Results handling¶

This worked example shows how to work with Breathe Design results objects, from quick equilibrium KPIs to dynamic simulations and batch runs.

You’ll learn to master:

• Fetch equilibrium KPIs for a baseline cell
• Inspect and compare designs
• Run a worked example simulation with a Cycler
• Explore dynamic data and plot key variables
• Scale up to batch simulations and summarize results

Setup¶

This imports the Breathe Design API and cycler package used throughout the worked example

In [37]:

from breathe_design import api_interface as api, Cycler

from breathe_design import api_interface as api, Cycler

The first result we can get from the api is the equilibrium KPIs

Designs & Equilibrium KPIs¶

We start with a baseline cell (here: "Molicel P45B").
get_eqm_kpis returns a results object with convenient accessors for capacity and other KPIs.

In [38]:

eqm_results = api.get_eqm_kpis("Molicel P45B")

eqm_results = api.get_eqm_kpis("Molicel P45B")

The results class is

In [39]:

eqm_results.__class__

eqm_results.__class__

Out[39]:

breathe_design.results.SingleSimulationResults

With methods and attributes

In [40]:

for attr in dir(eqm_results):
    if not attr.startswith("_"):
        print(f"{attr}")

for attr in dir(eqm_results): if not attr.startswith("_"): print(f"{attr}")

baseline
capacity
compare_designs
design_names
dynamic_data
dynamic_kpis
get_dynamic_data
get_input_parameters
get_kpis
get_normalized_kpis
get_summary
kpis
plot_dynamic_response
plot_radar
plot_sensitivities
plot_voltage_response
print_dynamic_variables

baseline provides a shortcut to the Baseline KPIs

In [41]:

eqm_results.baseline

eqm_results.baseline

Out[41]:

KPI
Capacity [Ah]                           4.500000
Nominal Voltage [V]                     3.656630
Energy [Wh]                            16.454837
Gravimetric Energy Density [Wh/kg]    241.196608
Volumetric Energy Density [Wh/l]      665.310409
Minimum Anode Voltage [mV]             80.490746
Weight [g]                             68.221675
Volume [l]                              0.024733
Heat Capacity [kJ/K]                    0.052220
Name: Baseline, dtype: float64

capacity gets the capacity value for the Baseline design

In [42]:

eqm_results.capacity

eqm_results.capacity

Out[42]:

np.float64(4.499999999999997)

compare_designs compares the KPIs of the designs but will return an error if only the Baseline design is present

In [43]:

try:
    eqm_results.compare_designs()
except ValueError as e:
    print(e)

try: eqm_results.compare_designs() except ValueError as e: print(e)

Only Baseline design found. Cannot compare designs.

design_names gets list of design names.

In [44]:

eqm_results.design_names

eqm_results.design_names

Out[44]:

['Baseline']

dynamic_data returns the dynamic data as result of passing a cycler to the api and running a simulation for each design and input parameter. However, in this case it will be empty as no designs or cycler objects were passed.

In [45]:

eqm_results.dynamic_data

eqm_results.dynamic_data

Out[45]:

{}

get_input_parameters gets the original input parameters used for the api call.

In [46]:

eqm_results.get_input_parameters()

eqm_results.get_input_parameters()

Out[46]:

{'base_battery': 'Molicel P45B', 'designs': [], 'formats': []}

get_kpis and kpis both return the kpi data. get_kpi can accept a design name and only return data for that design providing it exists.

In [47]:

try:
    eqm_results.get_kpis("testDesign")
except ValueError as e:
    print(e)

try: eqm_results.get_kpis("testDesign") except ValueError as e: print(e)

Design testDesign not found. Cannot normalize results.

get_normalized_kpis divides the values for each design by the Baseline values. If there is only a Baseline design this will return all 1.0s.

