diff --git a/Documentation/User Manual Source/Release Notes Vecto3.x.pdf b/Documentation/User Manual Source/Release Notes Vecto3.x.pdf
index 60d6f2363f6826accfe8ee8eef0267df576d3d6a..4df7d3f6eb9d833d7d9a745eb8fc8bebf1aa5d7c 100644
Binary files a/Documentation/User Manual Source/Release Notes Vecto3.x.pdf and b/Documentation/User Manual Source/Release Notes Vecto3.x.pdf differ
diff --git a/Documentation/User Manual/help.html b/Documentation/User Manual/help.html
index 371b472a23b31ddf22efce3f08686959a6dce823..2060d6c710de62878ba55310ea5053bb2ab575c4 100644
--- a/Documentation/User Manual/help.html	
+++ b/Documentation/User Manual/help.html	
@@ -3045,12 +3045,12 @@ Example: “Gears\Gear1.vtlm” points to the “Gears” subdirectory of the Ge
 <p>The file is described <a href="#torque-converter-characteristics-.vtcc">here</a>.</p>
 <p>This file defines the torque converter characteristics as described in VDI 2153:</p>
 <ul>
-<li><strong>Speed Ratio</strong> (ν) = Output Speed / Input Speed</li>
-<li><strong>Torque Ratio</strong> (μ) = Output Torque / Input Torque</li>
-<li><strong>Input Torque</strong> (T<sub>ref(ν)</sub>) is the input torque (over ν) for a specific reference engine speed (see below).</li>
+<li><strong>Speed Ratio</strong> (<img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAIBAMAAAAy1HOFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVM3vqzJ2IhC7ZomZ3UTZb8aUAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAN0lEQVQIHWNgVGZgcGVgSGFg/8HAMIuB+wADw3eG2gYGVgMGPQYGNgceAQYGzuXHGRgYmDcCCQDG7AgWCjnuiAAAAABJRU5ErkJggg==" alt="\nu" title="\nu" />) = Output Speed / Input Speed</li>
+<li><strong>Torque Ratio</strong> (<img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAMBAMAAABGh1qtAAAALVBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAq0S7MiIQ71TdiZnNZgbEMwIAAAAJcEhZcwAADsQAAA7EAZUrDhsAAABJSURBVAgdY2BgVGAwYWBg4Atgewak1Bk4JgCphQzcG4DUCQbOBiD1lCEvJYGB6QnDcl4GBq6DHdmXGBh4QBIgbWBKHUwydIEpAEhtDOiQpn1yAAAAAElFTkSuQmCC" alt="\mu" title="\mu" />) = Output Torque / Input Torque</li>
+<li><strong>Input Torque</strong> (<img style="vertical-align:middle" src="data:image/png;base64,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" alt="T_{ref}(\nu)" title="T_{ref}(\nu)" />) is the input torque (over ν) for a specific reference engine speed (see below).</li>
 </ul>
 <p>The Input Torque at  reference engine speed is needed to calculate the actual engine torque using this formula:</p>
-<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="T_{in} = T_{ref}(v) \cdot ( \frac{n_{in}}{n_{ref}} )^{2}" title="T_{in} = T_{ref}(v) \cdot ( \frac{n_{in}}{n_{ref}} )^{2}" /></p>
+<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="T_{in} = T_{ref}(\nu) \cdot ( \frac{n_{in}}{n_{ref}} )^{2}" title="T_{in} = T_{ref}(\nu) \cdot ( \frac{n_{in}}{n_{ref}} )^{2}" /></p>
 <p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="\mu(\nu) = \frac{T_{out}}{T_{in}}" title="\mu(\nu) = \frac{T_{out}}{T_{in}}" /></p>
 <p>with:</p>
 <ul>
@@ -3396,18 +3396,35 @@ Example: “Gears\Gear1.vtlm” points to the “Gears” subdirectory of the Ge
 </ul>
