Results handling¶

This worked example shows how to work with Breathe Simulate 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 Simulate API and cycler package used throughout the worked example

In [1]:

from breathe_simulate import api_interface as api, Cycler

from breathe_simulate 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 [2]:

eqm_results = api.get_eqm_kpis("Molicel P45B")

eqm_results = api.get_eqm_kpis("Molicel P45B")

The results class is

In [3]:

eqm_results.__class__

eqm_results.__class__

Out[3]:

breathe_simulate.results.SingleSimulationResults

With methods and attributes

In [4]:

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 [5]:

eqm_results.baseline

eqm_results.baseline

Out[5]:

KPI
Capacity [Ah]                           4.500000
Nominal Voltage [V]                     3.656630
Energy [Wh]                            16.454834
Gravimetric Energy Density [Wh/kg]    241.196572
Volumetric Energy Density [Wh/l]      665.310310
Minimum Anode Voltage [mV]             80.487709
Weight [g]                             68.221675
Volume [l]                              0.024733
heat_capacity_kjkgk                     1.200000
thermal_conductivity_in_w_mk          291.658683
thermal_conductivity_through_w_mk     274.511028
price_usd                               1.157604
price_usdkwh                           70.350411
price_usdkg                            16.968278
Name: Baseline, dtype: float64

capacity gets the capacity value for the Baseline design

In [6]:

eqm_results.capacity

eqm_results.capacity

Out[6]:

np.float64(4.4999999999999964)

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

In [7]:

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 [8]:

eqm_results.design_names

eqm_results.design_names

Out[8]:

['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 [9]:

eqm_results.dynamic_data

eqm_results.dynamic_data

Out[9]:

{}

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

In [10]:

eqm_results.get_input_parameters()

eqm_results.get_input_parameters()

Out[10]:

{'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 [11]:

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 [12]:

eqm_results.get_normalized_kpis()

eqm_results.get_normalized_kpis()

Out[12]:

	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_kjkgk	1.0
thermal_conductivity_in_w_mk	1.0
thermal_conductivity_through_w_mk	1.0
price_usd	1.0
price_usdkwh	1.0
price_usdkg	1.0

get_summary prints a summary of the results

In [13]:

eqm_results.get_summary()

eqm_results.get_summary()

Out[13]:

{'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 [14]:

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 [15]:

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 [16]:

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 [17]:

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 [18]:

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 [19]:

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 [20]:

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 [21]:

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 [00:07<00:00,  7.32s/ designs]

In [22]:

single_output.kpis

single_output.kpis

Out[22]:

	Baseline	Lower NP
KPI
Capacity [Ah]	4.500000	4.626562
Nominal Voltage [V]	3.656630	3.659476
Energy [Wh]	16.454834	16.930793
Gravimetric Energy Density [Wh/kg]	241.196572	246.933259
Volumetric Energy Density [Wh/l]	665.310310	684.554503
Minimum Anode Voltage [mV]	80.487709	77.406754
Weight [g]	68.221675	68.564246
Volume [l]	0.024733	0.024733
heat_capacity_kjkgk	1.200000	1.197895
thermal_conductivity_in_w_mk	291.658683	291.658683
thermal_conductivity_through_w_mk	274.511028	274.511028
price_usd	1.157604	1.164483
price_usdkwh	70.350411	68.779013
price_usdkg	16.968278	16.983826

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

In [23]:

single_output.get_kpis("Lower NP")

single_output.get_kpis("Lower NP")

Out[23]:

KPI
Capacity [Ah]                           4.626562
Nominal Voltage [V]                     3.659476
Energy [Wh]                            16.930793
Gravimetric Energy Density [Wh/kg]    246.933259
Volumetric Energy Density [Wh/l]      684.554503
Minimum Anode Voltage [mV]             77.406754
Weight [g]                             68.564246
Volume [l]                              0.024733
heat_capacity_kjkgk                     1.197895
thermal_conductivity_in_w_mk          291.658683
thermal_conductivity_through_w_mk     274.511028
price_usd                               1.164483
price_usdkwh                           68.779013
price_usdkg                            16.983826
Name: Lower NP, dtype: float64