In [48]:

eqm_results.get_normalized_kpis()

eqm_results.get_normalized_kpis()

Out[48]:

	Baseline
KPI
Capacity [Ah]	1.0
Nominal Voltage [V]	1.0
Energy [Wh]	1.0
Gravimetric Energy Density [Wh/kg]	1.0
Volumetric Energy Density [Wh/l]	1.0
Minimum Anode Voltage [mV]	1.0
Weight [g]	1.0
Volume [l]	1.0
Heat Capacity [kJ/K]	1.0

get_summary prints a summary of the results

In [49]:

eqm_results.get_summary()

eqm_results.get_summary()

Out[49]:

{'is_batch': False,
 'num_simulations': 1,
 'design_names': ['Baseline'],
 'num_designs': 1,
 'input_parameters': {'base_battery': 'Molicel P45B',
  'designs': [],
  'formats': []}}

plot_sensitivities plots the sensitivities of each KPI to variation in each design parameter

In [50]:

eqm_results.plot_sensitivities()

eqm_results.plot_sensitivities()

Some of the methods will not work without designs or dynamic data generated from passing a cycler to the api

In [51]:

try:
    eqm_results.plot_radar()
except ValueError as e:
    print(e)

try: eqm_results.plot_radar() except ValueError as e: print(e)

Only Baseline design found. Cannot compare designs.

In [52]:

try:
    eqm_results.plot_dynamic_response()
except ValueError as e:
    print(e)

try: eqm_results.plot_dynamic_response() except ValueError as e: print(e)

No dynamic data available for this simulation

In [53]:

try:
    eqm_results.plot_voltage_response()
except ValueError as e:
    print(e)

try: eqm_results.plot_voltage_response() except ValueError as e: print(e)

No dynamic data available for this simulation

In [54]:

try:
    eqm_results.print_dynamic_variables()
except ValueError as e:
    print(e)

try: eqm_results.print_dynamic_variables() except ValueError as e: print(e)

No dynamic data available for this simulation

Designs & Dynamic Data¶

Lets add a virtual cell design and run a dynamic simulation in parallel with our baseline cell. In this example we will adjust the NPratio to a fixed value of 1.0.

In [55]:

designs = [{"designName": "Lower NP", "NPratio": 1.0}]

designs = [{"designName": "Lower NP", "NPratio": 1.0}]

Build a Cycler¶

A Cycler describes the protocol (e.g., CC charge to a voltage limit).
Here we set units and (optionally) pass in the baseline capacity for relative C‑rates.

In [56]:

cycler = Cycler(selected_unit="C", cell_capacity=eqm_results.capacity)

cycler = Cycler(selected_unit="C", cell_capacity=eqm_results.capacity)

Worked Example: P45B Charge Run¶

We’ll run one simulation using our cycler and a simple design list (including Baseline).
This returns a results object with:

• .kpis and .get_kpis(designName) for design‑specific KPIs
• .compare_designs() to compare across designs
• .plot_* helpers for quick visualizations

In [57]:

single_output = api.run_sim(
    base_battery="Molicel P45B",
    cycler=cycler.cccv(1.0, -1.0, 0.01, 4.2, 2.6, 60.0, 60.0),
    designs=designs,
    initialSoC=0.5,
    initialTemperature_degC=25.0,
    ambientTemperature_degC=25.0,
)

single_output = api.run_sim( base_battery="Molicel P45B", cycler=cycler.cccv(1.0, -1.0, 0.01, 4.2, 2.6, 60.0, 60.0), designs=designs, initialSoC=0.5, initialTemperature_degC=25.0, ambientTemperature_degC=25.0, )

Running...:   0%|          | 0/1 [00:00<?, ? designs/s]

Running...: 100%|██████████| 1/1 [00:09<00:00,  9.98s/ designs]

In [58]:

single_output.kpis

single_output.kpis

Out[58]:

