feat(devices): add battery profiles

A battery profile describes a battery model: the charge and discharge limit
maps over temperature and SoC, the charge and discharge efficiency curves
over C-rate, the technical SoC bounds, the hard temperature cutoffs and,
for a model, its nominal capacity and power limits. EOS packages the
generic profile generic-lfp; users add their own profiles.

A battery or electric vehicle selects its profile by battery_profile and
describes how the installation differs from it in profile_adjustment
(capacity, power limits of the installation, periodic full charge). The
effective values are resolved from both at the measured operating point
(temperature, SoC, power) and shown as effective_* fields.

The GENETIC simulation uses the limits and efficiencies of the profile over
SoC, with precomputed tables as a fast path; GENETIC0 gets the values at the
operating point. Optimization parameters are always valid: a battery whose
profile is missing, unreadable or invalid uses the packaged generic-lfp
profile, and the former defaults only if that is not usable either; each is
logged as error, and an optimization run is never cancelled because of a
battery.

Additional changes
- Add a generic device profile store: user and packaged profiles, atomic
  saves, compiled profiles cached per file and checked again at most once a
  second, plain file names as profile names ('schema' is reserved). On
  Windows, a save or read that collides with the other is retried briefly.
- Add the REST API /v1/profile to list the profile kinds and to list, read
  (document and JSON schema), save and delete profiles.
- Check the hard temperature cutoffs on the exact temperature.
- Resolve inhibited charging or discharging to a power of 0, e.g. for a cold
  battery; give GENETIC0 its own discharge limit for that.
- Limit generic profiles without own power limits to the former 5000 W.
- Accept only the implemented units; current limits need the nominal
  voltage.
- Migrate legacy battery settings into the profile adjustment, keep the
  former charge power limit as discharge limit (also for a Home Assistant
  entity on it) and report dropped efficiencies as errors.
- Limit GENETIC battery discharge by the discharge power.
- Allow battery profile lookups while the configuration is created.
- Resolve a battery once per configuration dump.
- Package the bundled profiles, document battery profiles and regenerate
  the configuration and OpenAPI documentation.
- Ignore the report PDFs written by the visualization tests.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Assisted-by: Claude Code (claude-opus-5-5)

tests/test_geneticcompletedevices.py

@@ -227,9 +227,7 @@ def test_ev_deadlines_roundtrip_without_changing_slot_physics() -> None:
     settings = BatteriesCommonSettings.model_validate(
         {
             "device_id": "car",
-            "capacity_wh": 10000,
-            "charging_efficiency": 0.8,
-            "max_charge_power_w": 4000,
+            "profile_adjustment": {"nominal_capacity_wh": 10000, "max_charge_power_w": 4000},
             "min_soc_deadline_datetime": "2026-09-16T07:30:00+02:00",
             "min_soc_max_duration_h": 2.5,
         }
@@ -238,21 +236,27 @@ def test_ev_deadlines_roundtrip_without_changing_slot_physics() -> None:
     assert params.min_soc_max_duration_h == 2.5
     assert params.min_soc_deadline_datetime is not None
     assert params.min_soc_deadline_datetime.in_timezone("Europe/Berlin").hour == 7
+    assert params.capacity_wh == 10000
+    assert params.max_charge_power_w == pytest.approx(4000)
     battery = Battery(params, prediction_hours=16, slot_duration_h=0.25)
     battery.charge_array[0] = 1
+    # 4000 W for a 0.25 h slot caps the 2000 Wh request at 1000 Wh; the charging
+    # efficiency comes from the battery profile.
     charged, losses = battery.charge_energy(2000, hour=0)
-    assert charged == pytest.approx(800)
-    assert losses == pytest.approx(200)
+    assert charged == pytest.approx(1000 * params.charging_efficiency)
+    assert losses == pytest.approx(1000 * (1 - params.charging_efficiency))
     assert "min_soc_deadline_datetime" not in settings.to_genetic0_ev_bat_param().model_dump()
 
 
 @pytest.mark.parametrize("duration", [0.0, -1.0, float("inf"), float("nan")])
 def test_ev_invalid_departure_durations_rejected(duration: float) -> None:
-    for model in (ElectricVehicleParameters, BatteriesCommonSettings):
+    # Optimizer parameters take "capacity_wh", settings a profile adjustment.
+    for model, capacity in (
+        (ElectricVehicleParameters, {"capacity_wh": 10000}),
+        (BatteriesCommonSettings, {"profile_adjustment": {"nominal_capacity_wh": 10000}}),
+    ):
         with pytest.raises(ValidationError):
-            model.model_validate(
-                {"device_id": "car", "capacity_wh": 10000, "min_soc_max_duration_h": duration}
-            )
+            model.model_validate({"device_id": "car", **capacity, "min_soc_max_duration_h": duration})
 
 
 def test_completed_cycles_parameter_initializes_remaining_global_ids() -> None: