file names chosen via config and fix soc calculation error in ride.py #…

…21

scenarios/public_transport_base/incline.csv

@@ -3,4 +3,4 @@ Marktplatz,0,0.01,0.01,0.01,0.01
 Artrium,-0.01,0,0.01,0.01,0.01
 Therme,-0.01,-0.01,0,0.01,0.01
 Gries,-0.01,-0.01,-0.01,0,0.01
-Marktplatz,-0.01,-0.01,-0.01,-0.01,0
+Bahnhof,-0.01,-0.01,-0.01,-0.01,0

scenarios/public_transport_base/scenario.cfg

@@ -21,6 +21,9 @@ min_charging_power = 0.1
 vehicle_types = vehicle_types.json
 charging_points = charging_points.json
 schedule = schedule.csv
+consumption = consumption_ez10.csv
+distance = distance.csv
+incline = incline.csv
 
 
 [grid]

scenarios/public_transport_base/vehicle_types.json

@@ -6,7 +6,7 @@
         "charging_curve": [[0, 150], [0.8, 150], [1, 150]],
         "charging_power": {"CCS": 50, "Type2": 22, "Schuko": 3.7}
     },
-    "SH_oppb-0": {
+    "EZ10": {
         "name": "shuttle bus - opportunity charging",
         "capacity": 32,
         "charging_curve": [[0, 150], [0.8, 150], [1, 150]],

src/advantage/ride.py

@@ -95,7 +95,7 @@ def calculate_consumption(
         )
         consumption = consumption_factor * distance
 
-        return consumption, consumption * vehicle_type.battery_capacity / 100
+        return consumption, consumption / vehicle_type.battery_capacity
 
     def get_consumption(
         self, vehicle_type_name: str, incline, temperature, speed, load_level
@@ -129,7 +129,7 @@ def get_consumption(
         inc_col = df["incline"]
         tmp_col = df["t_amb"]
         lol_col = df["level_of_loading"]
-        speed_col = df["sp_type"]
+        speed_col = df["mean_speed"]
         cons_col = df["consumption"]
         data_table = list(zip(lol_col, inc_col, speed_col, tmp_col, cons_col))
 

src/advantage/simulation.py

@@ -350,15 +350,15 @@ def from_config(cls, scenario_name):
         }
 
         # read consumption_table
-        consumption_path = pathlib.Path(scenario_path, "consumption.csv")
+        consumption_path = pathlib.Path(scenario_path, cfg["files"]["consumption"])
         consumption_df = pd.read_csv(consumption_path)
 
         # read distance table
-        distance_table = pathlib.Path(scenario_path, "distance.csv")
+        distance_table = pathlib.Path(scenario_path, cfg["files"]["distance"])
         distance_df = pd.read_csv(distance_table, index_col=0)
 
         # read incline table
-        incline_table = pathlib.Path(scenario_path, "incline.csv")
+        incline_table = pathlib.Path(scenario_path, cfg["files"]["incline"])
         incline_df = pd.read_csv(incline_table, index_col=0)
 
         consumption_dict = {

src/advantage/simulation_type.py

@@ -3,7 +3,7 @@
 
 if TYPE_CHECKING:
     from advantage.simulation import Simulation
-    from advantage.simulation import Vehicle
+    from advantage.vehicle import Vehicle
 
 
 def class_from_str(strategy_name):
@@ -25,13 +25,14 @@ def get_predicted_soc(self, vehicle: "Vehicle", start: int, end: int):
             if start < task.arrival_time < end:
                 if task.task == "driving":
                     # TODO run task through driving simulation, add result to consumption
-                    self.simulation.driving_sim.calculate_trip(
+                    con = self.simulation.driving_sim.calculate_trip(
                         task.departure_point,
                         task.arrival_point,
                         vehicle.vehicle_type,
                         0.0,
                     )
+                    consumption += con[1]
                 if task.task == "charging":
                     # TODO check how much this would charge
                     pass
-        return vehicle.soc - consumption / vehicle.vehicle_type.battery_capacity
+        return vehicle.soc - consumption