	Baseline	Lower NP
KPI
Capacity [Ah]	4.500000	4.626559
Nominal Voltage [V]	3.656630	3.659476
Energy [Wh]	16.454837	16.930780
Gravimetric Energy Density [Wh/kg]	241.196608	246.933081
Volumetric Energy Density [Wh/l]	665.310409	684.554009
Minimum Anode Voltage [mV]	80.490746	77.407029
Weight [g]	68.221675	68.564246
Volume [l]	0.024733	0.024733
Heat Capacity [kJ/K]	0.052220	0.052487

If we only want the equilibrium KPIs for a specific design, we can use .get_kpis(designName)

In [59]:

single_output.get_kpis("Lower NP")

single_output.get_kpis("Lower NP")

Out[59]:

KPI
Capacity [Ah]                           4.626559
Nominal Voltage [V]                     3.659476
Energy [Wh]                            16.930780
Gravimetric Energy Density [Wh/kg]    246.933081
Volumetric Energy Density [Wh/l]      684.554009
Minimum Anode Voltage [mV]             77.407029
Weight [g]                             68.564246
Volume [l]                              0.024733
Heat Capacity [kJ/K]                    0.052487
Name: Lower NP, dtype: float64

We can now compare the results of our virtual cell design with the baseline cell.

In [60]:

single_output.compare_designs()

single_output.compare_designs()

In [61]:

single_output.plot_voltage_response()

single_output.plot_voltage_response()

In [62]:

single_output.print_dynamic_variables()

single_output.print_dynamic_variables()

Available variables:
Time [s]
Current [A]
Charge current [A]
Discharge current [A]
Voltage [V]
Anode voltage [V]
Cathode voltage [V]
ocvAveCell [V]
ocvAveAnode [V]
ocvAveCathode [V]
SoC
Negative particle average stoichiometry
Positive particle average stoichiometry
Cell temperature [°C]
Heat generation total [W]

In [63]:

single_output.plot_dynamic_response("Cell temperature [°C]")

single_output.plot_dynamic_response("Cell temperature [°C]")

Batch Results¶

Now we scale up: pass lists (e.g., initialSoC) to evaluate multiple starting conditions in one call.

• Use .get_summary() for an at‑a‑glance table across runs
• Use .get_dynamic_data(design) to retrieve time‑series data
• Use .plot_dynamic_response(variable) to visualize across designs/runs

In [64]:

initial_socs = [0.1, 0.25, 0.5, 0.75, 0.9]
batch_output = api.run_sim(
    base_battery="Molicel P45B",
    cycler=cycler.cc_chg(1.0, 4.2),
    designs=designs,
    initialSoC=initial_socs,  # List of SOC values
    initialTemperature_degC=25.0,
    ambientTemperature_degC=25.0,
)

initial_socs = [0.1, 0.25, 0.5, 0.75, 0.9] batch_output = api.run_sim( base_battery="Molicel P45B", cycler=cycler.cc_chg(1.0, 4.2), designs=designs, initialSoC=initial_socs, # List of SOC values initialTemperature_degC=25.0, ambientTemperature_degC=25.0, )

Running...:   0%|          | 0/1 [00:00<?, ? designs/s]


Running...: 100%|██████████| 1/1 [00:07<00:00,  7.14s/ designs]


Running...: 100%|██████████| 1/1 [00:26<00:00, 26.12s/ designs]

Running...: 100%|██████████| 1/1 [00:26<00:00, 26.19s/ designs]
Running...: 100%|██████████| 1/1 [00:28<00:00, 28.60s/ designs]
Running...: 100%|██████████| 1/1 [00:35<00:00, 35.84s/ designs]

Here we get a different class but the methods should be the very similar

In [65]:

batch_output.__class__

batch_output.__class__

Out[65]:

breathe_design.results.BatchSimulationResults

In [66]:

for attr in dir(batch_output):
    if not attr.startswith("_"):
        print(f"{attr}")

for attr in dir(batch_output): if not attr.startswith("_"): print(f"{attr}")

baseline
capacity
compare_designs
design_names
dynamic_data
dynamic_kpis
first
get_dynamic_data
get_input_parameters
get_kpis
get_normalized_kpis
get_summary
num_simulations
plot_dynamic_response
plot_radar
plot_sensitivities
plot_voltage_response
print_dynamic_variables