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

In [24]:

single_output.compare_designs()

single_output.compare_designs()

In [25]:

single_output.plot_voltage_response()

single_output.plot_voltage_response()

In [26]:

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]
Anode-Separator Reference Potential [mV]
Cathode voltage [V]
Cell open-circuit voltage [V]
Negative electrode OCP with hysteresis [V]
Positive electrode OCP with hysteresis [V]
SoC
Average negative particle stoichiometry
Average positive particle stoichiometry
Cell temperature [°C]
Heat generation total [W]

In [27]:

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 [28]:

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 [00:04<00:00,  4.61s/ designs]
Running...: 100%|██████████| 1/1 [00:06<00:00,  6.79s/ designs]


Running...: 100%|██████████| 1/1 [00:07<00:00,  7.67s/ designs]

Running...: 100%|██████████| 1/1 [00:09<00:00,  9.29s/ designs]
Running...: 100%|██████████| 1/1 [00:04<00:00,  4.74s/ designs]

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

In [29]:

batch_output.__class__

batch_output.__class__

Out[29]:

breathe_simulate.results.BatchSimulationResults

In [30]:

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 [31]:

batch_output.get_summary()

batch_output.get_summary()

Out[31]:

{'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,
   'isothermal': False,
   'initialOCVType': None},
  {'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,
   'isothermal': False,
   'initialOCVType': None},
  {'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,
   'isothermal': False,
   'initialOCVType': None},
  {'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,
   'isothermal': False,
   'initialOCVType': None},
  {'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,
   'isothermal': False,
   'initialOCVType': None}]}

In [32]:

batch_output.get_kpis()

batch_output.get_kpis()

Out[32]:

	Baseline	Lower NP
KPI
Capacity [Ah]	4.500000	4.626562
Nominal Voltage [V]	3.656630	3.659476
Energy [Wh]	16.454834	16.930793
Gravimetric Energy Density [Wh/kg]	241.196572	246.933259
Volumetric Energy Density [Wh/l]	665.310310	684.554503
Minimum Anode Voltage [mV]	80.487709	77.406754
Weight [g]	68.221675	68.564246
Volume [l]	0.024733	0.024733
heat_capacity_kjkgk	1.200000	1.197895
thermal_conductivity_in_w_mk	291.658683	291.658683
thermal_conductivity_through_w_mk	274.511028	274.511028
price_usd	1.157604	1.164483
price_usdkwh	70.350411	68.779013
price_usdkg	16.968278	16.983826

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

In [33]:

batch_output.plot_dynamic_response("Voltage [V]")

batch_output.plot_dynamic_response("Voltage [V]")

In [34]:

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 [35]:

batch_output.dynamic_data

batch_output.dynamic_data

Out[35]:

