diff --git a/battery_model.pkl b/battery_model.pkl
index 3ac50f2..bcb2ea5 100644
Binary files a/battery_model.pkl and b/battery_model.pkl differ
diff --git a/modules/class_akku.py b/modules/class_akku.py
index aac1bb0..7cfc96c 100644
--- a/modules/class_akku.py
+++ b/modules/class_akku.py
@@ -108,7 +108,16 @@ class PVAkku:
     
         verluste_wh = geladene_menge_ohne_verlust* (1.0-self.lade_effizienz)
         
-        return geladene_menge, verluste_wh
+        # Zusätzliche Verluste, wenn die Energiezufuhr die Kapazitätsgrenze überschreitet
+        zusatz_verluste_wh = 0
+        if effektive_lademenge > geladene_menge_ohne_verlust:
+            zusatz_verluste_wh = (effektive_lademenge - geladene_menge_ohne_verlust) * self.lade_effizienz
+        
+        # Gesamtverluste berechnen
+        gesamt_verluste_wh = verluste_wh + zusatz_verluste_wh
+        
+        
+        return geladene_menge, gesamt_verluste_wh
         # effektive_lademenge = wh * self.lade_effizienz
         # self.soc_wh = min(self.soc_wh + effektive_lademenge, self.kapazitaet_wh)
 
diff --git a/modules/class_optimize.py b/modules/class_optimize.py
index b442384..e2803b6 100644
--- a/modules/class_optimize.py
+++ b/modules/class_optimize.py
@@ -130,7 +130,7 @@ class optimization_problem:
 
     # Genetischer Algorithmus
     def optimize(self,start_solution=None):
-        population = self.toolbox.population(n=300)
+        population = self.toolbox.population(n=200)
         hof = tools.HallOfFame(1)
         
         stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -144,7 +144,7 @@ class optimization_problem:
                 population.insert(0, creator.Individual(start_solution))     
         
         #algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.4, mutpb=0.5, ngen=500,             stats=stats, halloffame=hof, verbose=True)
-        algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=400,             stats=stats, halloffame=hof, verbose=True)
+        algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=100,             stats=stats, halloffame=hof, verbose=True)
         
         member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
         for ind in population:
@@ -269,7 +269,7 @@ class optimization_problem:
         
         print(o)
         visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
-        os.system("scp visualisierungsergebnisse.pdf andreas@192.168.1.135:")
+        os.system("cp visualisierungsergebnisse.pdf ~/")
         
         #print(eauto)
         return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto,"start_solution":best_solution,"spuelstart":spuelstart_int}