feat: rename remaining German API fields and deprecate legacy endpoints (#1164)

Complete the English API translation started in #675:

- rename input fields pv_akku to pv_battery and eauto to ev, and the
  solution fields eautocharge_hours_float to ev_charge_hours_float and
  eauto_obj to ev_obj; the German names are still accepted on input
  via validation aliases and re-emitted in responses as deprecated
  computed fields, same pattern as #675
- mark the legacy endpoints /strompreis, /gesamtlast and
  /gesamtlast_simple as deprecated in the OpenAPI schema
- use amount instead of a euro reference in the battery LCOS log
  message

Co-authored-by: Tobias Welz <tobias.wizneteu@gmail.com>

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -242,6 +242,8 @@ class GeneticSimulationResult(GeneticParametersBaseModel):
 class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
     """**Note**: The first value of "load_wh_per_hour", "grid_feed_in_wh_per_hour", and "grid_consumption_wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
 
+    model_config = ConfigDict(populate_by_name=True, extra="ignore")
+
     ac_charge: list[float] = Field(
         json_schema_extra={
             "description": "Array with AC charging values as relative power (0.0-1.0), other values set to 0."
@@ -257,9 +259,17 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             "description": "Array with discharge values (1 for discharge, 0 otherwise)."
         }
     )
-    eautocharge_hours_float: Optional[list[float]] = Field(json_schema_extra={"description": "TBD"})
+    ev_charge_hours_float: Optional[list[float]] = Field(
+        validation_alias=AliasChoices("ev_charge_hours_float", "eautocharge_hours_float"),
+        json_schema_extra={
+            "description": "Array with EV charging values as relative power (0.0-1.0), or `null` if no EV is optimized."
+        },
+    )
     result: GeneticSimulationResult
-    eauto_obj: Optional[ElectricVehicleResult]
+    ev_obj: Optional[ElectricVehicleResult] = Field(
+        validation_alias=AliasChoices("ev_obj", "eauto_obj"),
+        json_schema_extra={"description": "Electric vehicle state after optimization."},
+    )
     start_solution: Optional[list[float]] = Field(
         default=None,
         json_schema_extra={
@@ -273,6 +283,17 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         },
     )
 
+    # Computed fields for backward compatibility (deprecated German names)
+    @computed_field(json_schema_extra={"deprecated": True})
+    def eautocharge_hours_float(self) -> Optional[list[float]]:
+        """Deprecated: Use ev_charge_hours_float instead."""
+        return self.ev_charge_hours_float
+
+    @computed_field(json_schema_extra={"deprecated": True})
+    def eauto_obj(self) -> Optional[ElectricVehicleResult]:
+        """Deprecated: Use ev_obj instead."""
+        return self.ev_obj
+
     @field_validator(
         "ac_charge",
         "dc_charge",
@@ -283,7 +304,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         return NumpyEncoder.convert_numpy(field)[0]
 
     @field_validator(
-        "eauto_obj",
+        "ev_obj",
         mode="before",
     )
     def convert_eauto(cls, field: Any) -> Any:
@@ -548,14 +569,14 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             solution[key] = operation[key]
 
         # Add EV battery solution
-        # eautocharge_hours_float start at hour 0 of start day
+        # ev_charge_hours_float start at hour 0 of start day
         # result.ev_soc_per_hour start at start_datetime.hour
-        if self.eauto_obj:
+        if self.ev_obj:
             ev_device_id = self._ev_device_id()
-            if self.eautocharge_hours_float is None:
+            if self.ev_charge_hours_float is None:
                 # Electric vehicle is full enough. No load times.
                 solution[f"{ev_device_id}_soc_factor"] = [
-                    self.eauto_obj.initial_soc_percentage / 100.0
+                    self.ev_obj.initial_soc_percentage / 100.0
                 ] * n_points
                 solution["genetic_ev_charge_factor"] = [0.0] * n_points
                 # operation modes
@@ -576,7 +597,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
                 operation = {
                     "genetic_ev_charge_factor": [],
                 }
-                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
+                for hour_idx, rate in enumerate(self.ev_charge_hours_float):
                     if hour_idx < start_day_hour:
                         continue
                     if hour_idx >= start_day_hour + n_points:
@@ -791,10 +812,10 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             )
 
         # Add EV battery instructions (fill rate based control)
-        # eautocharge_hours_float start at hour 0 of start day
-        if self.eauto_obj:
+        # ev_charge_hours_float start at hour 0 of start day
+        if self.ev_obj:
             resource_id = self._ev_device_id()
-            if self.eautocharge_hours_float is None:
+            if self.ev_charge_hours_float is None:
                 # Electric vehicle is full enough. No load times.
                 logger.debug("EV: {} - SoC >= min, no optimization", resource_id)
                 plan.add_instruction(
@@ -810,9 +831,9 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
                 last_operation_mode = None
                 last_operation_mode_factor = None
                 logger.debug(
-                    "EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:]
+                    "EV: {} - {}", resource_id, self.ev_charge_hours_float[start_day_hour:]
                 )
-                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
+                for hour_idx, rate in enumerate(self.ev_charge_hours_float):
                     if hour_idx < start_day_hour:
                         continue
                     operation_mode, operation_mode_factor = self._battery_operation_from_solution(