"Components/ControllerAndEnvironment/Harness/Components/Vehicle/Utility/Configuration\"\n",
- " \"Components/ControllerAndEnvironment/SimulationCases\"\n",
- " \"Components/ControllerAndEnvironment/Test\"\n",
- " \"Components/ControllerAndEnvironment/Utility\"\n",
- " \"Components/ControllerAndEnvironment/Utility/Configuration\"\n",
- " \"Components/MotorDriveUnit\"\n",
- " \"Components/MotorDriveUnit/Harness\"\n",
- " \"Components/MotorDriveUnit/Notes-Efficiency\"\n",
- " \"Components/MotorDriveUnit/SimulationCases\"\n",
- " \"Components/MotorDriveUnit/Test\"\n",
- " \"Components/MotorDriveUnit/Utility\"\n",
- " \"Components/MotorDriveUnit/Utility/Configuration\"\n",
- " \"Components/MotorDriveUnit/Utility/Images\"\n",
- " \"Components/Reducer\"\n",
- " \"Components/Reducer/Utility\"\n",
- " \"Components/Reducer/Utility/Images\"\n",
- " \"Components/Vehicle1D\"\n",
- " \"Components/Vehicle1D/Custom\"\n",
- " \"Components/Vehicle1D/Harness\"\n",
- " \"Components/Vehicle1D/SimulationCases\"\n",
- " \"Components/Vehicle1D/Test\"\n",
- " \"Components/Vehicle1D/Utility\"\n",
- " \"Components/Vehicle1D/Utility/Configuration\"\n",
- " \"Components/Vehicle1D/Utility/Images\"\n",
- " \"Components/VehicleSpeedReference\"\n",
- " \"Components/VehicleSpeedReference/Harness\"\n",
- " \"Components/VehicleSpeedReference/SimulationCases\"\n",
- " \"Components/VehicleSpeedReference/Test\"\n",
- " \"Components/VehicleSpeedReference/Utility\"\n",
- " \"Components/VehicleSpeedReference/Utility/Configuration\"\n",
- " \"DetailedModelApplications\"\n",
- " \"DetailedModelApplications/MotorDrivePmsmFem\"\n",
- " \"DetailedModelApplications/MotorPmsmFem\"\n",
- " \"Interface\"\n",
- " \"Test\"\n",
- " \"Test/CheckProject\"\n",
- " \"Utility\"\n",
- " \"Utility/Checks\"\n",
- " \"Utility/LocalTasks\"\n",
- " \"Utility/SignalDesigner\"\n",
- " \"Utility/TestTools\"\n",
- " \"simcache\""
- ]
- },
- "metadata": {},
- "execution_count": 2,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Startup Files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "source": [
- "startups = extractAfter([prj.StartupFiles]', rootfolder);\n",
- "disp(startups)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- " \"\\Utility\\atProjectStartUp.m\"\n",
- " \"\\BEVProject_main_script.html\""
- ]
- },
- "metadata": {},
- "execution_count": 3,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulink cache folder"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "source": [
- "simcache = extractAfter(prj.SimulinkCacheFolder, rootfolder);\n",
- "disp(simcache)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\\simcache"
- ]
- },
- "metadata": {},
- "execution_count": 4,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Files in Project\n",
- "\n",
- "The number of files in the project\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "source": [
- "numel(prj.Files)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ans = 405"
- ]
- },
- "metadata": {},
- "execution_count": 5,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "source": [
- "files = extractAfter([prj.Files.Path]', rootfolder);\n",
- "% disp(files)"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "System Models\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "source": [
- "idx = contains(files, \"system_model\", IgnoreCase=true);\n",
- "sysmdls = files(idx);\n",
- "disp(sysmdls)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- " \"\\BEV\\BEV_system_model.mdl\"\n",
- " \"\\BEV\\Utility\\Images\\BEV_system_model_screenshot.png\""
- ]
- },
- "metadata": {},
- "execution_count": 7,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Referenced Subsystems\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "source": [
- "idx = contains(files, \"refsub\", IgnoreCase=true);\n",
- "refsubs = files(idx);\n",
- "numel(refsubs)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ans = 44"
- ]
- },
- "metadata": {},
- "execution_count": 8,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "source": [
- "disp(refsubs)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- " \"\\Components\\BEVController\\BEVController_refsub_Basic.mdl\"\n",
- " \"\\Components\\BEVController\\BEVController_refsub_Basic_params.m\"\n",
- " \"\\Components\\BEVController\\Utility\\Configuration\\BEVController_useRefsub.m\"\n",
- " \"\\Components\\BEVController\\Utility\\Configuration\\BEVController_useRefsub_Basic.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_Basic.mdl\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_Basic_params.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_System.mdl\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_SystemSimple.mdl\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_SystemSimple_params.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_SystemTable.mdl\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_SystemTable_params.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\BatteryHV_refsub_System_params.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\Utility\\Configuration\\BatteryHV_useRefsub.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\Utility\\Configuration\\BatteryHV_useRefsub_Basic.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\Utility\\Configuration\\BatteryHV_useRefsub_System.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\Utility\\Configuration\\BatteryHV_useRefsub_SystemSimple.m\"\n",
- " \"\\Components\\BatteryHighVoltage\\Utility\\Configuration\\BatteryHV_useRefsub_SystemTable.m\"\n",
- " \"\\Components\\ControllerAndEnvironment\\CtrlEnv_refsub_Basic.mdl\"\n",
- " \"\\Components\\ControllerAndEnvironment\\CtrlEnv_refsub_Basic_params.m\"\n",
- " \"\\Components\\ControllerAndEnvironment\\Harness\\Components\\Vehicle\\CtrlEnv_Vehicle_refsub.mdl\"\n",
- " \"\\Components\\ControllerAndEnvironment\\Harness\\Components\\Vehicle\\CtrlEnv_Vehicle_refsub_params.m\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_Basic.mdl\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_BasicThermal.mdl\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_BasicThermal_params.m\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_Basic_params.m\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_System.mdl\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_SystemTable.mdl\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_SystemTable_params.m\"\n",