In [67]:

batch_output.get_summary()

batch_output.get_summary()

Out[67]:

{'is_batch': True,
 'num_simulations': 5,
 'design_names': ['Baseline', 'Lower NP'],
 'num_designs': 2,
 'input_parameters': [{'base_battery': 'Molicel P45B',
   'cycler': {'cycle_type': 'CC_CHG',
    'selected_unit': 'C',
    'control_parameters': {'I_chg': 1.0, 'V_max': 4.2}},
   'designs': [{'designName': 'Lower NP', 'NPratio': 1.0}],
   'socCell_0': 0.1,
   'initialTemperature_degC': 25.0,
   'ambientTemperature_degC': 25.0,
   'heatTransferCoefficient': 35.0},
  {'base_battery': 'Molicel P45B',
   'cycler': {'cycle_type': 'CC_CHG',
    'selected_unit': 'C',
    'control_parameters': {'I_chg': 1.0, 'V_max': 4.2}},
   'designs': [{'designName': 'Lower NP', 'NPratio': 1.0}],
   'socCell_0': 0.25,
   'initialTemperature_degC': 25.0,
   'ambientTemperature_degC': 25.0,
   'heatTransferCoefficient': 35.0},
  {'base_battery': 'Molicel P45B',
   'cycler': {'cycle_type': 'CC_CHG',
    'selected_unit': 'C',
    'control_parameters': {'I_chg': 1.0, 'V_max': 4.2}},
   'designs': [{'designName': 'Lower NP', 'NPratio': 1.0}],
   'socCell_0': 0.5,
   'initialTemperature_degC': 25.0,
   'ambientTemperature_degC': 25.0,
   'heatTransferCoefficient': 35.0},
  {'base_battery': 'Molicel P45B',
   'cycler': {'cycle_type': 'CC_CHG',
    'selected_unit': 'C',
    'control_parameters': {'I_chg': 1.0, 'V_max': 4.2}},
   'designs': [{'designName': 'Lower NP', 'NPratio': 1.0}],
   'socCell_0': 0.75,
   'initialTemperature_degC': 25.0,
   'ambientTemperature_degC': 25.0,
   'heatTransferCoefficient': 35.0},
  {'base_battery': 'Molicel P45B',
   'cycler': {'cycle_type': 'CC_CHG',
    'selected_unit': 'C',
    'control_parameters': {'I_chg': 1.0, 'V_max': 4.2}},
   'designs': [{'designName': 'Lower NP', 'NPratio': 1.0}],
   'socCell_0': 0.9,
   'initialTemperature_degC': 25.0,
   'ambientTemperature_degC': 25.0,
   'heatTransferCoefficient': 35.0}]}

In [68]:

batch_output.get_kpis()

batch_output.get_kpis()

Out[68]:

	Baseline	Lower NP
KPI
Capacity [Ah]	4.500000	4.626559
Nominal Voltage [V]	3.656630	3.659476
Energy [Wh]	16.454837	16.930780
Gravimetric Energy Density [Wh/kg]	241.196608	246.933081
Volumetric Energy Density [Wh/l]	665.310409	684.554009
Minimum Anode Voltage [mV]	80.490746	77.407029
Weight [g]	68.221675	68.564246
Volume [l]	0.024733	0.024733
Heat Capacity [kJ/K]	0.052220	0.052487

We can plot the variables of interest for the designs across the batch

In [69]:

batch_output.plot_dynamic_response("Voltage [V]")

batch_output.plot_dynamic_response("Voltage [V]")

In [70]:

batch_output.plot_dynamic_response("Cell temperature [°C]")

batch_output.plot_dynamic_response("Cell temperature [°C]")

We can also retrieve all of the dynamic data for the batch for all designs, or for a single design