 <p>The first two curves are read from a single .vemp file (see <a href="#electric-motor-max-torque-file-.vemp">Electric Motor Max Torque File (.vemp)</a>). The drag curve is provided in a .vemd file (see <a href="#electric-motor-drag-curve-file-.vemd">Electric Motor Drag Curve File (.vemd)</a>) and the electric power map in a .vemo file (see <a href="#electric-motor-map-.vemo">Electric Motor Map (.vemo)</a>).</p>
 <p>The convention for all input files is that positive torque values drive the vehicle while negative torque values apply additional drag and generate electric power.</p>
+<p>The follwing picture shows the signals used in VECTO and provided in the .vmod file. The VECTO convention is that positive torque adds additional drag to the drivetrain. Thus, if the electric motor propells the vehicle it applies negative torque.</p>
 <div class="figure">
 <img src="data:image/png;base64,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" />
 
+</div>
+<div id="electric-motor-model" class="section level3">
+<h3>Electric Motor Model</h3>
+<p>The VECTO component for the electric motor contains the electric motor itself which is connected via a transmission stage to the drivetrain. The ratio and efficiency of the transmission stage can be defined in the vehicle model.</p>
+<div class="figure">
+<img 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" />
+
+</div>
+<p>The naming convention for the signals is that ‘X’ denotes the position of the EM in the powertrain. P_X_… denotes signals related to the drivetrain speed while P_X-em_… denotes signals to the electric motor shaft.</p>
+<p>P_X_in = P_X_out + P_X_mech</p>
+<p>P_X_mech = P_X-em_mech + P_X_transm_loss</p>
+<p>P_X-em_mech = P_X-em_mech_elmap + P_X-em_inertia</p>
+<p>P_X-em_mech_elmap = P_X-em_el + P_X-em_loss</p>
+<p>P_X-em_mech_elmap = n_X-em * T_X-em_map</p>
+<p>P_X-em_el = PowerMap(n_X-em, T_X-em_map)</p>
+<p>P_X_loss = P_X_mech - P_X-em_el</p>
 </div>
 <div id="thermal-de-rating" class="section level3">
 <h3>Thermal De-Rating</h3>
 <p>The electric machine can be overloaded for a certain period. In addition to the maximum drive and generation torque (which already is in overload condition) the mechanical power the electric machine can generate is required.</p>
 <p>The basic principal of the thermal de-rating is as follows: based on the continuous power and the angular velocity for the continuous power as well as the maximum overload time a thermal energy buffer is calculated. During the simulation the difference between the current losses in the electric machine and the losses at the continuous power operating point are integrated over time. If this value reaches the capacity of the thermal energy buffer the electric machine can only deliver the specified continuous power until the thermal energy buffer goes below a certain.</p>
 <p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="E_\textrm{th,buf} = P_\textrm{loss,cont} * t_\textrm{ovl}" title="E_\textrm{th,buf} = P_\textrm{loss,cont} * t_\textrm{ovl}" /></p>
