diff --git a/docs/source/features.rst b/docs/source/features.rst
index ec9dceb..cfbb76d 100644
--- a/docs/source/features.rst
+++ b/docs/source/features.rst
@@ -3,21 +3,50 @@ Features - overview
 
 This section presents the main features and functions to use in SimBEV.
 
-General concept
----------------
+simbev.simbev_class
+-------------------
 
-Simbev forecast charging demand for different e-cars for a pregiven time period. It does so by analysing the mobility in Germany
-data. This data is split into different region types ( Ländliche Regionen LR_Klein - Kleinstädtischer, dörflicher Raum LR_Mitte
-- Mittelstädte, städtischer Raum LR_Zentr - Zentrale Stadt Stadtregionen SR_Klein - Kleinstädtischer, dörflicher Raum SR_Mitte
-- Mittelstädte, städtischer Raum SR_Gross - Regiopolen, Großstädte SR_Metro - Metropole).
-The system is parameterized with the help of different input parameters such as battery capacity and charging power (slow and fast)
-as well as the consumption of each car.
+.. automodule:: simbev.simbev_class
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+simbev.car
+----------
+
+.. automodule:: simbev.car
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+simbev.mid_timeseries
+-----------
+
+.. automodule:: simbev.mid_timeseries
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+simbev.plot
+-----------
 
+.. automodule:: simbev.plot
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+simbev.region
+-----------
+
+.. automodule:: simbev.region
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-SimBEV - core
--------------
+simbev.trip
+-----------
 
-.. automodule:: simbev.simbev.simbev_class
+.. automodule:: simbev.trip
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/usage_details.rst b/docs/source/usage_details.rst
index 0ff90dd..f642d0b 100644
--- a/docs/source/usage_details.rst
+++ b/docs/source/usage_details.rst
@@ -3,6 +3,16 @@ SimBEV usage
 
 This section gives a quick overview, what you can do with SimBEV and how to use it.
 
+General Concept:
+
+Simbev forecast charging demand for different e-cars for a pregiven time period. It does so by analysing the mobility in Germany
+data. This data is split into different region types ( Ländliche Regionen LR_Klein - Kleinstädtischer, dörflicher Raum LR_Mitte
+- Mittelstädte, städtischer Raum LR_Zentr - Zentrale Stadt Stadtregionen SR_Klein - Kleinstädtischer, dörflicher Raum SR_Mitte
+- Mittelstädte, städtischer Raum SR_Gross - Regiopolen, Großstädte SR_Metro - Metropole).
+The system is parameterized with the help of different input parameters such as battery capacity and charging power (slow and fast)
+as well as the consumption of each car.
+
+
 Usage overview
 --------------------
 With SimBEV, you can:
@@ -25,3 +35,53 @@ Configuration possibilities
 ---------------------------
 Each simulation must include the following parameters:
 
+#. param1
+#. param2
+
+
+
+Get the data
+------------
+
+If you want to run SimBEV in the mode using probabilities, a data set is available `here <https://zenodo.org/record/7609683>`_.
+
+Create a scenario
+-----------------
+
+- You can use a default scenario or define a custom one in the directory `scenarios`
+- Run simbev with the desired scenario:
+
+.. code:: bash
+
+    python -m simbev path/to/config
+
+defaults to:
+
+.. code:: bash
+
+    python -m simbev scenarios/default/configs/default.cfg
+
+- Results are created in the subdirectory `results` in the scenario directory
+
+Set parameters for your scenario
+--------------------------------
+
+Select regio-type for the mobility characteristics:
+- regiotypes:
+Ländliche Regionen
+LR_Klein - Kleinstädtischer, dörflicher Raum
+LR_Mitte - Mittelstädte, städtischer Raum
+LR_Zentr - Zentrale Stadt
+Stadtregionen
+SR_Klein - Kleinstädtischer, dörflicher Raum
+SR_Mitte - Mittelstädte, städtischer Raum
+SR_Gross - Regiopolen, Großstädte
+SR_Metro - Metropole
+
+Change vehicle configuration
+- battery capacity
+- charging power (slow and fast)
+- consumption
+
+Decide how many vehicles should be simulated
+- note: more than 5000 vehicles of one type in one region is not necessary, if you want to analyze more, scale it accordingly