use_selection=True,
name='WLTP profile')
comb_profile_wltp = electrical.CombinationPowerProfile([wltp_bat], name='wltp_profil')
comb_profile_load = electrical.CombinationPowerProfile([load_bat], name='load_profil')
comb_profile_end = electrical.CombinationPowerProfile([end_bat], name='end_soc_profil')
# =============================================================================
# Electrical Optimizer
# =============================================================================
input_values = {}
blocks = []
block_ebo = wf.InstanciateModel(eo.ElecBatteryOptimizer, name='ElecBatteryOptimizer')
optimize_ebo = wf.ModelMethod(eo.ElecBatteryOptimizer, 'Optimize', name='Optimize-ElecBatteryOptimizer')
attribute_selection_ebo = wf.ModelAttribute('powerpack_electric_simulators')
blocks.extend([block_ebo, optimize_ebo, attribute_selection_ebo])
pipes = [wf.Pipe(block_ebo.outputs[0], optimize_ebo.inputs[0]),
wf.Pipe(optimize_ebo.outputs[1], attribute_selection_ebo.inputs[0])
]
# =============================================================================
# Thermal specs
# =============================================================================
altitude = 0.1
evol_power1 = electrical.Evolution(evolution = list(npy.linspace(0, 5., 10)))
evol_power2 = electrical.Evolution(evolution = list(npy.linspace(0, 6., 10)))
evol_h_min1 = electrical.Evolution(evolution = [0 + 0.3*1e1*p/(p + 0.1)/2. for p in npy.linspace(0, 1, 10)])
evol_h_max2 = electrical.Evolution(evolution = [altitude + 0.3*1e1*p/(p + 0.1)/2. for p in npy.linspace(0, 1, 10)])
filter_max = [{'attribute': 'MPG', 'operator': 'gte', 'bound': 30}]
catalog2 = wf.InstanciateModel(model_class=Catalog, name='Car 2')
filter2 = wf.ModelMethod(model_class=Catalog,
method_name='filter_',
name='Filter 2')
filtered_catalog2 = wf.InstanciateModel(model_class=Catalog,
name='Filtered Catalog 2')
catalogs = wf.Sequence(number_arguments=2, name='Catalogs')
concatenate = wf.ClassMethod(class_=Catalog,
method_name='concatenate',
name='Concatenate')
display = wf.Display(order=0)
array_attribute = wf.ModelAttribute(attribute_name='array', name='Array')
blocks = [
import_csv, instantiate_pareto, catalog1, filter1, filtered_catalog1,
catalog2, filter2, filtered_catalog2, catalogs, concatenate, display,
array_attribute
]
# Pipes
pipes = [
wf.Pipe(import_csv.outputs[0], catalog1.inputs[0]),
wf.Pipe(import_csv.outputs[1], catalog1.inputs[1]),
wf.Pipe(instantiate_pareto.outputs[0], catalog1.inputs[2]),
wf.Pipe(import_csv.outputs[0], catalog2.inputs[0]),
wf.Pipe(import_csv.outputs[1], catalog2.inputs[1]),
wf.Pipe(instantiate_pareto.outputs[0], catalog2.inputs[2]),
comb_profile_wltp = electrical.CombinationPowerProfile([wltp_bat],
name='wltp_profil')
comb_profile_load = electrical.CombinationPowerProfile([load_bat],
name='load_profil')
comb_profile_end = electrical.CombinationPowerProfile([end_bat],
name='end_soc_profil')
# =============================================================================
# Electrical Optimizer
# =============================================================================
input_values = {}
blocks = []
block_ebo = wf.InstanciateModel(eo.ElecBatteryOptimizer, name='EBO')
optimize_ebo = wf.ModelMethod(eo.ElecBatteryOptimizer, 'Optimize',
'Optimize EBO')
attribute_selection_ebo = wf.ModelAttribute('powerpack_electric_simulators',
'Attribute Selection EBO')
filters = [{
'attribute': 'bms.battery.number_module_parallel',
'operator': 'gt',
'bound': 2
}, {
'attribute': 'bms.battery.number_module_serie',
'operator': 'lte',
'bound': 10
}, {
'attribute': 'bms.number_cells',
'operator': 'gte',
'bound': 750
}, {
'attribute': 'bms.number_cells',
class Optimizer(DessiaObject):
def __init__(self, model_to_optimize: Model, name: str = ''):
self.model_to_optimize = model_to_optimize
DessiaObject.__init__(self, name=name)
def optimize(self, optimization_value: int = 3) -> None:
self.model_to_optimize.value += optimization_value
instanciate_generator = wf.InstanciateModel(model_class=Generator,
name='Instantiate Generator')
generator_generate = wf.ModelMethod(model_class=Generator,
method_name='generate',
name='Generator Generate')
attribute_selection = wf.ModelAttribute(attribute_name='models',
name='Attribute Selection')
# Subworkflow of model optimization
instanciate_optimizer = wf.InstanciateModel(model_class=Optimizer,
name='Instantiate Optimizer')
optimization = wf.ModelMethod(model_class=Optimizer,
method_name='optimize',
name='Optimization')
model_fetcher = wf.ModelAttribute(attribute_name='model_to_optimize',
name='Model Fetcher')
pipe1_opt = wf.Pipe(input_variable=instanciate_optimizer.outputs[0],
output_variable=optimization.inputs[0])
pipe2_opt = wf.Pipe(input_variable=optimization.outputs[1],
output_variable=model_fetcher.inputs[0])
optimization_blocks = [instanciate_optimizer, optimization, model_fetcher]