- " \"\\Components\\MotorDriveUnit\\MotorDriveUnit_refsub_System_params.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Configuration\\MotorDriveUnit_useRefsub.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Configuration\\MotorDriveUnit_useRefsub_Basic.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Configuration\\MotorDriveUnit_useRefsub_BasicThermal.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Configuration\\MotorDriveUnit_useRefsub_System.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Configuration\\MotorDriveUnit_useRefsub_SystemTable.m\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Images\\MotorDriveUnit_refsub_Basic_efficiency.png\"\n",
- " \"\\Components\\MotorDriveUnit\\Utility\\Images\\MotorDriveUnit_refsub_System_efficiency.png\"\n",
- " \"\\Components\\Reducer\\Reducer_refsub_Basic.mdl\"\n",
- " \"\\Components\\Reducer\\Reducer_refsub_Basic_params.m\"\n",
- " \"\\Components\\Vehicle1D\\Custom\\Vehicle1D_refsub_Custom.mdl\"\n",
- " \"\\Components\\Vehicle1D\\Utility\\Images\\Vehicle1D_refsub_Basic_ResistingForcePower.png\"\n",
- " \"\\Components\\Vehicle1D\\Vehicle1D_refsub_Basic.mdl\"\n",
- " \"\\Components\\Vehicle1D\\Vehicle1D_refsub_Basic_params.m\"\n",
- " \"\\Components\\VehicleSpeedReference\\VehSpdRef_refsub_Basic.mdl\"\n",
- " \"\\DetailedModelApplications\\MotorPmsmFem\\PmsmFemRefSub.mdl\""
- ]
- },
- "metadata": {},
- "execution_count": 9,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Simscape custom components\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "source": [
- "idx = endsWith(files, \".ssc\");\n",
- "sscfiles = files(idx);\n",
- "numel(sscfiles)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ans = 1"
- ]
- },
- "metadata": {},
- "execution_count": 10,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "source": [
- "disp(sscfiles)"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\\Components\\Vehicle1D\\Custom\\Vehicle1D_Custom.ssc"
- ]
- },
- "metadata": {},
- "execution_count": 11,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "*Copyright 2022-2023 The MathWorks, Inc.*\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "MATLAB (matlabkernel)",
- "language": "matlab",
- "name": "matlab"
- },
- "language_info": {
- "file_extension": ".m",
- "mimetype": "text/matlab",
- "name": "matlab",
- "nbconvert_exporter": "matlab",
- "pygments_lexer": "matlab",
- "version": "23.2.0.2459199"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/Utility/SignalDesigner/SignalDesigner_example.ipynb b/Utility/SignalDesigner/SignalDesigner_example.ipynb
deleted file mode 100644
index 243208b..0000000
--- a/Utility/SignalDesigner/SignalDesigner_example.ipynb
+++ /dev/null
@@ -1,564 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Using Signal Designer and Signal Source Block Library\n",
- "\n",
- "[**Signal Designer**](SignalDesigner.m) lets you define a signal trace using simple parameterization scheme to define the shape of a signal. Signal Designer can create the following three types of signals.\n",
- "\n",
- "- **Continuous multi-step signal** is a signal trace consisting of smoothly connected flat segments.\n",
- "- **Continuous signal** is a smooth signal trace.\n",
- "- **Piece-wise constant signal** is a signal trace consisting of constant segments that are connected with discrete jumps.\n",
- "\n",
- "Accompanying [**Signal Source Block Library**](SignalSourceBlockLibrary) provides four blocks to generate signals in Simulink: **Continuous Multi-Step block**, **Continous block**, **Piece-Wise Constant block**, and **Trace Generator block**. These blocks internally use Signal Designer.\n",
- "\n",
- "\n",
- "
\n",
- "\n",
- "\n",
- "This Live Script describes each block and shows example uses of Signal Designer in MATLAB.\n",
- "\n",
- "\n",
- "## Table of Contents\n",
- "[Continuous Multi-Step](#H_B8D9F8D4)\n",
- "\n",
- "[Continuous](#H_6CEACC67)\n",
- "\n",
- "[Piece-Wise Constant](#H_53999B69)\n",
- "\n",
- "[Trace Generator](#H_7D36F555)\n",
- "\n",
- "\n",
- "\n",
- "## Continuous Multi-Step\n",
- "\n",
- "Continuous Multi-Step block has two parameters:\n",
- "\n",
- "
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\")
\n",
- "\n",
- "\n",
- "Data points parameter (N-by-3 matrix) and Interpolation step parameter correspond to XYData and DeltaX below, respectively.\n",
- "\n",
- "\n",
- "Create a Signal Designer object for a continuous multi-step signal trace.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "source": [
- "smoothSignal = SignalDesigner(\"ContinuousMultiStep\");"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Set an N-by-3 matrix for data points characterizing a continuous signal trace. In each row, a flat segment starts from first column and ends at second column in X (horizontal) axis. In Continuous Multi-Step block, X axis represents time. Third column defines a constant value in Y (vertical) axis. Second column can be NaN, in which case third column defines a value at point (X, Y).\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "source": [
- "smoothSignal.XYData = [\n",
- " 0 1 0 ; ... a flat segment from 0 to 1 in X, of value 0 in Y\n",
- " 3 nan 9 ; ... a point at 3 in X, of value 9 in Y\n",
- " 6 8 7 ]; % a flat segment from 6 to 8 in X, of value 7 in Y"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Set a resolution of interpolation.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "source": [
- "smoothSignal.DeltaX = 0.05;"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Call the update function to generate a signal trace data set in the Signal Designer object.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "source": [
- "update(smoothSignal)"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "As an example, let's see the first 5 rows. Notice that the first and second rows are the start and end of a flat segment, and there is no data point in between although interpolation resolution 0.05 is smaller than the distance in X between first and second rows (which is 1). This is because Signal Designer removed redundant intermediate data points after interpolation. This data point optimization helps reduce the final data size, especially when there are more flat segments than non-flat segments in the trace.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "source": [
- "disp(smoothSignal.Data(1:5, :))"
- ],
- "outputs": [
- {
- "data": {
- "text/plain": [
- " X Y \n",
- " ____ ________\n",
- " 0 0\n",
- " 1 0\n",
- "1.05 0.016594\n",
- "2. 1 0.06525\n",
- "3. 15 0.14428"
- ]
- },
- "metadata": {},
- "execution_count": 5,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Use plotDataPoints function to see how data points were converted to a smooth signal trace.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "source": [