-<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="P_\textrm{loss,cont} = P_\textrm{map, el}(\frac{P_\textrm{cont}}{n_\textrm{P, cont}}, n_\textrm{P, cont}) - P_\textrm{cont}" title="P_\textrm{loss,cont} = P_\textrm{map, el}(\frac{P_\textrm{cont}}{n_\textrm{P, cont}}, n_\textrm{P, cont}) - P_\textrm{cont}" /></p>
+<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="P_\textrm{loss,cont} = P_\textrm{cont} - P_\textrm{map, el}(\frac{P_\textrm{cont}}{n_\textrm{P, cont}}, n_\textrm{P, cont})" title="P_\textrm{loss,cont} = P_\textrm{cont} - P_\textrm{map, el}(\frac{P_\textrm{cont}}{n_\textrm{P, cont}}, n_\textrm{P, cont})" /></p>
 <p>In every simulation step the losses of the electric machine are accumulated:</p>
-<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="E_{\textrm{ovl,} i + 1} = E_{\textrm{ovl,} i} + P_\textrm{loss, i} * dt" title="E_{\textrm{ovl,} i + 1} = E_{\textrm{ovl,} i} + P_\textrm{loss, i} * dt" /></p>
+<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="E_{\textrm{ovl,} i + 1} = E_{\textrm{ovl,} i} + (P_\textrm{loss, i} - P_\textrm{loss,cont}) * dt" title="E_{\textrm{ovl,} i + 1} = E_{\textrm{ovl,} i} + (P_\textrm{loss, i} - P_\textrm{loss,cont}) * dt" /></p>
 <p><img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAAUBAMAAABmJXv0AAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAid2ZuzKrVCJmEO9Eds0xWRQzAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAEMUlEQVRIDcVWW4gbZRT+spO5bm5bL+uuFqo+WQQ3KhY0hTxUYQtqWhdfCjGCKIQis1jUFC8Dq0VB7VSpyxZqh4qXFi/7UAsWH6Z9aAsqZqmgCy3mqfRxQexW8cFz/slM5pZkd1/8H+Y/5/vO+b4zk3+SAP/3MtrrnyAvWjb9edOO2oBm9fTYXWP3lwZUDKcy95UffDS9TMH6DYwp1so2oN5IFxWoYuFuqBt4LmHNx4A3rTAQxJexAYMWtxdtGH8HOsngBIxVaG6SWA9CBt/aqQ0dbMCgwFLP1CCvpmp64H6o/yBnDagYTsl/kY+ZVqfa2IBBhrWOAdKZNM0u5kDaAmNAAVHyYBr6CvCIk1aUtbAWg1hrrkHAc8CPtRgRTQtT0TyZjTKkLp/4uI+QUkXuu2QbIaJzuEFce5FaTy1cmUzRfOh5Ws8Komim8GHocFazgff22Go1DPfi0emjS04vDUUjHA81SGivANq/1Lnb5X5/FWp+5O2/WNG8l8n3inhm6RMXMD9EvtHjwtFBolMWtX/AcH8Df7C4Np1tPn6QXO73V/zE3u4Tyb3iQcfavJehiL1bltvBy+aM3qPUVUGd8f4GwWAx7a2A0qHOgGeZxLqaQALAG3xm6dcaQT+gaAVMJHgxkvWSCo5z0t8gGCymvQiMlKhTctX59ruzF+fvYB3x+YTOOH0Jqwv2ufnxCzuZzt0z+9o+hxB5orT9LD9ROuNtOnQreCOH1oLHc2Ww7qTIg/MvuecmNguitYCK93LGDDwnoncdki5PiNq4Ng1+0CRGcp/GU7cYZ2tfcVlwm6IHOfpdnUFTKhWtZYGUcSNvE3Ic1x4wOgyJ7wa9g7IuVxVH8KKye9HE74SAR7ComFn25MoKCg7dUsxAOBF9ckthbuobaE060FFtrYrMky+QiORuRnHPtHzKZqvo4PlN12/DrT/tlcwijjKNbVjR24Rc4pDunaz5krexa39m73lX8Iz46+brPKkHf7ZVcfUGpVxZQd5EwkA4Ef3zFCRzFPL3CW1dzEkiYvCT+458MU1JbHBGsJ1Q0vAHX9TbhIwFg4sacZHaAtRpiy+6Sb2tHPEH58oKtIZXFjYQTkRnSsIUalepp511fIiOyvlJo+x8qhESfeKiZAbvsNwhWJTyBCVC6viy+8RFjbjIVc0SfA/yIwF/hDMvm1nX4qOiWfRu7/TosIFwIvrtjjFHpsHgPe0Dvma9qS5PXnn9/d9+z9JzqDd8PNjVcbve/OPVJ6zHyfHq16tvXSNEHT9MYVDjBbMtj4/Bftvnr1w6sG0ZJAOudDDnFYYNPKfZFlVQaML0tQLth30ktAdVISwU+h9bCFpvqLi9h0ivR8wwxSBWQa9qLcWTVAct8RoOKhjOKW73bfZKLW/zr2sx4PMcXyk3HClJ3H2EXVOyu0l/D/pW9mciLf8BoMxD5aEFO+oAAAAASUVORK5CYII=" alt="P_\textrm{loss, i} = T_\textrm{em, mech} * n_\textrm{em} - P_\textrm{map, el}(T_\textrm{em, mech}, n_\textrm{em})" title="P_\textrm{loss, i} = T_\textrm{em, mech} * n_\textrm{em} - P_\textrm{map, el}(T_\textrm{em, mech}, n_\textrm{em})" /></p>
 <p>If <img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACoAAAATBAMAAAAUmIINAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAid3vmau7RHYyVGYizRAHtTi9AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVQYGW3QsQ7BUBSA4b9R5VKhEs/gBcTMZG08gUmMHTxA30BjMYkmFomFB5BYzGowmHQ2WS3iXrSRXic5ued85+ae5AL1ltNokQ1rAOMsUvWhqOkigJKmE8qawZDDH207J13tB7NERVNVFbmofCcg/HpPnUJmPgI7VJ2Mt6oiVwOT9YqlP/XeOvXgKNMRrugYm9vnrulhXLtO/2n43O1ok6p6BJRyyapwCy67QN7dyy3yzU+stjEW4hyPYO4mmp76P6lRIZ3/FqFqXsPAKV5uHNJMAAAAAElFTkSuQmCC" alt="E_\textrm{ovl, i}" title="E_\textrm{ovl, i}" /> reaches the overload capacity <img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAATBAMAAAAkFJMsAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAid3vmau7RHYyVGYizRAHtTi9AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVQYGWNgYBAyERQ1YcAC2BIYGEqwiDPwNzAwcGCTmD+BgYELm0Q/Azc2YQaGNIaj2CXMBC9ileD5yTAdJDGpAEiwa0LUPPRiYOD+wDCBYcFKBhaQBEMkREL6HQMD6wUGBp4FmagS7kB5ZgEGBhb2ixtY2qWBvMAVG+YuEOJMWcDAcB5ohCDQCBaHt0CJKAZ3lgXMDEAdjPcdBYP+gSSAfAaGcIatUAkgBwQieXBIuJ8ESRwAGhXOsaCYAehaKJjSMDeuf0EGg9SKDTzu9TuvHoBJQGisYQmSYkdVh+AtQDABmiwydLxLzNkAAAAASUVORK5CYII=" alt="E_\textrm{th,buf}" title="E_\textrm{th,buf}" /> the power of the electric machine is limited to the continuous power until <img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACcAAAATBAMAAADhZgm9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAid3vmau7RHYyVGYizRAHtTi9AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVQYGWNgYBAyERQ1YUADbAkMDCVoYgz8DQwMHOiC8ycwMHChC/YzcKMLMTCkMRzFFDQTvIghyPOTYTpMkFMdyLoJxNwfGCYwLIAKuwNpoL0MrBcYGHiQBUHyzAIMDCwMq1cwLG6YVABUCTbifAEDgyBnAKcD44ZXDCDtQMx431Ew6B9jA8MHngsbYIIgE4BSQEGGm2iCnAHsAQx7JwBVHgCrhihdsfEBAxsD59UHGZxXISLIJEbQgCTZkVXA2AuADAAuLCiMS3epRAAAAABJRU5ErkJggg==" alt="E_\textrm{ovl,i}" title="E_\textrm{ovl,i}" /> goes below the overload capacity multiplied by a certain factor. Then the maximum torque is available again.</p>
 </div>
@@ -4112,11 +4129,11 @@ If the battery’s SoC is below the lower SoC threshold <img style="vertical-ali
 <p>This file is used to interpolate the electric power required for a certain mechanical power at the eletric motor’s shaft. The file uses the <a href="#csv">VECTO CSV format</a>.</p>
 <ul>
 <li>Filetype: .vemo</li>
-<li>Header: <strong>n [rpm] , T [Nm] , P_el [Nm]</strong></li>
+<li>Header: <strong>n [rpm] , T [Nm] , P_el [kW]</strong></li>
 <li>Requires at least 2 data entries</li>
 </ul>
 <p><strong>Example:</strong></p>
-<pre><code>n [rpm], T [Nm], P_el [W]
+<pre><code>n [rpm], T [Nm], P_el [kW]
 0      , -1600 , 19.6898
 0      , -1550 , 18.5438
 0      , -1500 , 17.4322
@@ -6308,64 +6325,104 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <td>0 if the combustion engine is switched off (either during stand-still or eco-roll), 1 otherwise</td>
 </tr>
 <tr class="even">
-<td>n_em&lt;_POS&gt;</td>
+<td>i_&lt;POS}-em</td>
+<td>[-]</td>
+<td>Ratio between drivetrain and electric motor shaft</td>
+</tr>
+<tr class="odd">
+<td>n_&lt;POS&gt;-em_avg</td>
 <td>[rpm]</td>
 <td>Angular speed of the electric motor at position <em>POS</em></td>
 </tr>
