pymoo runs and is starting to make more sense. need to check that par…

…ameters have effect for all parameters
NREL · Jan 31, 2025 · ecfc18c · ecfc18c
1 parent e695acc
commit ecfc18c
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diff --git a/cal_and_val/thermal/cal_hev.py b/cal_and_val/thermal/cal_hev.py
@@ -1,6 +1,7 @@
 """
 Calibration script for 2021_Hyundai_Sonata_Hybrid_Blue    
 """
+import pprint
 from pathlib import Path
 import numpy as np  # noqa: F401
 import matplotlib.pyplot as plt  # noqa: F401
@@ -358,17 +359,17 @@ def get_exp_pwr_hvac(df):
         new_em_eff_range,
         new_fc_eff_max,
         # new_fc_eff_range, # range is not working
-        # new_cab_shell_htc,
-        # new_cab_htc_to_amb_stop,
-        # new_cab_tm,
-        # new_cab_length,
-        # new_speed_soc_disch_buffer_meters_per_second,
-        # new_speed_soc_disch_buffer_coeff,
-        # new_speed_soc_fc_on_buffer_meters_per_second,
-        # new_speed_soc_fc_on_buffer_coeff,
-        # new_fc_min_time_on_seconds,
-        # new_frac_pwr_demand_fc_forced_on,
-        # new_frac_of_most_eff_pwr_to_run_fc,
+        new_cab_shell_htc,
+        new_cab_htc_to_amb_stop,
+        new_cab_tm,
+        new_cab_length,
+        new_speed_soc_disch_buffer_meters_per_second,
+        new_speed_soc_disch_buffer_coeff,
+        new_speed_soc_fc_on_buffer_meters_per_second,
+        new_speed_soc_fc_on_buffer_coeff,
+        new_fc_min_time_on_seconds,
+        new_frac_pwr_demand_fc_forced_on,
+        new_frac_of_most_eff_pwr_to_run_fc,
         # TODO: make sure this has functions for modifying
         # - battery thermal -- not necessary for HEV because battery temperature has no real effect
         #     - thermal mass
@@ -391,65 +392,70 @@ def get_exp_pwr_hvac(df):
         (0.1, 0.6),
         (0.32, 0.45),
         # (0.2, 0.45), # range is not working
-        # (10, 250),
-        # (10, 250),
-        # (100e3, 350e3),
-        # (1.5, 7),
-        # (5, 50),
-        # (0.25, 2.0),
-        # (5, 50),
-        # (0.25, 2.0),
-        # (5, 30),
-        # (0.3, 0.8),
-        # (0.1, 1.0),
+        (10, 250),
+        (10, 250),
+        (100e3, 350e3),
+        (1.5, 7),
+        (5, 50),
+        (0.25, 2.0),
+        (5, 50),
+        (0.25, 2.0),
+        (5, 30),
+        (0.3, 0.8),
+        (0.1, 1.0),
     ),
     verbose=False,    
 )
 
+print("")
+pprint.pp(cal_mod_obj.params_and_bounds())
+print("")
+
 val_mod_obj = deepcopy(cal_mod_obj)
 val_mod_obj.dfs = dfs_for_val
 val_mod_obj.models = sds_for_val
 
 em_eff_fwd_max = fsim.ElectricMachine.from_pydict(veh_dict['pt_type']['HybridElectricVehicle']['em'], skip_init=False).eff_fwd_max 
 em_eff_fwd_range = fsim.ElectricMachine.from_pydict(veh_dict['pt_type']['HybridElectricVehicle']['em'], skip_init=False).eff_fwd_range 
 fc_eff_max = fsim.FuelConverter.from_pydict(veh_dict['pt_type']['HybridElectricVehicle']['fc'], skip_init=False).eff_max 
-print("Verifying that model responds to input parameter changes by individually perturbing parameters")
-baseline_errors = cal_mod_obj.get_errors(
-    cal_mod_obj.update_params([
-        em_eff_fwd_max,
-        em_eff_fwd_range,
-        fc_eff_max,
-        # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
-    ])
-)
-param0_perturb = cal_mod_obj.get_errors(
-    cal_mod_obj.update_params([
-        em_eff_fwd_max + 0.05,
-        em_eff_fwd_range,
-        fc_eff_max,
-        # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
-    ])
-)
-assert np.array(param0_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param0_perturb.values()}"
-param1_perturb = cal_mod_obj.get_errors(
-    cal_mod_obj.update_params([
-        em_eff_fwd_max,
-        em_eff_fwd_range + 0.1,
-        fc_eff_max,
-        # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
-    ])
-)
-assert np.array(param1_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param1_perturb.values()}"
-param2_perturb = cal_mod_obj.get_errors(
-    cal_mod_obj.update_params([
-        em_eff_fwd_max,
-        em_eff_fwd_range,
-        fc_eff_max - 0.15,
-        # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
-    ])
-)
-assert np.array(param2_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param1_perturb.values()}"
-print("Success!")
+# TODO: uncomment this and do it for all the parameters
+# print("Verifying that model responds to input parameter changes by individually perturbing parameters")
+# baseline_errors = cal_mod_obj.get_errors(
+#     cal_mod_obj.update_params([
+#         em_eff_fwd_max,
+#         em_eff_fwd_range,
+#         fc_eff_max,
+#         # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
+#     ])
+# )
+# param0_perturb = cal_mod_obj.get_errors(
+#     cal_mod_obj.update_params([
+#         em_eff_fwd_max + 0.05,
+#         em_eff_fwd_range,
+#         fc_eff_max,
+#         # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
+#     ])
+# )
+# assert np.array(param0_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param0_perturb.values()}"
+# param1_perturb = cal_mod_obj.get_errors(
+#     cal_mod_obj.update_params([
+#         em_eff_fwd_max,
+#         em_eff_fwd_range + 0.1,
+#         fc_eff_max,
+#         # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
+#     ])
+# )
+# assert np.array(param1_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param1_perturb.values()}"
+# param2_perturb = cal_mod_obj.get_errors(
+#     cal_mod_obj.update_params([
+#         em_eff_fwd_max,
+#         em_eff_fwd_range,
+#         fc_eff_max - 0.15,
+#         # veh_dict['pt_type']['HybridElectricVehicle']['fc'],
+#     ])
+# )
+# assert np.array(param2_perturb.values()) != np.array(baseline_errors.values()), f"\n{baseline_errors.values()}\n{param1_perturb.values()}"
+# print("Success!")
 
 if __name__ == "__main__":
     parser = pymoo_api.get_parser()

diff --git a/python/fastsim/pymoo_api.py b/python/fastsim/pymoo_api.py
@@ -203,7 +203,7 @@ def get_errors(
                 sd_dict = sd.to_pydict()
                 walk_success = True
                 if len(sd_dict['veh']['history']['time_seconds']) < np.floor(len(df_exp) / 2):
-                    walk_success = True
+                    walk_success = False
 
             if self.verbose:
                 print(f"Time to simulate {key}: {t1 - t0:.3g}")
@@ -262,7 +262,7 @@ def get_errors(
 
     def params_and_bounds(self):
         return [
-            (param_fn, bound_set) for (param_fn, bound_set) in zip(self.param_fns, self.bounds)
+            (param_fn.__name__, bound_set) for (param_fn, bound_set) in zip(self.param_fns, self.bounds)
         ]