[{'Baseline': {'Time [s]': [0.0,
    3.0944708346150027,
    6.188941669230005,
    9.283412503845009,
    12.37788333846001,
    15.472354173075013,
    18.566825007690017,
    21.66129584230502,
    24.75576667692002,
    27.850237511535024,
    30.944708346150026,
    34.03917918076503,
    37.133650015380034,
    40.22812084999504,
    43.32259168461004,
    46.41706251922504,
    49.51153335384004,
    52.606004188455046,
    55.70047502307005,
    58.79494585768505,
    61.88941669230005,
    64.98388752691505,
    68.07835836153006,
    71.17282919614506,
    74.26730003076007,
    77.36177086537506,
    80.45624169999007,
    83.55071253460507,
    86.64518336922008,
    89.73965420383507,
    92.83412503845008,
    95.92859587306508,
    99.02306670768009,
    102.1175375422951,
    105.21200837691009,
    108.3064792115251,
    111.4009500461401,
    114.4954208807551,
    117.5898917153701,
    120.68436254998511,
    123.7788333846001,
    126.87330421921511,
    129.9677750538301,
    133.06224588844512,
    136.15671672306013,
    139.2511875576751,
    142.34565839229012,
    145.44012922690513,
    148.53460006152014,
    151.62907089613515,
    154.72354173075013,
    157.81801256536514,
    160.91248339998015,
    164.00695423459516,
    167.10142506921014,
    170.19589590382515,
    173.29036673844016,
    176.38483757305517,
    179.47930840767015,
    182.57377924228516,
    185.66825007690016,
    188.76272091151517,
    191.85719174613016,
    194.95166258074516,
    198.04613341536017,
    201.14060424997518,
    204.2350750845902,
    207.32954591920517,
    210.42401675382018,
    213.5184875884352,
    216.6129584230502,
    219.70742925766518,
    222.8019000922802,
    225.8963709268952,
    228.9908417615102,
    232.0853125961252,
    235.1797834307402,
    238.2742542653552,
    241.36872509997022,
    244.4631959345852,
    247.5576667692002,
    250.65213760381522,
    253.74660843843023,
    256.84107927304524,
    259.9355501076602,
    263.03002094227526,
    266.12449177689024,
    269.2189626115052,
    272.31343344612026,
    275.40790428073524,
    278.5023751153502,
    281.59684594996526,
    284.69131678458024,
    287.7857876191953,
    290.88025845381026,
    293.97472928842524,
    297.0692001230403,
    300.16367095765526,
    303.2581417922703,
    306.3526126268853,
    309.44708346150026,
    312.5415542961153,
    315.6360251307303,
    318.73049596534526,
    321.8249667999603,
    324.9194376345753,
    328.0139084691903,
    331.1083793038053,
    334.2028501384203,
    337.2973209730353,
    340.3917918076503,
    343.4862626422653,
    346.5807334768803,
    349.6752043114953,
    352.76967514611033,
    355.8641459807253,
    358.9586168153403,
    362.05308764995533,
    365.1475584845703,
    368.24202931918535,
    371.33650015380033,
    374.4309709884153,
    377.52544182303035,
    380.61991265764533,
    383.7143834922603,
    386.80885432687535,
    389.90332516149033,
    392.99779599610537,
    396.09226683072035,
    399.18673766533533,
    402.28120849995037,
    405.37567933456535,
    408.4701501691804,
    411.56462100379537,
    414.65909183841035,
    417.7535626730254,
    420.84803350764037,
    423.94250434225535,
    427.0369751768704,
    430.13144601148537,
    433.2259168461004,
    436.3203876807154,
    439.41485851533037,
    442.5093293499454,
    445.6038001845604,
    448.6982710191754,
    451.7927418537904,
    454.8872126884054,
    457.9816835230204,
    461.0761543576354,
    464.1706251922504,
    467.2650960268654,
    470.3595668614804,
    473.45403769609544,
    476.5485085307104,
    479.6429793653254,
    482.73745019994044,
    485.8319210345554,
    488.9263918691704,
    492.02086270378544,
    495.1153335384004,
    498.20980437301546,
    501.30427520763044,
    504.3987460422454,
    507.49321687686046,
    510.58768771147544,
    513.6821585460905,
    516.7766293807055,
    519.8711002153204,
    522.9655710499354,
    526.0600418845505,
    529.1545127191655,
    532.2489835537805,
    535.3434543883955,
    538.4379252230104,
    541.5323960576254,
    544.6268668922405,
    547.7213377268555,
    550.8158085614705,
    553.9102793960855,
    557.0047502307004,
    560.0992210653155,
    563.1936918999305,
    566.2881627345455,
    569.3826335691605,
    572.4771044037755,
    575.5715752383906,
    578.6660460730055,
    581.7605169076205,
    584.8549877422355,
    587.9494585768505,
    591.0439294114656,
    594.1384002460806,
    597.2328710806955,
    600.3273419153105,
    603.4218127499255,
    606.5162835845406,
    609.6107544191556,
    612.7052252537705,
    615.7996960883855,
    618.8941669230005,
    621.9886377576155,
    625.0831085922306,
    628.1775794268456,
    631.2720502614605,
    634.3665210960755,
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In [36]:

batch_output.get_dynamic_data("Baseline")

batch_output.get_dynamic_data("Baseline")

Out[36]:

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Version fe1f5cb5