+<tr class="even">
+<td>T_&lt;POS&gt;-em</td>
+<td>[Nm]</td>
+<td>Torque at the shaft of electric motor at position <em>POS</em>. Positive values mean that the electric motor acts as generator, negative torque values mean that the electric motor propels the vehicle</td>
+</tr>
 <tr class="odd">
-<td>T_em&lt;_POS&gt;</td>
+<td>T_&lt;POS&gt;-em_map</td>
 <td>[Nm]</td>
-<td>Torque applied by the electric motor at position <em>POS</em>.Positive values mean that the electric motor acts as generator, negative torque values mean that the electric motor propels the vehicle</td>
+<td>Torque internal torque of the electric motor at posision <em>POS</em>. Takes into account the electric motor’s intertia. Positive values mean that the electric motor acts as generator, negative torque values mean that the electric motor propels the vehicle</td>
 </tr>
 <tr class="even">
-<td>T_em&lt;_POS&gt;_mot_max</td>
+<td>T_&lt;POS&gt;-em_drive_max</td>
 <td>[Nm]</td>
 <td>Maximum torque the electric machine can apply to propel the vehicle. This already considers the maximum current the battery can provide</td>
 </tr>
 <tr class="odd">
-<td>T_em&lt;_POS&gt;_gen_max</td>
+<td>T_&lt;POS&gt;-em_gen_max</td>
 <td>[Nm]</td>
 <td>Maximum torque the electric machine can apply to generate electric power. This already considers the maximum charge current the battery can handle.</td>
 </tr>
 <tr class="even">
-<td>P_em&lt;_POS&gt;_in</td>
+<td>P_&lt;POS&gt;-em_drive_max</td>
 <td>[kW]</td>
-<td>Power at the electric machine’s input shaft</td>
+<td>Maximum power the electric motor can provide to drive the vehicle. This already considers the maximum electric power the battery can provide.</td>
 </tr>
 <tr class="odd">
-<td>P_em&lt;_POS&gt;_out</td>
+<td>P_&lt;POS&gt;-em_gen_max</td>
 <td>[kW]</td>
-<td>Power at the electric machine’s output shaft</td>
+<td>Maximum power the electric machine can generate. This already considers the maximum charge power the battery can handle.</td>
 </tr>
 <tr class="even">
-<td>P_em&lt;_POS&gt;_mech</td>
+<td>P_&lt;POS&gt;-em_mech</td>
 <td>[kW]</td>
-<td>Mechanical power the electric machine applies to the drivetrain. Positive values mean that electric energy is generated while negative values mean that the electric machine drives the vehicle.</td>
+<td>Power at the shaft of the electric motor at position <em>POS</em></td>
 </tr>
 <tr class="odd">
-<td>P_em&lt;_POS&gt;_el</td>
+<td>P_&lt;POS&gt;-em_mech_map</td>
+<td>[kW]</td>
+<td>Mechanical powerthe electric motor at position <em>POS</em> applies for driving or recuperation, including the electric motor’s inertia</td>
+</tr>
+<tr class="even">
+<td>P_&lt;POS&gt;-em_el</td>
 <td>[kW]</td>
 <td>Electric power generated or consumed by the elctric machine</td>
 </tr>
+<tr class="odd">
+<td>P_&lt;POS&gt;-em_loss</td>
+<td>[kW]</td>
+<td>Losses in the electric machine due to converting electric power to mechanical power</td>
+</tr>
 <tr class="even">
-<td>P_em&lt;_POS&gt;_drive_max</td>
+<td>P_&lt;POS&gt;-em_inertia_loss</td>
 <td>[kW]</td>