In [71]:

batch_output.dynamic_data

batch_output.dynamic_data

Out[71]:

[{'Baseline': {'Time [s]': [0.0,
    3.0967565843459512,
    6.1935131686919025,
    9.290269753037855,
    12.387026337383805,
    15.483782921729755,
    18.58053950607571,
    21.67729609042166,
    24.77405267476761,
    27.87080925911356,
    30.96756584345951,
    34.064322427805465,
    37.16107901215142,
    40.257835596497365,
    43.35459218084332,
    46.451348765189266,
    49.54810534953522,
    52.644861933881174,
    55.74161851822712,
    58.838375102573075,
    61.93513168691902,
    65.03188827126498,
    68.12864485561093,
    71.22540143995688,
    74.32215802430284,
    77.41891460864878,
    80.51567119299473,
    83.61242777734068,
    86.70918436168664,
    89.80594094603259,
    92.90269753037853,
    95.99945411472449,
    99.09621069907044,
    102.1929672834164,
    105.28972386776235,
    108.38648045210829,
    111.48323703645424,
    114.5799936208002,
    117.67675020514615,
    120.7735067894921,
    123.87026337383804,
    126.967019958184,
    130.06377654252995,
    133.1605331268759,
    136.25728971122186,
    139.3540462955678,
    142.45080287991377,
    145.5475594642597,
    148.64431604860567,
    151.7410726329516,
    154.83782921729755,
    157.93458580164352,
    161.03134238598946,
    164.12809897033543,
    167.22485555468137,
    170.3216121390273,
    173.41836872337328,
    176.51512530771922,
    179.61188189206518,
    182.70863847641112,
    185.80539506075706,
    188.90215164510303,
    191.99890822944897,
    195.09566481379494,
    198.19242139814088,
    201.28917798248682,
    204.3859345668328,
    207.48269115117873,
    210.5794477355247,
    213.67620431987064,
    216.77296090421657,
    219.86971748856254,
    222.96647407290848,
    226.06323065725445,
    229.1599872416004,
    232.25674382594633,
    235.3535004102923,
    238.45025699463824,
    241.5470135789842,
    244.64377016333015,
    247.74052674767609,
    250.83728333202205,
    253.934039916368,
    257.03079650071396,
    260.1275530850599,
    263.22430966940584,
    266.3210662537518,
    269.4178228380978,
    272.5145794224437,
    275.61133600678966,
    278.7080925911356,
    281.80484917548154,
    284.90160575982753,
    287.9983623441735,
    291.0951189285194,
    294.19187551286535,
    297.28863209721135,
    300.3853886815573,
    303.4821452659032,
    306.57890185024917,
    309.6756584345951,
    312.7724150189411,
    315.86917160328704,
    318.965928187633,
    322.0626847719789,
    325.15944135632486,
    328.25619794067086,
    331.3529545250168,
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    353.03025061543843,
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    374.7075467058601,
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    390.1913296275899,
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    396.38484279628176,
    399.4815993806277,
    402.57835596497364,
    405.67511254931964,
    408.7718691336656,
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    414.96538230235745,
    418.0621388867034,
    421.1588954710494,
    424.25565205539533,
    427.35240863974127,
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    433.54592180843315,
    436.64267839277915,
    439.7394349771251,
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    445.93294814581697,
    449.0297047301629,
    452.1264613145089,
    455.22321789885484,
    458.3199744832008,
    461.4167310675467,
    464.51348765189266,
    467.61024423623866,
    470.7070008205846,
    473.80375740493054,
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    479.9972705736224,
    483.0940271579684,
    486.19078374231435,
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    495.48105349535217,
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    510.964836417082,
    514.0615930014279,
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    746.3183368273742,
    749.4150934117202,
    752.5118499960662,
    755.6086065804121,
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    761.802119749104,
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    771.0923895021418,
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In [72]:

batch_output.get_dynamic_data("Baseline")

batch_output.get_dynamic_data("Baseline")

Out[72]:

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