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
price_usd                                    NaN
price_usdkwh                                 NaN
price_usdkg                                  NaN
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
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...: 100%|██████████| 1/1 [00:06<00:00,  6.89s/ 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
price_usd	NaN	NaN
price_usdkwh	NaN	NaN
price_usdkg	NaN	NaN

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
price_usd                                    NaN
price_usdkwh                                 NaN
price_usdkg                                  NaN
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 [00:03<00:00,  3.29s/ designs]



Running...: 100%|██████████| 1/1 [00:03<00:00,  3.42s/ designs]
Running...: 100%|██████████| 1/1 [00:03<00:00,  3.68s/ designs]
Running...: 100%|██████████| 1/1 [00:04<00:00,  4.18s/ designs]

Running...: 100%|██████████| 1/1 [00:02<00:00,  2.87s/ 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
price_usd	NaN	NaN
price_usdkwh	NaN	NaN
price_usdkg	NaN	NaN

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,
    3.0945477302709894,
    6.189095460541979,
    9.283643190812969,
    12.378190921083958,
    15.472738651354947,
    18.567286381625937,
    21.661834111896926,
    24.756381842167915,
    27.850929572438904,
    30.945477302709893,
    34.040025032980886,
    37.134572763251875,
    40.22912049352286,
    43.32366822379385,
    46.41821595406484,
    49.51276368433583,
    52.60731141460682,
    55.70185914487781,
    58.7964068751488,
    61.890954605419786,
    64.98550233569078,
    68.08005006596177,
    71.17459779623276,
    74.26914552650375,
    77.36369325677474,
    80.45824098704573,
    83.55278871731672,
    86.6473364475877,
    89.7418841778587,
    92.83643190812968,
    95.93097963840067,
    99.02552736867166,
    102.12007509894265,
    105.21462282921364,
    108.30917055948463,
    111.40371828975562,
    114.4982660200266,
    117.5928137502976,
    120.68736148056858,
    123.78190921083957,
    126.87645694111056,
    129.97100467138156,
    133.06555240165255,
    136.16010013192354,
    139.25464786219453,
    142.34919559246552,
    145.4437433227365,
    148.5382910530075,
    151.6328387832785,
    154.72738651354948,
    157.82193424382046,
    160.91648197409145,
    164.01102970436244,
    167.10557743463343,
    170.20012516490442,
    173.2946728951754,
    176.3892206254464,
    179.4837683557174,
    182.57831608598838,
    185.67286381625937,
    188.76741154653035,
    191.86195927680134,
    194.95650700707233,
    198.05105473734332,
    201.1456024676143,
    204.2401501978853,
    207.3346979281563,
    210.42924565842728,
    213.52379338869827,
    216.61834111896925,
    219.71288884924024,
    222.80743657951123,
    225.90198430978222,
    228.9965320400532,
    232.0910797703242,
    235.1856275005952,
    238.28017523086618,
    241.37472296113717,
    244.46927069140816,
    247.56381842167914,
    250.65836615195013,
    253.75291388222112,
    256.8474616124921,
    259.94200934276313,
    263.0365570730341,
    266.1311048033051,
    269.22565253357607,
    272.3202002638471,
    275.41474799411804,
    278.50929572438906,
    281.60384345466,
    284.69839118493104,
    287.792938915202,
    290.887486645473,
    293.982034375744,
    297.076582106015,
    300.17112983628596,
    303.265677566557,
    306.36022529682793,
    309.45477302709895,
    312.5493207573699,
    315.64386848764093,
    318.7384162179119,
    321.8329639481829,
    324.92751167845387,
    328.0220594087249,
    331.11660713899585,
    334.21115486926686,
    337.3057025995378,
    340.40025032980884,
    343.4947980600798,
    346.5893457903508,
    349.6838935206218,
    352.7784412508928,
    355.87298898116376,
    358.9675367114348,
    362.06208444170574,
    365.15663217197675,
    368.2511799022477,
    371.34572763251873,
    374.4402753627897,
    377.5348230930607,
    380.62937082333167,
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    386.81846628387365,
    389.91301401414466,
    393.0075617444156,
    396.10210947468664,
    399.19665720495766,
    402.2912049352286,
    405.38575266549964,
    408.4803003957706,
    411.5748481260416,
    414.6693958563126,
    417.7639435865836,
    420.85849131685455,
    423.95303904712557,
    427.04758677739653,
    430.14213450766755,
    433.2366822379385,
    436.3312299682095,
    439.4257776984805,
    442.5203254287515,
    445.61487315902247,
    448.7094208892935,
    451.80396861956444,
    454.89851634983546,
    457.9930640801064,
    461.08761181037744,
    464.1821595406484,
    467.2767072709194,
    470.3712550011904,
    473.4658027314614,
    476.56035046173236,
    479.6548981920034,
    482.74944592227433,
    485.84399365254535,
    488.9385413828163,
    492.0330891130873,
    495.1276368433583,
    498.2221845736293,
    501.31673230390027,
    504.4112800341713,
    507.50582776444224,
    510.60037549471326,
    513.6949232249842,
    516.7894709552552,
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    529.1676618763391,
    532.2622096066102,
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    538.4513050671521,
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    544.6404005276942,
    547.7349482579651,
    550.8294959882361,
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    643.6659278963658,
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    649.8550233569077,
    652.9495710871788,
    656.0441188174498,
    659.1386665477207,
    662.2332142779917,
    665.3277620082628,
    668.4223097385337,
    671.5168574688047,
    674.6114051990756,
    677.7059529293467,
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    683.8950483898886,
    686.9895961201596,
    690.0841438504307,
    693.1786915807016,
    696.2732393109726,
    699.3677870412436,
    702.4623347715146,
    705.5568825017856,
    708.6514302320566,
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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 8e4a326f