-<td>Maximum power the electric motor can provide to drive the vehicle. This already considers the maximum electric power the battery can provide.</td>
+<td>Inertia loses of the electric machine</td>
 </tr>
 <tr class="odd">
-<td>P_em&lt;_POS&gt;_gen_max</td>
+<td>P_&lt;POS&gt;_in</td>
 <td>[kW]</td>
-<td>Maximum power the electric machine can generate. This already considers the maximum charge power the battery can handle.</td>
+<td>Power at the electric machine’s input shaft (drivetrain)</td>
 </tr>
 <tr class="even">
-<td>P_em&lt;_POS&gt;_loss</td>
+<td>P_&lt;POS&gt;_out</td>
 <td>[kW]</td>
-<td>Losses in the electric machine due to converting electric power to mechanical power</td>
+<td>Power at the electric machine’s output shaft (drivetrain)</td>
 </tr>
 <tr class="odd">
-<td>P_em&lt;_POS&gt;_inertia_loss</td>
+<td>P_&lt;POS&gt;_mech</td>
 <td>[kW]</td>
-<td>Inertia loses of the electric machine</td>
+<td>Mechanical power the electric machine applies to the drivetrain. Positive values mean that electric energy is generated while negative values mean that the electric machine drives the vehicle.</td>
+</tr>
+<tr class="even">
+<td>P_&lt;POS&gt;_loss</td>
+<td>[kW]</td>
+<td>The total sum of losses of the electric motor at position <em>POS</em>. Calcualted as the difference of mecanical power applied at the drivetrain and the electrical power drawn from the REESS.</td>
+</tr>
+<tr class="odd">
+<td>P_&lt;POS&gt;_transm_loss</td>
+<td>[kW]</td>
+<td>Losses of the transmission stage inside the electric motor component</td>
+</tr>
+<tr class="even">
+<td>EM_OVL-&lt;POS&gt;-em</td>
+<td>[%]</td>
+<td>Used capacity of the thermal overload buffer of the thermal derating model</td>
+</tr>
+<tr class="odd">
+<td>EM_&lt;POS&gt;_off</td>
+<td>[-]</td>
+<td>Indicates if the electric motor at position <em>POS</em> is energized or not.</td>
 </tr>
 <tr class="even">
 <td>P_bat_T</td>
@@ -6806,6 +6863,116 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <td>[%]</td>
 <td>Time share of stop phases (v &lt; 0.1 [m/s])</td>
 </tr>
+<tr class="odd">
+<td>E_EM_&lt;POS&gt;_drive</td>
+<td>[kWh]</td>
+<td>Mechanical energy applied at the drivetrain by the electric machine at position <em>POS</em> to drive the vehicle</td>
+</tr>
+<tr class="even">
+<td>E_EM_&lt;POS&gt;_gen</td>
+<td>[kWh]</td>
+<td>Mechanical energy at the drivetrain recuperated by the electric machine at position <em>POS</em></td>
+</tr>
+<tr class="odd">
+<td>η_EM_&lt;POS&gt;_drive</td>
+<td>[-]</td>
+<td>Average efficiency at the drivetrain of the electric machine when the electric machine drives the vehicle. Based on the mechanical energy at the drivetrain and the electric power.</td>
+</tr>
+<tr class="even">
+<td>η_EM_&lt;POS&gt;_gen</td>
+<td>[-]</td>
+<td>Average efficiency at the drivetrain of the electric machine when the electric machine generates electric energy. Based on the mechanical energy at the drivetrain and the electric power</td>
+</tr>
+<tr class="odd">
+<td>E_EM_&lt;POS&gt;-em_drive</td>
+<td>[kWh]</td>
+<td>Mechanical energy at the electric motor shaft applied by the electric machine at position <em>POS</em> to drive the vehicle</td>