- "plotDataPoints(smoothSignal);"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 6,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "A Signal Designer object has properties which you can modify.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "source": [
- "smoothSignal.XName = \"Time\";\n",
- "smoothSignal.XUnit = \"s\";\n",
- "\n",
- "smoothSignal.YName = \"Speed\";\n",
- "smoothSignal.YUnit = \"km/hr\";\n",
- "\n",
- "smoothSignal.Title = \"Vehicle speed reference\";\n",
- "\n",
- "% Make sure to call update to reflect new property values.\n",
- "update(smoothSignal)\n",
- "\n",
- "% Figure object is returned.\n",
- "fig = plotDataPoints(smoothSignal);\n",
- "fig.Position(3:4) = [500 300]; % width height"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 7,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Continuous\n",
- "\n",
- "Continuous block has two parameters:\n",
- "\n",
- "![\"image_2.png\"](\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAApoAAACnCAYAAACxd/ylAABLSUlEQVR42u3dd7B0VZ3u8VvzlzXjjGOYcYwoJkwIiICYQBRFMWIEFBAlKogRUVBRwYQimDNmESSKoIAJRMk5jICACIwKI0Ex71ufde/vrU3TfU6f+J7wrKqnTp/uvddeeX3Xb4X9f/74h5u7P9xyU/f73/+++9///d8oiqIoiqIompFw5R9uubn7PwHNKIqiKIqiKKAZRVEURVEUBTSjKIqiKIqigGb7cGNAM4qiKIqiKJpt0KQbb7xxwQXyhhtuaOp/7n83KmLT8X86v0817DP1M4qiKIqiaHFaNG/8/azBYRI31uEoiqIoigKaAc0oiqIoiqJobqfO58oKN9tTxcP8W0oWxFhDoyiKoiha9rvO5xqIhvm/lAAzQBlFURRFUUBzgW0OSuZFURRFURQtctD8n//5n+6qq67qfvnLX3aXX355k89XXHFF96tf/aq77rrrut/97nfzPnU+H9P44153/fXXd1dffXX361//uqXXbMSznw8+Tzf9YjGNoiiKomjBgWZBzRFHHNHttNNO3dOe9rTuSU96UrfBBht0T3nKU7rnP//53Wte85rua1/7WnfOOecsiSnpQZAbhLtRfl588cXd2972tm7fffftvvOd7zQAXwzrXKMoiqIoilYqaH7605/uNtxww+6Rj3xkA8ytt96622abbboXv/jF3fOe97zuZS97WfeFL3yhu/LKK8eybI4jFsKZAtVsgOa40/Rnn312t9lmm3WveMUrui9+8YvN4jtXoDkbaRNFURRFUbQgps6B5jOf+czu5S9/eff1r3+9+81vftOmiVnudtttt+4e97hHt+2223annnpq+/6aa65p0GlqfXCKHSQNTjfXlLNr3Ee+MwUNXP3mO371/ROOvn+u9QzT/BUOn11P/HHPoJ9+c63v+wDns+vr2vKHn/24uOaUU07pnvOc53Rbbrll9/GPf7xZeF3n3rrO32uvvbaFvdKGv6757W9/exuArHj4TVx8dq17K6x1TflV/g3GJVAaRVEURdGCBE2QUqC51VZbdd/85jcbEAEZazUPO+yw7iEPeUj3whe+sDv44IO7E044oTvwwAOb1fPZz35294xnPKNZ+nbdddf2Oygqq6fp5j322KPbfffdu/e9730NWlkEd9hhh+5LX/pSg7fTTz+9e9e73tWevemmm3abbLJJs6S+6U1v6r773e92v/jFL1aE9YILLmjhe9WrXtW99a1v7T72sY81a6uwv+AFL+je/e53d8cdd1x31llntSluv5V/BxxwQPsevFW8ge73vve99nzXAkkW3J133rn76le/2l122WUNEH/wgx+0eKy22mrdAx/4wG799ddv12233Xbd+9///hZG14nvZz7zme61r31tW3bwrGc9q8E7/8UVUFZcvvGNb7TlCsK55557djvuuGMLw3ve854W7/POO6+FwTWeJZ3599KXvrSBrrQAwCngURRFURQtKtCs34Dmt771re5BD3pQ96IXvah9PvLII7sPf/jDDYC23377Bn2g87nPfW73hje8ocEmq5v7gZ3vrfsEqq973esadO69995tXSgL6UknndTWPgItAAreWA1f8pKXtOs9r6x3p512Wnv2Ix7xiO7pT39695a3vKV79atf3cINdn23yy67dB/5yEcaGALGgk2AC5AvvfTSFjaWw6OPPrp75zvf2Z7nuZ4vPptvvnn3+te/vvv85z/f0gAkAso11lijCWB7rntdw9IImD/72c+2NLHsgF9UyxAA5IknnrjC+igej3nMY1qYX/nKV7bwvvnNb255AZati/U/yGRN5pc0IjALRIFyCngURVEURQt+6pzFjLWswIkAD8vh/e9//wZiP/7xj7vjjz++WTV/+tOfNlgz9fvzn/+8gReLJLA699xzbwOaD3vYw7qNNtqoWQ/7m2h8dg2rKf9MCYOnY489toEeCyIIrelpoPnBD36wW2WVVbrHP/7x3Xvf+972LNY9YPbwhz+8u9/97tfictBBBzW//QYSWSsB55lnntlgz32gEmD7+5Of/KRNU1900UUNAlkkQeDPfvazBrr9NZr87q/R5B/I5Ber40c/+tFmDTWNftRRRzX4XWeddVp4WSFd7xmrr7569+AHP7h7+9vf3gCYVZR/F154YbP+gm1/xcN9rLHCff755zcAruujKIqiKIoWNGjaaW6KfL311muwBLJqBzqr4Je//OUV6whrjSUAI9DFQsmy+IQnPKEBYR80AR7rI0jqbybyGVyCsvIPUJZ/a621Vtv1Dg5BFn/322+/btVVV20WPv67328nn3xyCzegBXYA0W/8NfXNqrjmmms2C6rvwPK6667bwPiYY45pzwRu/GItBHiPfvSju0MPPbSFbxRoCrP0YJEEjqboWWprfal7v/3tb3drr712m1K3thNMA01pzXoKkgtACzRZh8GuNDWVDiw9C7xS//ooiqIoiqIFC5qmYu06f9SjHtWOODKVC6hMA5vqtlYQaAEdsAniDjnkkLYT3b3WSlofCfJMa4M5kFWgucUWW3Sf+MQnGnQN7jzn3w9/+MM25f65z32uQS+ZXrceEgiacmbVBJof+tCHmoWUFbC/UQjEAmJWQNPU//3f/71iIw8rpSlnIA0wraX0PMC68cYbN+jzTNZcYWCdtFRAXGqaehRogkbPZvG9+93v3gBRevCr/BMe1lnpKq4A2DOf+MQntulxlt5+uvDbM8AuKzOIZckVlsMPP3yFhTOgGUVRFEXRggZNnwGM6WaQ5rNpXFa6WgNYZzsCJOsVTQGbJrfGEIyBKBB317vetU0FA0P3skQCTXDGImqjUD9g4JF/Nv54vvWPIJKsC73jHe/YNvLUuZVA0/pL1sF99tnnNn6BQc8hVsVai8lKCXBZRvnJOljrKU2z21HPckosmOSzaXjT85/85Ccb2I0CTWkCrG36ucMd7tA99KEPvY1fxJIqfaxrBZUsqkCTtdhGoR/96Ee3iUsBvQ1DANbmI/cLkzBUHmXqPIqiKIqiRWHRtL6QRZD1krWsjuPpX2e9ow06rIwsd6Z8QaANO6x34LMPmmXRBGdf+cpXbgearIvveMc72o5xVjvT1PyzRhMIAisbdWzaKdDcf//9h4Imq2IfNMt62gdNYQOapqZZZB/wgAe0TUo++x4EloTBelTXgkmgKZw25vRBU1pJF1P59773vdvUvvTo+0X8N6Veu/KBJisyS7C1r/24gHrhtpud9Vg4hJE110YkllHpYzo9BTyKoiiKokUBmoO7zgfFQuk6cAbmTEEDItDF+maNJJgDkOOApulraydt1DG1Xpt+WOv8BjStYRwXNEEgjQLNsmgKLysi0Ks4i0u9WhI8CoNNN9Zs+s7aStArvKbGy3/hFTbAzMLL2lvT43VMlHjVBp6C94lAs+D1kksuWZEmngE4pae1m+7zewp4FEVRFEULHjRZI8cFTZZPFkzwBB7tOrdL3BrPqYKm36zh5J/pYrLb3eahe97zng1q+6A50dR5HzQHp87tii/QBIHWbQozaDPtzQrJD9eKlw1C4Jk/YBH4OSvUtXbCC0ttigKC4meaXzqCTdZLFkkwKH38Dij7u85HgSZ/HSVlQ5R7+WPN6fe///32DOnFcto/YzSKoiiKomhRWjTrehY5m2ZsEnK9KXSfWQvBlzWH44Bm+Wc62oYiG28AXx3mbs2mjTDWO3rGRKBZfg1OnU8GmnZu26nOQspKae1jHRhvehpMs1KCztpZDv6czWntpl35gM+mJTBpit3ucvDKCssPfvHTlLu1lsBaeCYDTWEXLscuVZjqwHbp+YEPfKCtM63D56MoiqIoihYsaJqSZSH71Kc+1eBrlEfABjx61zeL41577dUOLQc+1lf6axe1aWZwVsAEZMEUwOv7539WUmBoFzlY5Z9pdOsgvTHHWkSA6dmsekDRtXZf9/0qCPYs19RbeOo1mPwDsGecccaKKXJ/xdc9zgsFzECX5RJA2sAEUvlR09m+e+Mb39iAU1y9pcc14ltHGUkHoGynO8B1nfWVptRr6pzF1FuBnCEKZvtxYdUVP+d/Cgt/bCQCsfxxXFN2nUdRFEVRtChAc1ArE2AGd8Sv7PDMdRzn+pooiqIoiqIFBZpzDaAzAaS5gM5xwzPsuoUAwQHOKIqiKIoCmrOgTBdHURRFURQtUtCsw9mTcFEURVEURdG8WTSjKIqiKIqiKKAZRVEURVEULRzQzLR5FEVRFEVRFItmFEVRFEVRFNCMoiiKoiiKljFomjL31pooiqIoiqIoGqXBpZaTgqb3b//617/ufvGLX7TXPHp1pNcpRlEURVEURRE2xImXXHJJd8UVV6x4nfdYoOmd426+6KKLmgcFmt4fHkVRFEVRFC1fFWgyRhYrgs3f/e5344Gmi88777zmya9+9avu2muvbbruuuuiKIqiKIqiZaziQrPfmBFsXnDBBd1vfvObYaB58+1A88orr+wuvPDC7qqrrmoemkqPoiiKoiiKIgKVpsvBJsPkxRdfPD5oAkw3XH311c0TptAoiqIoiqIoKgFOBknT6abR/T8WaJoud0NAM4qiKIqiKJoINH/5y1+2vT1TBk3mUGbQJGYURVEURVEU0IyiKIqiKIrmBTTNfAc0oyiKoiiKooBmFEVRFEVRFNCMoiiKoiiKApoBzSiKoiiKoiigGUVRFEVRFAU0oyiKoiiKooBmQDOKoiiKoigKaEZRFEVRFEUBzSiKoiiKoiigGdCM5rvweTVVFEVRFEXTk75UnxrQjKL/L4Xuqquu6s4+++zupJNO6k488cTuhBNOiKIoiqJoTOk79aFnnXVW47KFyGMBzWil6Morr+zOO++87tRTT+1++tOfdieffPJQTfRbFEVRFC136Sd//vOftz5V36qPxWWMOZdcckl3/vnnd+eee+5Ycq173DtbbBfQjFaKFOYf/ehHrWBfccUVreBdf/313Q033LBCVdb630VRFC1XaSOjqFRACeDOOeec7oc//GHrW32HzfSvLJ5HH310d8QRR3SHH374hHKNa93j3tniuwUNmuOuN6gEj2Zfs522lafM/Mcff3x3zTXXdH/4wx+6P//5z91f/vKXKIqiaIi0k/0+OIrKEOOvvlSfeuaZZzYu08eefvrpK4w5vrv22msnFD9YREHraaed1vzw3TyB5k0LGjSjxbUByF8V4Hvf+14rS//4xz+6uLi4uLjR7tZbb239cBQN6o9//GPrS/Wp+lZ8xrp5xhlnNFD0+1//+tfu73//+4RyjWvd415+8GtJg2Z0W6uizFls8P1buv723xstHXfccd2NN964oiENcMbFzZ1L/QpoRktTyoa+VJ+qb2WV/O53v9vWbLKGA8i//e1vY8m17nEvP2rNZ0AzFtOFe6xB0/VjgWZcXFxcXEAzmhlomio/5phjGiwCxz/96U/tmnHk2gJNfgQ0A5qLIi6//e3vAppxcXFxAc1onkDzO9/5zgqLpulwa3zHkWuXPWjO