+</tr>
+<tr class="even">
+<td>E_EM_&lt;POS&gt;-em_gen</td>
+<td>[kWh]</td>
+<td>Mechanical energy at the electric motor shaft recuperated by the electric machine at position <em>POS</em></td>
+</tr>
+<tr class="odd">
+<td>η_EM_&lt;POS&gt;-em_drive</td>
+<td>[-]</td>
+<td>Average efficiency of the electric machine when the electric machine drives the vehicle. Based on the mechanical energy at the electric motor shaft and the electric energy.</td>
+</tr>
+<tr class="even">
+<td>η_EM_&lt;POS&gt;-em_gen</td>
+<td>[-]</td>
+<td>Average efficiency of the electric machine when the electric machine generates electric energy. Based on the mechanical energy at the electric motor shaft and the electric energy.</td>
+</tr>
+<tr class="odd">
+<td>n_EM_&lt;POS&gt;_avg</td>
+<td>[rpm]</td>
+<td>Average angular speed of the electric machine</td>
+</tr>
+<tr class="even">
+<td>E_EM_&lt;POS&gt;_off_loss</td>
+<td>[kWh]</td>
+<td>Total losses added by the electric machine when the electric machine is not energized (i.e., the electric machine’s drag losses)</td>
+</tr>
+<tr class="odd">
+<td>E_EM_&lt;POS&gt;_transm_loss</td>
+<td>[kWh]</td>
+<td>Losses of the transmission stage in the electric motor component.</td>
+</tr>
+<tr class="even">
+<td>E_EM_&lt;POS&gt;-em_loss</td>
+<td>[kWh]</td>
+<td>Total losses of the electric motor component. Calculated from P_<POS>-em_loss</td>
+</tr>
+<tr class="odd">
+<td>E_EM_&lt;POS&gt;_loss</td>
+<td>[kWh]</td>
+<td>Losses of the electric machine. Calculated from P_<POS>_loss</td>
+</tr>
+<tr class="even">
+<td>EM &lt;POS&gt; off time share</td>
+<td>[%]</td>
+<td>Time share the electric motor is not energized during the cycle.</td>
+</tr>
+<tr class="odd">
+<td>Battery Start SoC</td>
+<td>[%]</td>
+<td>Battery state of charge at the beginning of the simulation run</td>
+</tr>
+<tr class="even">
+<td>Battery End SoC</td>
+<td>[%]</td>
+<td>Battery state of charge at the end of the simulation run</td>
+</tr>
+<tr class="odd">
+<td>Battery Delta SoC</td>
+<td>[kWh]</td>
+<td>Difference of the energy stored in the battery between the beginning and end of the simulation run</td>
+</tr>
+<tr class="even">
+<td>E_Batt_loss</td>
+<td>[kWh]</td>
+<td>Total losses in the battery due to its internal resistance</td>
+</tr>
+<tr class="odd">
+<td>E_Batt_T_chg</td>
+<td>[kWh]</td>
+<td></td>
+</tr>
+<tr class="even">
+<td>E_Batt_T_dischg</td>
+<td>[kWh]</td>
+<td></td>
+</tr>
+<tr class="odd">
+<td>E_Batt_int_chg</td>
+<td>[kWh]</td>
+<td></td>
+</tr>
+<tr class="even">
+<td>E_Batt_int_dischg</td>
+<td>[kWh]</td>
+<td></td>
+</tr>
 </tbody>
 </table>
 <p><strong>Note:</strong> The fuel name is only added to the fuel consumption signals for vehicles with dual-fuel engines. In case single-fuel and dual-fuel vehicles are simulated in one simulation run, the fuel consumption for single-fuel vehicles is reported without the fuel name suffix while the fuel consumption of dual fuel vehicles contains the fuel name suffix!</p>
diff --git a/HashingTool/Properties/Version.cs b/HashingTool/Properties/Version.cs
index 26792b1611bf66d5f028125fa8d65b72318e7fc6..e7df7786c475fde134695f288d07f7dd136ea109 100644
--- a/HashingTool/Properties/Version.cs
+++ b/HashingTool/Properties/Version.cs
@@ -30,5 +30,5 @@
 */
 
 using System.Reflection;
-[assembly: AssemblyVersion("0.2.0.2108")]
-[assembly: AssemblyFileVersion("0.2.0.2108")]
+[assembly: AssemblyVersion("0.2.0.2205")]
+[assembly: AssemblyFileVersion("0.2.0.2205")]
diff --git a/VectoCommon/VectoCommon/Properties/Version.cs b/VectoCommon/VectoCommon/Properties/Version.cs
index 0d7be451226308f51246c683dab59bff19e9f78a..64b160b3b978821e44bda0250f96e3b35f876b3c 100644
--- a/VectoCommon/VectoCommon/Properties/Version.cs
+++ b/VectoCommon/VectoCommon/Properties/Version.cs
@@ -30,5 +30,5 @@
 */
 
 using System.Reflection;
-[assembly: AssemblyVersion("0.7.1.2108")]
-[assembly: AssemblyFileVersion("0.7.1.2108")]
\ No newline at end of file
+[assembly: AssemblyVersion("0.7.3.2205")]
+[assembly: AssemblyFileVersion("0.7.3.2205")]
\ No newline at end of file
diff --git a/VectoCommon/VectoHashing/Properties/Version.cs b/VectoCommon/VectoHashing/Properties/Version.cs
index 9320342a67e43f4b9648e969f242d75b8d3c7ca9..e029270f331d8634a9d50236e60e1726d1714930 100644
--- a/VectoCommon/VectoHashing/Properties/Version.cs
+++ b/VectoCommon/VectoHashing/Properties/Version.cs
@@ -30,5 +30,5 @@
 */
 
 using System.Reflection;
-[assembly: AssemblyVersion("1.2.0.2108")]
-[assembly: AssemblyFileVersion("1.2.0.2108")]
+[assembly: AssemblyVersion("1.2.0.2205")]
+[assembly: AssemblyFileVersion("1.2.0.2205")]
diff --git a/VectoConsole/Properties/Version.cs b/VectoConsole/Properties/Version.cs
index ed1c7e3760038fd998f186b3912a76d03e43b89b..01f066262a07a23924fbb4753b61f9511d35353b 100644
--- a/VectoConsole/Properties/Version.cs
+++ b/VectoConsole/Properties/Version.cs
@@ -30,5 +30,5 @@
 */
 
 using System.Reflection;
-[assembly: AssemblyVersion("0.7.1.2108")]
-[assembly: AssemblyFileVersion("0.7.1.2108")]
\ No newline at end of file
+[assembly: AssemblyVersion("0.7.3.2205")]
+[assembly: AssemblyFileVersion("0.7.3.2205")]
\ No newline at end of file
diff --git a/VectoCore/VectoCore/Properties/Version.cs b/VectoCore/VectoCore/Properties/Version.cs
index f54d6f67b5d0ff97d4a935bc34ccfc7fac75c279..550a08e25c644dc96dfc6488e7b5c7b6bf1c3134 100644
--- a/VectoCore/VectoCore/Properties/Version.cs
+++ b/VectoCore/VectoCore/Properties/Version.cs
@@ -30,5 +30,5 @@
 */
 
 using System.Reflection;
-[assembly: AssemblyVersion("0.7.1.2108")]
-[assembly: AssemblyFileVersion("0.7.1.2108")]
+[assembly: AssemblyVersion("0.7.3.2205")]
+[assembly: AssemblyFileVersion("0.7.3.2205")]
diff --git a/VectoCore/VectoCore/Utils/VectoVersionCore.cs b/VectoCore/VectoCore/Utils/VectoVersionCore.cs
index 35eaf8ae0727f741318901cee0f06d8cce1d4d9f..b7bf7119a8800aed8a7320f7600e22bb2b7db862 100644
--- a/VectoCore/VectoCore/Utils/VectoVersionCore.cs
+++ b/VectoCore/VectoCore/Utils/VectoVersionCore.cs
@@ -47,7 +47,7 @@ namespace TUGraz.VectoCore.Utils
 		public static string VersionNumber
 		{
 			get {
-				return "0.7.2.2118" + SUFFIX;
+				return "0.7.3.2205" + SUFFIX;
 			}
 		}