1fRxNhTN7zKAiUAzU3xxcXFxAc1o+lPnINFGIBttAeQtt9zSFNAMPC4bS2wsmnFxcXEBzWjuQDMWzWhGmaYQKFTOvrI7bKmApnh97Wtf67785S93hxxySPfjH/+4u/nmm9PLxMVN02mftRHf/va3uy9+8Yvd17/+9dbYs27EBTSj5QeaDnf//Oc/3+TzsgBN9/NPIC677LIVuvzyy9t3Iup37/Oc6bNE1vqFhfqqpnEtsw5W3W233bott9yyO+igg6btd52bJR+lzVztOm8aAzQ/8pGPdKuuump3v/vdr3vsYx/b7brrru0NBX1ncbMK4d6qaGDUFMFUpt3tsLPw2b3lz0033dQqrd9mOoVf/uvQK6zqDv+n6oSFf+4VxmHhnYnjfzVIff+FW/qIh+MvxnWud++wc99871lT8W/c9FEu+uVhKunqnn6ZqrRV3mYjfHUEyTB51rh5WEeQDIbX/8I7mK7O1Xv/+9/fbbDBBt3d73737gEPeEAbzGkDK2xxK99Nlg9zDZo6f/2BvuDqq69uf+vsxT4YjCPXauf12eUP8Z9fzn6cSTiFif9TCdO4EjbPEHbPmCyOOELfOZM4zfXU+SBovve97+3uda97Nfk8CJruce+SAs2LLrqo22effbqXvvSlrTGkJz/5yd3Tn/70brPNNut23HHH7n3ve187I8qrkWYCmTIBnGl4L7744hWFabHJKITV7+Mf/3j3gx/8YNr+fOMb3+he+9rXdp/73Ofa6yBXtkUTaK699trd7rvv3n3rW9/qLrjggtuAmcaYdWb//ffvtt566+65z31u94IXvKB74xvf2CqFcjouGBjIHHrood0uu+zS/HjOc57TvepVr2pWHwMdsDQTp1IZEOy5557dy1/+8u6Zz3xmt/HGG3df+tKXpuyXim+A9JWvfKXbfvvtW7zVjVe/+tXNQqUuzsSB4VNOOaWVp5133rl74Qtf2D3vec9rdfINb3hDs4bpJADiOO7II4/sttpqq+4Zz3hG99SnPvU2khbOZ1MOZtPao2344Ac/2PLxda97XXsP8Dj3KQfKv3iK77Of/ewW/9e85jVtxO9NGzOBMeAHAj/zmc/cLi1IO7fTTju1Bn0cBxw/+tGPtkHYS17yku5Zz3pWC+/rX//6NujUNvfzSR1Tnn/2s5+18r3aaqu1MlOgGbc4LZr9QcZsyFE2e+yxR2tTlEftAO27776tHVNexn22tkL9U061zcqqdvZtb3tb80vfO91wHnXUUd173vOe7ic/+UmrM7MNbdqRY489tjvwwANbHEZdhx20mV/96ldb+6G9D2guYNA8++yzu2222aZbZ511ukc96lGtA33Zy17WOiQwUX8V1C984QstAWpEM1XQBGUPechDWoPrubNx6v3KkI7DwazecarSTtefD3/4ww3q3/GOd7RKtRBA83GPe1zrlFX4wYbWoMQ1OlZlpMoHKHrLW97SGiHlczKLkDDobLfddttuiy22WFHG+LvDDju0RkYZn4k168ILL2wdP+gBsuuuu273L//yLy2tp2oZVU4/+clPtoafX8IK5AyafAavyve4IDjoWM0NOvbaa69WN17xilc0eYa0VfcAuAZiHOj62Mc+1ury4x//+Ha/+l1685vf3AY1k+XTVEBOeyB9QCaL+EYbbdQ6gMmcMKhHBgPbbbddKw8Vb2kLPD/xiU/MaOAh/1g7dLh3utOdGnwru5Ue0vud73xny+NxnE5QfhhwCG+VhRe/+MXts8HIqIGHDvrhD394QHMZT52r62VV7FsEtbnqj5ky7cDee+/d9KlPfWrFW9zGfYayZVkX0Hz3u9/dpH1m1FD/veZwqtbIeguOtuVFL3pRC1MffmdL4FFbAor1J6OuM9N28MEHN6OVAe6ll146tsVU/zOY/nMFmtqtQdAUL/lMPg+CpnuWLGgCTJYsEVSoBUonYLSgAb3nPe/ZrA06VQ1/RUAnI9NdzzpBNT3et1j6X8I96EEPav55Gb04uLdM+hJDnCRu+VfT91OJa01Jeyb/fRYm8r8wD/rlf/f14+Kv/8WjNgFpKITT9Eb5VWnhO/f469n1zHqu+2rnvvioJE960pNWQJrrhLWmS+o5vu+Hyf9TgfSpTJ2PAk3pw+IGXIRZXgqL8mLE/LSnPa2BgYI8EQyxLrEI6+Af/OAHt8bQK7fEyXNZiFhVrQ81pdlv6IW3P1U90XOkFSv8YYcd1qzPb3/721sZHgaaE/mj8rNGrb/++s0qCoLFm7VXeB/2sIe1+uPd8RVe8KXBYKn0d7JpanmtnvHPsRjSXnpIJ42RPNlwww3bM8eZ8tYZyKMPfehDzQLdP6fNaHnQD0BfywKmCnTSR3nadNNNG8yDzE022WQs0BRvHYuOwvU1iFUedLCgTLsEwtW7yitxkLbKUsVlVB72QVN5+/73v9/qwOC5deNaTZX7t771rc0yxNqqflpPpXyJP2u32YCA5vICzYmsjP3pYPUaFNV0b/XpjA76V/2i34b573rtcPUPg9PZ7lNPyqJpQKmc+U7bYeCtHfnABz7QvuuDVj/8Plc/1596V9ZZ4NR1bFDvgHddvULRddXvl/+u8Zvr+s/0ffXFlRb6JfVkv/32a3W1/BxcQoBBDjjggDbbqn2Xrn4Tbs9337DwCEMxgfjUkrWZTL2PAk3tmN/G3QhUco97lyRostQAnnopvMzxHAUUBLE86bxMF+m8BFbmGjlpZH2vIwYKrBHvete7WkdfkPbNb36zPePOd75zg82acvJMHQkw0KEbzbAWABodlr9A5rOf/WyzHhasTRQnDbkpfyM51hLTEcCGBcIUl/jwq9Zfiq/0ZI1405ve1MKpMomzkZXRm4Jd17PMahiMPPlV4KjgC6+/4u950sJf17KAKuQaGmChU/qv//qvZn0yretao06dGP+kB/hjgdEISV/3sPpIz4nSYaJd/9MBTfkjfPJMQyAOIEoFNRUrTI9+9KNbAzfRqy2VB8+RtsqMsAAcfkkbnfiaa67ZgE7ZK2ddG8uRvPXscQCoGnfP8Mz73Oc+U7ZoKhef/vSnWx4pG9KB3yDHNKpB0+abb94aYI0hp4xWGTD9KxyTTc3XoEhZLED1WXrK/0c+8pG3gdnJQNMSGHHWYA2+6mwwb6Sz8qjOWM4wFRBXRjX2prqVAWVEWR0HNMVbWZGmynuBrr/aGPFWRkwfVtoqs6yK0pZ1ZjJgGwRNdZcfg2kyrpMn6oY8rnWZwstKxOJtQMIKG9CMRbPWEapfoEGboL/TFxicVDut3jM6GFQp96OgR99sGY2lbrVUiR/KnjpigGOwxs8yzhRgAQy/Gwh61iBoDoKxQa4+V3us3WP1B3SMUdo8z1EXzBxpOzCAvou0O9qtssKqL37Xvxb4+l7bqk74voxXwqi/1k57puUE+n9hFk9w6F79t7DVsr4CYXDNeOF+YTWjxUBy0kkntTbEc/R98sE1Zor8pg2aroUzoDlF0JSpKsXgNZ6l8IChNdZYozX0wiDTZBQYA4evfOUrm186G8ClQoArBQCQmR77j//4j+YH+DIFZeSiAEnQE088sRUo3xcYggumeoW71g1OBposOaxiwqvzUyDFrfxVcPlVllQVmBneb6BBHGpa1/8qmnVvBc2sZCqDcAGJ2lDF+nS3u92tTQW7R2G29sz/ptakofSQ4Z7H/1VWWaVNcXqOzlCaGcmpnCqRMOlshUf6kenlyUBzNqfOddYaFp2oMEuLmnqtqXCdvgEEi7cByCg4kc/WIImTQYAy3AcanfQTnvCElm4AthzYM+1ZA46pOJXLAEZaTxU05ZfyzVolffpQouHSwIuLMiNfOeUKCGnk3KOSz2TtpjIMNK2LGme3MtAUXmWH9QLYKLOsJcI4uFHnrLPOautuDSKGrWEdlpfyvSyxwmdZDcuk8mkwNA5ojnqOvwYT6r601SYUaCp34iJtxU9ZGxc0y6LNsqP+aXfEXTxmuulIx6pdeeITn9jKWkBzeYKmthC0sPhpYxlR1BEDZ4NR7YLP2teyGuqDtYXWDKu7+ib9JT/66yDdAzTVN36AEc9g9NC/MVBY3jQIu56j7dDXar/1WaOmjoXHc9URcKg/0g9p5/RnBqP6Ns+p6/wmXgw74NB17tOv6jPBH4jWPmgbyxJqoK4PVb/VY+GUVlhEn+17YRYO9V0a6YekMb/Vf9dIP4whjvoJbZn7tH2WH2i/PVs/ID2Bpf+F1199jnZP2upz+K1fGtfKOVXQlB7aS/J5WYKmEcrgNUZcGlIwdP/7378VLgVN4dDoA8+a9hIR3z3lKU9pfupohVWBY01gVRi1RlOnCqB07joa8XOdcK233npttGRqcbI4Ac3VV1+9e+ADH9gKpzB5vk5FJ/DQhz60+SUOvhdeHSSLnFGQ68RZxWWlEGYgBaxlmALp+0HQ5Kd1YDYYKEQFpjoWnabK5vpx1mgaUeq0pKHKJw61a19lkN7zBZoKvryRbho5DYQK0XcaGxsdVG5hG+Xk6/Of//xWlkyPSrs+ZEgrvyuTgLacsqNR8Hey9XSDYDQT0LRIX+PKmqzc95201ECJC+t7rc3TQLH0g61xzivth7c+A1r57ZnKmXKibo5jfZOurMKgRp74azAD8IVJPe3vslam1GEWF43wOI4VT8Ou0zGIAv/yaxA0p7ORhzVXGRVnHaMyUVPnyp0OrjYBTGYtFk9tpo7LbIqlDtoG6WMgqmMUD23suJbNwfwy9c7irq5Ki0ydL0/Q1JZp3xgKgCBjBOu+th/UqB/qcB9gtGXaNAMhfRzYNKC2DEn/oC/sAyFDC3/Bk3baTII+Tpka3JzjPvUDYIApMwQAUP8xKg4gmUWwrIXaNHVPn6jtZ/ABcfoHfZm4aVvVV/ECfOBPvBmOXKdv06bzt07D0LYZ+LpOPGsNKEg0vQ8upYtnqys1Zc8vzxAefupv/K6Nl+bCqM32XN9rl6Q9w5d+qxjK80Ge9rU2IuqfsQbLbX/ZwWyD5kSbgZa1RdNzNO6sajprBUtCCLyAAzDX1FoRGapD569RvmmCcUBToiogtU5R3BQqlYgVT2FgQR0HNFl0dNBAoNZ38Evh18GwvjorUuIDLIXbM3QStXZDGrvGRikVDOQK2yiLJtBUeBRslahAULx1aJYeqKSDoKlSDYKmgu43jY7GwRRlrfOsdaDztRkIVNRGLpZrjeXgMUEg0PSyztT0z0RT0eIEzupoqL6T3n4DGP1p3LIUqPiePRWAGRc0h/mpc1CONWpgou80jMqL8Co/lWbAo44Wma61THyVVdBmhkD45f048QZOGmD5qPwb9BgICKdBkO8NsvpgV0thxGmy9BEfaWqwBTTlk3xRP2Zi0axnWCJgYKe+KPvKfVlhgaP2UNqqc5MdowQeWYGFTd6rr6bqwLgORr5q+6pTm6pTN4RXh29gyP9sBlqeazQBjvqgvvprIKTc6Qv0l9ov7UIfHNU7ddGadNeq8+qT9tQMAwtn37qm/2GMYeFTdv1lNVUfB48D0u/pt/VHwgMchUV9GrVRCcBp71goGTsqvLjAs9RL7Yn6oo3RH2hvhLGsp6aqzURID6CsjdTfau/rWYCc1dZsn1kqYdevgVRQaOArrrX8oDYh8VNY1DmDOvCq3dJvmFEEo2YusQwA97dmJjFHf92oMGuzpZH4MCDpk/Xt+nvx0A/PBDSHHdY+GWi6Z9mCpgR3nc6aeZrVSiEEHdYwloXBbzJLwWIFksHAcxzQlBCeo6IBSn6RcAmfzlzBmixOYM70q4ohs/q/CQtgVcBZcKSjDugRj3hE64xVxEHgA6YqjgqhgkwEmiy+Ovf+bnR+1NFBKn19b/Q2yqIpH0xBSHOVGYzyg9/WpAyzPM8VaFqLpmIDTSAhbCpD32l0WIqAgTSeCPo0BsqFBmsQNDVK0lpjreKPgpH5Ak2NoVGyxm8QnjSqGn1xsfZpcF3rdFwtRVBu5b9yL20Hj5qayKmvOgwdmwGJxkqnYW2Y8qZu8n+6U9u19ENDrwwX8M0ENPkN2ljOAZm6ZXZBvZAm0z3iqCyOOiMDoFq7pt5LA22ReqnsmzKbilPvAYJ8MgjXmWsXRy1vCGgubYumdllfpf1SD7SFAAhsKW/Ky+B0dW2U6W92Ub8McKtvUI8LIpVjA179graaZROcDgJjzUKy8imX6ql+WtkfFX6/6aO1dYw1+uyaYlc3zIgwmJjpEy/lGXjWcUkFmqBQ2+D5nmlQJwz6zXqWuqj9B4Xa0Jqy1wdLO8ap4h3pot8vP91rxkg7hhP0ydpI/b09GD6rzyX/1+kV/fQXVnH2LOEUHnWZlRYfYA9hmi5oYpxhazAn2nVecu+yA02RNcKQ0Y4vYRFQuFQAYACwjOaBhulVQGLjhTV7Oh2ZNRloirxpOwVcQWdFNM3FP8+8733v22CwbxGcCDRZQ2ojz2B8a5OOhsBoUuFykLLCDJIGwVTa1FQGmCjQlB6DoMkfaVKLm0n8VXgWPxVVASeVH6AMA01po0KpKNJDOlgKIF1M5QpDfzf8XIKmDRpARSeprADnfmfKaqTRke/yT8Ud5aSLkbr01CDV1Hk5gKLMyCOVcCpQOBdrNKWTBtbUuIao76S9/BcX8Ow5M3HSUV032GEtfMxjHtPKzUzPkixnykxDrG4MriOciv/WNiqD0sWgsBpc8MkSIC2UIQOUcQ9C11gbPGl8TacpZ8rRdA7Yn8qAREclb8VHGzeuv+qEwZcpRnWS9USHN9Gu/YDm0gZNHbmBBwMG44ByDIZYEmvaXL4P7jgfhE/tCjhlqNFe6eurj2TpUtZM8/JfH6Yc9i11Bpf6Un2NPooByMBzss0uyi+IdI/2oa73XDNqnslP7ZN6ozyLm3avwA0rsErqM/UT6hR4rL0HNXWuX/Y9AworpTBrV/ipPy54rZkszzH41JcKiziBV8YI17qHddAz+jA9mMb9nen81ReaKsc+2nF9E38ZdKT1OMdKTRU0x9GSBE0Wm4lAU0bKaFBkHaNCUpXK2V/WfoAwgKVCGXFZc6LD8bvCMdE5muJgBOYZdssqNAoSv8AGcDTK8LYaIDPZO8ddD5h0HkY+/d/AG3BjhQI60tGCYZCskKug/V3bAEu8jVJBRd+iOQ5oCucgaPYtmtayDgPN8lNhl27SAehoZFhrAYMpi+lMoU8VNAGQcuG50k3l7m8GElb5bWDRf+vJMCef67Br+Sz8/Q5cnggDa5b4zYZTuYyq+6A5Llhp1DSGrF7KeH8dX236EZdxNqZMBs3807ho8IC2gY/OabZeA6q+akQNCkftjB7HqZvWkxn48UsHRPKNRd+mG/Aln9Wvic4XLYujDsJUtkGGNNVJ1e7wuQRNHYp2yrIZHd44TnvMeltQDCzUKR3EROENaC5t0NTW10ktLPO1GUj50G7XesqywtXymjqWqKaJ1dPauKMtLkuo72vjj9/VYf2i+ljXMJzon5RNbR1rYR1ZOFn4y6DkRBhWwNq4ow00la9vBnP6BkCqL9Pui6/rgJv+UBz1qbihlqwIr4FugTCQZezhrz4XT4iHfhWk6/ekSb3QgR+erT3BH2AWL+g3a/e6/kT/UZbLsuzWcYv+92x1VX+sj9Fnu1c6Szd9rf5aHFmZZ7JGE+MwyEwVMt2zpC2a/eONJLKASTgWTB0AU35BogQAP8BRgWG+911tuuEXS0GBJj+Bg92zwNaoqCDJPeKjIuqoVCDPqLMtPV+l07mNC5qAGMSBuarUCr6KB1iFoXa7e561a9KhzuSSMSwqGoW11lqrjUwVcAV3uqAJZlWYCqdKIY10/v0p/jouSTpqOGq9KujQMUpDaV7hn48D26WJBsCIT0MnXCqEsNZaG7vSB8+/HHSu57/BiTKlTGh8+MVPsCHvLFPoWwilhUbb33HeagMwdPosauIij4Gm6WPfAeRxIEYdUCZsHpG/Gm3gpx7qTJQBeaEzqd3lfhcv5WXQ+jvMmXaW38oNOJPGyrDy517hHee1nLUeUTiUR/lb53mqa9JUeK3tkpblameoAcRE1ui+lVdHVidD+EvqEMu7V5ha5iKtdRSj1qgKr7yXTmYfwCoLoc5DuCuP+vlUbyFyj05M2zjZUoRaO64O1Rqo6pQNIgyg/JWfkznpCEhN+bHeCq8yKRyT5VNAc2mD5uBGHOUNhKkvYEn+G0SBLGVY+6YfNBNggK5v8lc7rJ8AlayVVT9N5epXGRy0swDNNDPLIAto7R8wG6L+6eO0S9ov9V07Xf3SqLftgErtPIDU95mxMOBV3hlc9GPqEoONz56lXeG/62uJF2DVpmkH9cGgzppHywv8Jh7YwD3a1IJFAz7P0q9KF+s6pYV768gkceSf5+rL9Mmm8qUtA0zdqz1V1wCudly89WPSTRhr17mwy4f+EoXZON5IuzAMNCfbde4e9y5J0NTp6zxkAiCT+TJIYdKRmrrWSSl4AkyshRpOBVOCiYQKJIGY/VnwCjTLOmj3K2hUqXRqEpJUSKCpozJ96jcZD3wVNH7R4NT5sPMiXSNcpu9VRIWbP8JrHQc4Bnd1cK4OC0TYQAR0VA7hUekBJsuNTlMhld7TmTpX4QdBU0EzHaCzBpB1GLvOrHb1eZa8EFZ5JW1MqQqXgq2SzQdouk+lZ20z9QpKpJGGqXZlj7Lq9Ttenbz4yGtLIzSmOngQrawpL6xkykqtA3W/RsHSAaNYo93JrIMAQ4WVN/w3Qr/3ve/d8ldDSYB4MngzFarSG7QY6WuYlKU6r03ZEP8+UPahfPAIp1EWMnWGtdjSEqN1DV+Fs9ZvDXuXdj/8LIN1zIeyrrwoN/KJBUI9N0jRyOuYyqnzGu5aqziZK5jtH+ZMOjQDCMApbeptSaPSuN6AoX2R58qWsNTLHmppiHJaU/B1pJJ64zmTvf6zdqnrpFgrtG3SRB0QRhvT1Esdkro0mQOZ0lE7pb20tKZe3FBhzhrNgOYw8FR2tSUGSbVmWB+g3CvLxPCg7WCEMBCqqWMDZW2JtkE/yz/lWPsJsMzYqO+mqA2KGVPApv0F9bpV1s86T3Ki6X9Aq990LwOUdl17zZig/6kpeoYOYGeAqP3X3unfxK+WBgirtseztQ3aX225NtNATT/kmeqlNogRQxw9C5OYLXK9ultwVK9u5m8dE8UP/RM/tdPCY+aSP9LY72Xh1OboE7Qdc/lmoFGgOdlmoCULmkY+Cqe1kFVAdawaYdYPmWekAi7qqCLSyBr5KAwKGfip97Ty09ti+qCp4jCp13EtKpUCU8cl+aswKtz+KoyATqEHdUBw3DWapu5UAFBba+iER+HV0YLL6sBrY0BZk1wr7gqr5+ogVJ7BczSnukZzEDRrd7B0kB4qhrgajckXHZnvKizC5X8ds9FcHbc01TwXHvA9FdBkdVNJdfKASGUWbmHzWWMnPpNN8wKGOuC64sefkjzQmEnrshC5nuXIAEEj059uH+U0wvyRRyytjvm5wx3usOIViaRxLkvURFZCeS5PDFKEsToFZUkDDOrUy/JHvtTZr8M2PA06ZUcDrHx4TaawCnOFk+S56eWJzuSURxom8apyXGGtFynorEBxf3e5ssCKoL6Ms4lHPEEi9d86JB0MmjyLpaLeQjRqClu8DTi1PXe5y13alLv2R13Q/lBNrykPZX1VZ6TtOO8oV3YMmJXPfllTn7RN2hiduvIy0XrQmuLXthiw3PGOd2yzLwbOwiuPhFfnPOws0oDm8gbN6sPrrXXaFJ8BFvgDUiWDd4PDepOOaw3E9bd1UHodp2TQq6+omS+DcBZDbYXBlZnGksHlZEf21C54YXCPNh2UaTPqbX4Vl5rxZF1Vx1wLkGq3eIURKHu2+/U9wsuQ4DvAXSAEvCuOrmXNlTbi1N8pD5Y8l7/9NNJn8VNYSLj4I40LrrTF0qlOlglozhNo1kYJIxP0D/CMPnQYOlGAqeOSaRrjwU1CRvhGVTpK019AUkcAwExz15lb/fM4mdLBqJGb0QpwU+AUQo00qDC68WyNs44AWIE05v9xd52Lk3v4p0PRmfJPJawF1rUeEhwA6XrLCRO++8Wh1pD0z/sEEPUGhGo06t3aCrc8quvLygtwQWj/nekqs07QM6WJNKzXEJousKtPWACojpUf0lbFn26ejwJNFh4wD6LluXj3wVFnqwGsPJKeYBEI1Ftrxt38IX3kgxE4P6qsgZX+cTb1jnWABGo1gOOsWeSHdDQQqqndQYHH/msMRzmAoa5ZM6w8CatBhbJbjUH/WtZ4kMdqrvEcPHN02HICVoSJwqo+KHcTHcFTO9bVSfVQuspL4WUpMIAYzFMO7NUZe+NMH49yOghlQ32VT5NtNtL41VFGo+KtvNeRZv01vuLmWZNBfG0yAtDSQFpIE/VUvTPlpq5NtulI+KWvtmpUWEk97W9iKysW6wtoNphQ7sYZLMUtLdCMlqamCpqT7TpfkqA5kSZ6jeFU76t3fM9HWICmhcs6EiOb2Y7fQknnUf5Mls6jps516KYwrUlloTF4WNmWF2EExfIUCE12WPvKdGCk3uXOOmgAN5PNLPPlDOJYQEFobfJaqI4Vg0VdedD+TbTRaCE41huDVbMplllolwxY6k1HcQHNaHmB5mTShywr0FysKtBk1ZkINJejrv/t77rTR4CmcsWiZQqERWrYwezz7WpXO+vvZMfHzDVEjhtedbTObJzqsUSzcYzRVP1kJax3LE+2Q3xlp7OGuM4IHbZmdaE5dUzZNfhgvTW1Z/p/ZZXjuIBmNH+gCRr7B7EHNJcoaFrzkjTpaQLQXMiWwoUQhoUAWivTz8Ua/4VSfuerDMUFNKOVD5rWm1pONhWrpmvd496A5gKXjTfW81loa21p0mS8qfPlCgPjxCeAsLgGGvOVXwvBYh03P6A52YHnUUBzEDR9P1XQdE9AcxHIpgSbdJifbTJKmswuaMbKuTjiFqhJ2Y2LRTOaf9C0IbOWI03VP/e4lx8BzSigmc46oBmXfAloRgHNFaBZp+tYl+0zsAOP48i17nGqDD+mc052QHMB79heihq10385TZ3PJKxLCQgCNzNPt6RhQDOKJgNNR5fZVOuYQ7OqYBHoYbXqk4epzuN2j3ttyMV4Ac1oUarO0TSCWhm7dtNhJ83i4gKa0VJQvdJWn6pvBXVOxbDO0kkTNiQ7L3vwAP1hcq173FsH0s8TaN4c0IxmVc72U+it/7D4eL4hJrtvA5pxcYvJOfYLUERRXzbw4LOaLmeNLLBzrBlgLOvmOHKte+r12DM5ezygGa1UOR+zXhfGzG+07k08GtP50LBnzeT58xn2KIqWj9K2RBOVDX2nPlRf6oQbfetCXEIX0IzmXcqP1zrW4ewqSBRFURRFU1O9D16f2n/1dEAzWtZSZozCzj///La7jcne6wejKIqiKBpP+k59qL5Un7oQeSygGUVRFEVRFAU0oyiKoiiKooBmFEVRFEVRFNAMaEZRFEVRFEUBzSiKoiiKoiigGUVRFEVRFAU0A5pRFEVRFEVRQDOKoiiKoigKaEZRFEVRFEUBzYBmFEVRFEVRFNCMoiiKoiiKAppRFEVRFEVRQDOgGUVRFEVRFAU0oyiKoiiKooBmFEUrWeroVVdd1V1yySXd+eef35133nlRFEXREtBFF13UXXbZZd21117bIC6gGUXRvFdy9fScc87pTjjhhO6oo47qDjvssCiKomiR6/DDD++OPfbY7uSTT+4uv/zy7rrrrrsd4JHvx1FdP5vAGtCMoiWua665pjvrrLO60047rTv77LPb6FfdVeGjKIqixSntuFkqVs3TTz+9+/nPf96+A3Kg0Wff/ehHP+pOPPHEZmiYSK5xbfnDj9kAzoBmFC1xqas//OEPuzPOOKPVV/X35ptv7m655ZYoiqJoEeumm25qDAbggKKZq4JMBoaf/exn3amnntoMDePItaecckq7t2BznkDzpoDmItX111+fuC2hKfDpjC6vvPLK7rvf/W5bm/nXv/61+8c//tGtLOfZkz1/nGvi4uLi4v6f+/vf/97dcMMN3fe+971m2bz66qu7n/70pw0aL7300mZY0Pa7biK5xrVgkFWTH/wKaC5REJlJ2Gcr3ssN5FZWvo5K59laIwM0jznmmO6CCy5Y8JAZ0IyLi4ubetuK0Y477rgGmjZ+Hn/88W2p1I033tj9+c9/7v72t7+NJde6x7384FdAMwogRmOD5spuDOfi2ri4uLjl7sAh0DT9XbNY1m7+5S9/afA4FbnHvfzgV0AzO4pvZ/maDmhOZFVb2ccljArDQgbqfphXdjinCppzAXkBx7i4uLj5A01tPlg0Hf7HP/6x+8Mf/jCWXOse9/JjSYHmQjz7aWWHcdTzFkNaRcvLojkTkAyExsXFxc0NaLJOThU0y6IZ0FwmoDnsmQHNKKAZFxcXFxfQzO7fGT3DsQJ2fFmMO1dpLPPloefUMQYLZcp3utP+KzPc0lDduOKKK5rk3ajDbycqR+5x/qX7+aOye+vDdMrBQlmjGbCMi4uLW/mgieHqYHafA5rT3LG72FXHE+y7777dzjvv3J177rlz8hznK+6///7dO97xju7oo49elJubFlIZ8IaGPfbYo3vBC17QPfe5z+1e85rXtGMiQONU/HHPpz71qe6Vr3xlt+mmm3YveclL2hsgHM67WHedx8XFxcXNL2ja3DMIml/72te6F7/4xU0+D4Kme5YcaFaiHHHEEQ18+D0df1h+dMb8OvPMM6fcuS+06U7Qss0223SPfexj26Gr0wXWn/zkJy1Nvv/979/uqAJvA3jf+97X7b777i3tFkv6VF7bFedg2YWS11/5yle6F77whd16663XPec5z2kAryyyRk7FH/d89atf7d7ylre0/F911VW7T3/609MacEwEmv5XRk466aTuW9/6VvelL32pPdf1F198cXfrrbe289XGcYBf43TkkUe2dODXoYce2g6K95sF5vVMDZkz3j772c/eTl/4whda3mpn4uLi4uKmB5r6i2Gg+d73vre7173u1eTzMNB075ICTSfZAypWINYgHeI41qrBqUcd2vOf//zml87KQaWL5bigwbjMFmh63eC73vWubuutt27WtUFQcYi3Q15BvvRbLKA5V3k9G6D58pe/vNthhx0auM1G2XvrW9/aPfjBD+4+85nPzBg0ByHzT3/6U7Nks5w+8pGPbA3P/e53v+6pT31q9/GPf7zVc43ORI4/zl/TwGm0nvCEJ3SrrLJKd8973rNbc801W51Wfh0EXNdqQ970pjd1//RP/9T98z//c/ev//qv3b/927813fWud+2e9KQnNdCNi4uLi5s+aGrjB0HzQx/6UPeQhzykyedB0HTPkgNNB4OOAs1Rne8w+JsL+Bi2hk58Z+sdoONYNFnHZgqa/BkGmvLvsssua5k/XUvyYgHN+dhENRloTnUZQIGmBmG2QVOd9v0uu+zSbbzxxt0HPvCBFn5WxZe97GXdlltu2b373e9u1uJhcFlOoyT9lTOAyDourCyjPpueMdBh7WQhZdn0FgugCWwtDzn44IObJZSA749//OMGpnFxcXFxswuauMC15POyBU1+Wo8GInTYP/jBD1pnZDruoIMO6r7+9a93J598cguHa3ViX/ziF7vHPe5x3QYbbNDtuOOO3YEHHtg6O8AmsVxnytVL4/mlM/zkJz/Z/NO5SfD+FKz3fVq/UNP6PnuGa62fdGr+UUcd1aYHDzvssOanjpp/vndC/+CULqhz7ze/+c32/E984hMtjsIoDjXFOsqiCXCliylvaWEdHz/45fmuMxUqrp5lanmrrbZqabLJJpt0H/zgB7vPfe5z7Vpw4Jmm1HXwpmv778dW0ExhCp90AiDf/va3W/j9XlZYYZIWwuLvd77zne7LX/5ySyt/+c06Nc70tvyRbvJcOMWNPv/5z7c8UFCFayp5zS9hnyyvlSlh95xvfOMbLS+F/5BDDmlpM06Fmwg0hUm6ST/PqvwXLmnsPbXip0L275sr0FROTM1vttlm3Ste8Yq2LlQYhYE1U30EoPJD4zMKNLUHyoVy9sQnPrHVBc9UNsRp22237R796Ee39PDMAs03vvGNzVKrLPvfAvTSyn5VZlxcXNxiB02zxQb34+44L7nHvUseNEEAANIRPfnJT26Wkac//endYx7zmDYdBzJYW9zrWh0dq4npujvf+c7dfe5zn2611VZrFpay5FUnr3N92tOe1j3oQQ/q7nvf+3ZrrbVWs96AKkBU4dpvv/2aNZF1x7q7DTfcsHWY22+/feuIX//617dNGsIl7M94xjNauFyzxRZbdB/72Mca0EifSmjALNzi5Pn3vve925SlqUuQIhNcPwo0rbGULqxB0sI0Jz/4tdFGGzWQFFfACp7e//73d494xCO6u9zlLt3d7na3lp4Pf/jDW1hrXd6rX/3qZnUCc1UoFDIgy2oofNLTvc961rO697znPW1dZO2Clq6vfe1ru//8z/9s1jH5sP7667d8WmeddVragc0qYBOVBev2PvrRj7b8AVcsXuInDptvvnmzZM5lXsu3F73oRW3q2BSwtJdWgFAFniz8o0DTfeqKdGPBA2QPfOADW9xMM0tjZV/8XTcfoCn+yqG8F27WU85UuXImL4VRehtMjHLgsSyX7nFvH0hNzSgLe+21VyvHps4LNOWLwRD/WTA1ckbTrgloxsXFxc0+aE6063zZguYaa6zROjAWRRYnnSIwe9WrXtVASocpLCyLOk5goONjNZLwgEs4wc5uu+3WnuN3/rAsWlfG6vLSl760WcoqPjpIzwaOng8cWCpZUm2wedvb3tYsaiAIPLISCh9rmOtBGSsbq6HngwjxAxlvfvObmyVLGExZgigWR9Yz8ZkINFmAWNrAjzgSqOUHiDKFCXD5YXPVTjvt1NKE5YrlTBxYHln8QEQfNMXdMw444IAWHs+3WagscAAKWPtOGvRB89///d/bBhh5Ih1IugiX31kKJ5vCliZ2WQNGYRA+aSLcrJGssCy6ldfCOJjXwGeyvBYm/tUgAGjKa0C06667NqucSga23/72t7c4g3hpNmhxHBc05au83m677ZoFkZVVPFwnfgWDdczUXIImqyFLpTgLL8vvTTfd1H6z+YcFU7zVP4OqidZLajz4of5Io0EoFTfppxwq033QvNOd7tTy22BOvqgX8lJjBDjj4uLi4mYXNCfadb5sQZNlSQd24YUXrphS9z+o0kGxEk20bs89wgmA+AViwAtoYJUDjgBg7bXXbp26+yQK0GQBA4Yf/vCHWyJVXIXFjmJWTlY7wFD38Zd/ph1Z1GS+aWwQy2LG8gZG+SEdQeTrXve65g8/WT1HgaZ0EV+FSUEAVeImDcUPTD772c9uEDHOGk1A1QdNfnmW7x71qEc1AGMZ9D3YkA7AgNUPhPVBk8UU/APKOjsS8AE3QAhYJjs/krV09dVXb5DoXn4DF2VNHIXDvf28Fu6p5jVrsIGC+/gvnvLaPeJY0++eawDB2gmUrB2caC3rRFPnBhwFv6zi4K7Ox1SmPaueO9egqTExUOCvQRt/fdd3ZQ0H58rcKKfhMMAA75Y7yJ++A/jKi3RR3oAmqDVQYm0GmNJLONRn6Sw/pMlgmOLi4uLixgfNYYe1T7TrvNZpLjvQtPYLIICqWscHZnRQrFoshaNAU4R05CBJB3b/+9+/wYBOXwIS/1j4WLOAkmeJF9B8/OMf36yTrID9MBdoAjsdbH99I7HEeZ7fdb4FGaba/fV//3rhFX+/s1ZOtOtceouT9Kg4yAuAakoZIIrDIGiKx2SgCYgBJOsUiLZurw+FrKkATVqBwj5omsYXVzvZ++dBArd1112322effW63y35w80tBoDxn3VRp5KNn1FT9IGj2BxWgcap5LR2BjWUPziwFuP1w1bOAknWoKsp0QFO5Bo3KRE1XGxAIUwH1sPfWzwVoapTkpfpljSuoM4rtuxpoKT/yYZRT9lgs1QNx8kyupr7VBQMsZVua1G539V499lc5ZY2WD095ylO6hz70oW0wJl3i4uLi4qYOmtrWYaA50a7zAk33LivQBAT9sxIrEU0/AsGCMLCnEx+EDyDhGhDm+BSWRp0ei2iJRcYaPtfILNPHMoA1E1gJS39XcIGmztVUKPrvx8m0sTWcLDbWHIqj8Lp+7733bh1r/3pWM+Bsut15ieJo7dqwzUCezdxt6hGQgRYg9MxnPrOll04adLp2FGhWPAZBU57aWCOc/Bw88kg4wKDn7LnnnrcBTWc9mvbkRz0DSJkCB3biPVlZYOVjrZYWDjwXJ2EDWyxjl19++dBBBUCuvAYsU81rgCOvTbP7brB8upa1Tdz7azunAprSyVIGlkLpKxysz8InLW1Sqs1t82HRtEbX2laWxBr59p3lEQ972MPaX2kwyqlnZdEUxmEWTet0lVVrYznT8xpGZdRf6zNNp8tHyy1Y/dU7A4G4uLi4uOmB5rDNPhiCIYt8HnbNsgNN04ysZKN2ZAOLPmgCuj5o1vpI19sUA1xMqbIslkzvsuxY68gK5x7wocMDSMBtmEWzjm4Rh/7v/SljfurIAbPO2H3u71+vMxY+HbbOehRoihOr4Rve8IYGNMLMCgds/G/aWZqx2kkvoKnjHteiqRCY4i4Qqin4kml04AFQbO7og+YDHvCABlcFfQWEwJWVVbzH2XUuriyHlh3UdKrwKQfipbDytz+oqGfKN/k3UV4L67C8Nm1uCYPK1Q+TQQ6LHEh13XRBs9LKTmxpYkOX+AFNaW1N5LBD9edqjab6Yv2x9GXd7K/RvOWWWxr8KksGPsrFKCc/1DlWcBudlLu+U15trpIP4jeZs3bWmmJ5pJ7HxcXFxc0eaI6jZQmadfSPAA4DzZpOZTnR6enc+lPnpgZ1dPwDAPVbJUKdjdmPyzigab0iKyQ4qnv9BRw6cGvTrM0EyjY66IyFA7z0d6ODiOc973ntHtbKUVPnwI8VjLWvdt2Ln/xg/WE1EkdpJw2BpilraQIoJwNNFkNWyVpLah1fTekKr4IsHqa3QYWwTwSawjsRaA6u1+Rf5YPvQRcrJwsnuGVlNmgYltf9458qr63P7Od1/xzUwbyuEwZM2fbzBlyzTEuT/nrQqYJmHQXVf/e5tGLJVI7A5jBr92yApkHL4K5z9ddASN5LQ+ECmabQNQbKhedaNiK8ddi6XelUU+PaAJAMmMGhOPGHXMeCawpeeVUeyp+S/6mul8Ys2QZQyltcXFxc3NRBE2cwGkx117l73LvkQBMw9EETPLFeDYLmqDMmBVzC+A4ssLqV5clvoMtaQesOWZGsgxRhkfcbOPE8Vp4CK1BjV/lEoMkKZnrxIx/5SMtgU7f+mta2uYdViLUIwAEYVjFhBiTiCaTAnE7V9DIoNKU5CjRBFusQOGBpEm5xEB4dunWGfdCsqXAAWscD9aFrEDTdIw1YY20EER4bYMTLMywctoYOdAHiySyak4FmvyDyS9op3OIlLMqXMAujY5mkMz9ZiJWZyfKaFdma08rrGnQM5jXQZA0WZ2XQb7Xkwm+ebd2n/wePH5rK1DlrJnAVHs/2nbLFOktgbC5Ac9jxRtJJ2VbvDICEq87RFGcDHwAsrQsu63gtcTPVXQ0ckJUfYN0yEJZp6eq5yp6Ndqz88sD6TPE3+Kp3t1ebUlZ85cWSi8Ewx8XFxcXNDDQZkxgGyOdlA5qDFs2pgqbvwQboYP0CdNbT1XE+Oj6ZYLoXnAAjnSkIImBic4u1lWU5HQc0rQEEiEAIhAEh1h0gZg2hDrkOB5dxnmFa0PNZBD3XWjTXgz1hlRGj4igdQKDpetPn9QYWR+WYigW3fdAUb366R1g939Q4IKojoQo0pZV4Ayz++s7zgY541dFAAMS607Li9kGTBXG6oKkgAl9lQN65z3N8BjHSU3jBCQHrymufJ8trftGwvPa7fLRpSZz5B3QMFGpJg6n2YbvCh4GmMjsImiyh8kl8rEOtsics4mZa3xIKlXE+QNNUOWux9GaVl17yWdyVR2uPhU++cBofAx1rfgG8dOYKQPfff//mj3jwg1/KHdD0nfrpWusxQa0p8kpn+SIPLF0wIFK+ha02FsXFxcXFzQ5oTrbrfMmBZh0OzgIEGMBITZ2z/uj8rS3sr9HUqXk/t2k6h5TXrnOAxDoIuByA7SBs5wQChZpilnisdMBB5+3Qb9d5jqlYls6yculkTS3KCFa9wVc7CiuoAMiAhnXT4eEOoWYhAhU1zVqbb3SwOlRWSde5HoSxloGogozBOCo8/JDuIFKHzEpUb8cBf6xh4iUu0k58K3NBgPCBQYXLJioduWfatAQG6tDuAmmgJB7CVwe2e47w19ubCjRNp7MI2uxRG3Zq3azd42C5v+t8VEFkpZU2nuWZ9Vx5Iz9qo9FgXtvcI5zTzev+rnPl0GfXus80ruUPymmlz6gjmgo0ATD/pU2duwm0gJXwyPs6jJ61VHiAV20GKku7ciCOlg1MBzSFERAOA03OdDWLuzqmrgiPTV0smTXw8JaeasiUSWXdgEYZ6Tt1FDAqr3b8i5/rgKlw12YjgGvgptzVM+9xj3u0smlDFuCVt3kFZVxcXNzsg+Zku86XpEUTNFhfCHxYwMpPwFKve6zXKtYaO1Yz9+j4avNEnWFpgwfLFutYvTe5OnD+gB+dq6NVHL1S1+n8dJ4FEDpHVlUJzt9hFk2WFzAIsFgg+cdfu4v7cNzf7AKEhLueX3GsaXAwM1EcfW9dKoucqUaFC1iaYvbX99Ku1hmCKZAkfMLpmT7LdM9kmZRG/Q1K8sBv4iF8FS/+98+zrAIE9l2nTPQPHJfu0kE4B4+AGlYYXQukK23qufKm3ng0Kq/97ee1v8PyWvqIl8FC5XXtOmddNtUuvK51nzIA0up4rYlUyyO8Gcm6TjCl4gt3WbX74SHhrrJX6zelp4GKsgXWwDTw6x8dNRWLpji7d9jbdird1T9pL3yer8z112ICTukqDsNAEEAqU8omP/jlOmXCb9Zjlj913FM9k5Qt5V25UwcKcOPi4uLiZg80J9t1viRBc6LADfs8U79mQwWaLGjDzsWciQbPllzsEp/5itM4+TwsPECTZZj1DYRO9/nAiuXVsgbTxv0NW1PxRyUHrcDX0gbHLgGxvrV4tkAzLi4uLm5pgma92ncqcs+yAc2FpMGp0po6nwvQjOZfBZrD1uMudo1aoxkXFxcXt3RB04ye2aRhVs1Rcq173BvQXACgadPPMNAsi9lSs0wGNAOacXFxcXEBzYDmPEzLSjA7hO3gtlGkv9N6KU5/L3U5egdk2gA1+JangGZcXFxc3GICzdoXgd2A41TkHvfyI6AZRVFAMy4uLi6geRvQtNGn3uznzOOpqI4m5EdAcxFuikk6JB8WImhmk1BcXFzc0gBNfOZEj/5pNgCyz3DD5BrXusfpIXWKS0AziqKxX0E5CigDmnFxcXGL02m/MRrQZIl0yonjAB3/5/hCy8GcOqIPmEiuca17HHHXf1FOQHOeNwKN+1sULRTQNAWi0fjLX/4SqIyLi4tbQs4Zxdp6Z27Xa6YBojbfsXh11nSd3TxKdR61ewoyZ4NvApor+UzHKJprqaumQIx0fb711lsDm3FxcXFLwHm7m+Vg3jxXLzYp/sBnzl4Gd3htHLnWPfVSltlioTFA8+aAZkAzWqSqaRSgaSeheqshUemjKIqixSsn3pjytjZTG1+vvV5oLBTQjKIlPuCpaRSvhhx3GiWKoihauNKO1+uvbfzxWmZAF9CMomilVHTvap/qNEoURVG0sMWqaS3+ddddtyBnUgOaURRFURRFUUAziqIoiqIoCmhGURRFURRFAc2AZhRFURRFURTQjKIoiqIoigKaURRFURRFUUAzoBlFURRFURQFNKMoiqIoiqKAZhRFURRFURTQDGhGURRFURRFAc0oiqIoiqIooBlFURRFURQFNAOaURRFURRF0ayC5nXXXTd90Lz66qsbqSYxoyiKoiiKolkBzauuuqq7+OKLA5pRFEVRFEXRhKB5+eWXd5dccsn4oHnllVd2F1xwQft77bXXtunzKIqiKIqiKCoxRjJKsmZedNFF7buxQPOKK67ozjvvvHYj2LRW85prrmnQGUVRFEVRFC1fYUICmayZIPPCCy8cHzR5ctlll7Wb3Gy9Jui89NJLoyiKoiiKomUubGiZpRlwny27NJ0+Fmi6EGxa3Ak4/WXljKIoiqIoiiJsyJqJE20iZ8284YYbxgPNKIqiKIqiKJqOAppRFEVRFEVRQDOKoiiKoihafKD5fwGsPsFV9W8mDwAAAABJRU5ErkJggg==\")
\n",
- "\n",
- "\n",
- "Data points parameter (N-by-2 matrix) and Interpolation step parameter correspond to XYData and DeltaX below, respectively.\n",
- "\n",
- "\n",
- "Create a Signal Designer object for a continuous signal trace.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "source": [
- "smoothSignal = SignalDesigner(\"Continuous\");"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Set an N-by-2 matrix for data points characterizing a continuous signal trace. In each row, first element and second element are X and Y values, respectively, defining a point (X, Y). In Continuous block, X axis represents time. For smoothing, X is treated as an independent variable. Y is treated as a function of X.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "source": [
- "smoothSignal.XYData = [\n",
- " 0 0 ; ... (0, 0)\n",
- " 1 0 ; ... (1, 0)\n",
- " 2 5 ; ... (2, 5)\n",
- " 4 2 ; ... (4, 2)\n",
- " 5 2 ]; % (5, 2)"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Set a resolution of interpolation.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "source": [
- "smoothSignal.DeltaX = 0.05;"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Call the update function to generate a signal trace data set in the Signal Designer object.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "source": [
- "update(smoothSignal)"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Visualize the result.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "source": [
- "plotDataPoints(smoothSignal);"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 12,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "As you can see, the Continuous signal type does not produce flat segments. However, you can do so by repeating the same Y value in at least three consecutive rows. This is possible because of the interpolation algorithm used which is modified Akima interpolation.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "source": [
- "smoothSignal.XYData = [\n",
- " 0 0 ;\n",
- " 0.5 0 ; % Added\n",
- " 1 0 ;\n",
- " 2 5 ;\n",
- " 4 2 ;\n",
- " 4.5 2 ; % Added\n",
- " 5 2 ];\n",
- "update(smoothSignal)\n",
- "plotDataPoints(smoothSignal);"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 13,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "You can use Continuous type in place of Continuous Multi-Step type if you can specify flat segments explicitly as presented here. However, unlike Continuous Multi-Step type, redundant data points in flat segments are not removed in Continuous type because there is not enough information to do so robustly.\n",
- "\n",
- "\n",
- "## Piece-Wise Constant\n",
- "\n",
- "Piece-Wise Constant block block has one parameter:\n",
- "\n",
- "![\"image_3.png\"](\"data:image/png;base64,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\")
\n",
- "\n",
- "\n",
- "Data points parameter (N-by-2 matrix) corresponds to XYData below.\n",
- "\n",
- "\n",
- "Create a Signal Designer object to create a picewise constant signal trace.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "source": [
- "stepSignal = SignalDesigner(\"PieceWiseConstant\");"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Set an N-by-2 matrix for data points characterizing a step signal trace. In each row, first element and second element are X and Y values, respectively, defining a point (X, Y). X is treated as an independent variable. In Piece-Wise Constant block, X axis represents time. Y is treated as a function of X.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "source": [
- "stepSignal.XYData = [\n",
- " 0 1 ; ... a constant segment from 0 in X, of value 1 in Y\n",
- " 2 3 ; ... a constant segment from 2 in X, of value 3 in Y\n",
- " 4 2 ; ... a constnat segment from 4 in X, of value 2 in Y\n",
- " 5 2 ]; % a constnat segment from 5 in X, of value 2 in Y\n",
- "update(stepSignal)\n",
- "fig = plotDataPoints(stepSignal);\n",
- "fig.Position(3:4) = [500 300]; % width height"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 15,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Trace Generator\n",
- "\n",
- "Trace Generator block is an application of Continuous Multi-Step block. In Trace Generatgor block, signal trace is parameterized in a higher-level manner than Continuous Multi-Step block, and some parameters are randomized to generate a specific signal trace. The block internally uses SignalDesignUtility.buildXYData function in addition to Signal Designer.\n",
- "\n",
- "\n",
- "Trace Generator block has block parameters as follows.\n",
- "\n",
- 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\n",
- "\n",
- "\n",
- " **Random seed** is used to randomly generate data from other block parameters which Signal Designer can take.\n",
- "\n",
- "\n",
- " **Interpolation step** is used to generate Continuous Multi-Step signal trace.\n",
- "\n",
- "\n",
- " **Initial constant duration** and **Initial data value** specify the initial value and duration of signal trace.\n",
- "\n",
- "\n",
- "**Number of transitions** specifies the number of flat segments excluding the initial and final flat segments.\n",
- "\n",
- "\n",
- "**Range of transition duration** and **Range of constant duration** specify value range to generate random numbers for transition and constant durations, respectively.\n",
- "\n",
- "\n",
- " **Range of data value** speicifies value range to randomly generate data values.\n",
- "\n",
- "\n",
- " **Final transition duration** specifies duration between the final flat segment and the one before.\n",
- "\n",
- "\n",
- " **Final constant duration** and **Final data value** speicify the duration and value of final flat segment, respectively.\n",
- "\n",
- "\n",
- " **Advanced** section has parameters as follows.\n",
- "\n",
- "\n",
- " **Data value scaler** is used to devide the data value that are normally generated. For example, if Data value scaler parameter is 1000, a data value normally generated to be 1 is converted to 0.001 by the scaler.\n",
- "\n",
- "\n",
- "**Time scaler** is used to devide the time points normally generated. This works the same way as Data value scaler, but for time.\n",
- "\n",
- "\n",
- "Code below corresponds to what Trace Generator block does in the block.\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "source": [
- "xydata = SignalDesignUtility.buildXYData( ...\n",
- " RandomSeed = 5, ... Random seed\n",
- " XInitialFlatLength = 3, ... Initial constant duration\n",
- " YInitialValue = 0, ...Initial data value\n",
- " NumTransitions = 2, ... Number of transitions\n",
- " TransitionRange = [ 2 5 ], ... Range of transition duration\n",
- " FlatRange = [ 5 10 ], ... Range of constant duration\n",
- " YRange = [ -5 10 ], ... Range of data value\n",
- " XFinalTransitionLength = 3, ... Final transition duration\n",
- " XFinalFlatLength = 4, ... Final constant duration\n",
- " YFinalValue = 0, ... Final data value\n",
- " XScale = 1, ... Data value scaler\n",
- " YScale = 1 ); % Time scaler\n",
- "\n",
- "sig = SignalDesigner(\"ContinuousMultiStep\");\n",
- "sig.XYData = xydata;\n",
- "sig.DeltaX = 0.1; % Interpolation step\n",
- "\n",
- "update(sig)\n",
- "\n",
- "plotDataPoints(sig);"
- ],
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "execution_count": 16,
- "output_type": "execute_result"
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "*Copyright 2022-2023 The MathWorks, Inc.*\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "MATLAB (matlabkernel)",
- "language": "matlab",
- "name": "matlab"
- },
- "language_info": {
- "file_extension": ".m",
- "mimetype": "text/matlab",
- "name": "matlab",
- "nbconvert_exporter": "matlab",
- "pygments_lexer": "matlab",
- "version": "23.2.0.2459199"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
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BatteryHV_refsub_Basic/Battery Status/IV Status/Gain
@@ -2665,7 +2671,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/Gain
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/I^2
@@ -2673,7 +2679,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/I^2
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Add
@@ -2681,7 +2687,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Add
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Constant
@@ -2689,7 +2695,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Constant
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Divide
@@ -2697,7 +2703,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Divide
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Saturation
@@ -2705,7 +2711,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/PS Saturation
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2715,7 +2721,7 @@ Converter1
Converter1
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2725,7 +2731,7 @@ Converter2
Converter2
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2735,7 +2741,7 @@ Converter3
Converter3
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2745,7 +2751,7 @@ Converter4
Converter4
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2755,7 +2761,7 @@ Converter6
Converter6
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/PS-Simulink
@@ -2765,7 +2771,7 @@ Converter
Converter
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/Power
@@ -2773,7 +2779,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/Power
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Battery Status/IV Status/R
@@ -2781,7 +2787,7 @@ Converter
BatteryHV_refsub_Basic/Battery Status/IV Status/R
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Current Sensor
@@ -2789,7 +2795,7 @@ Converter
BatteryHV_refsub_Basic/Current Sensor
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/DC Voltage Source
@@ -2797,7 +2803,7 @@ Converter
BatteryHV_refsub_Basic/DC Voltage Source
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Ground
@@ -2805,7 +2811,7 @@ Converter
BatteryHV_refsub_Basic/Ground
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Internal Resistance
@@ -2813,7 +2819,7 @@ Converter
BatteryHV_refsub_Basic/Internal Resistance
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_Basic/Voltage Sensor
@@ -2821,7 +2827,7 @@ Converter
BatteryHV_refsub_Basic/Voltage Sensor
Block
-
+
MATLABFile,FunctionCall
12
Line
@@ -2839,15 +2845,15 @@ Converter
Line
37
-
+
Toolbox
Toolbox
-
+
Toolbox
Toolbox
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Ambient Thermal Mass
@@ -2855,7 +2861,7 @@ Converter
BatteryHV_refsub_System/Ambient Thermal Mass
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/Charge
@@ -2863,7 +2869,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/Charge
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/Gain
@@ -2871,7 +2877,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/Gain
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/I^2
@@ -2879,7 +2885,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/I^2
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS Add
@@ -2887,7 +2893,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/PS Add
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS Constant
@@ -2895,7 +2901,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/PS Constant
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS Divide
@@ -2903,7 +2909,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/PS Divide
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS Saturation
@@ -2911,7 +2917,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/PS Saturation
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2921,7 +2927,7 @@ Converter1
Converter1
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2931,7 +2937,7 @@ Converter2
Converter2
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2941,7 +2947,7 @@ Converter3
Converter3
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2951,7 +2957,7 @@ Converter4
Converter4
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2961,7 +2967,7 @@ Converter6
Converter6
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/PS-Simulink
@@ -2971,7 +2977,7 @@ Converter
Converter
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/Power
@@ -2979,7 +2985,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/Power
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/IV Status/R
@@ -2987,7 +2993,7 @@ Converter
BatteryHV_refsub_System/Battery Status/IV Status/R
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/PS-Simulink
@@ -2997,7 +3003,7 @@ Converter5
Converter5
Block
-
+
Toolbox
LibraryLink
BatteryHV_refsub_System/Battery Status/PS-Simulink
@@ -3007,7 +3013,7 @@ Converter7
Converter7
Block
-
+