Dynamic analysis II¶

In this example we simulate different SOC breakpoints for the baseline and virtual cell with 5% less NPratio, and compare the capacity and DCIR.

In [12]:

from breathe_design import api_interface as api
from breathe_design import Cycler
from breathe_design import enable_notebook_plotly
import plotly.express as px

enable_notebook_plotly()

from breathe_design import api_interface as api from breathe_design import Cycler from breathe_design import enable_notebook_plotly import plotly.express as px enable_notebook_plotly()

As before, we first import the necessary modules and define the base battery, cycler, and designs.

In [13]:

base_params = api.get_design_parameters("Molicel P45B")
eqm_results = api.get_eqm_kpis("Molicel P45B")
designs = [{"designName": "Lower NP", "NPratio": base_params["NPratio"] * 0.95}]
baseline_capacity = eqm_results.capacity
cycler = Cycler(selected_unit="C", cell_capacity=baseline_capacity)

base_params = api.get_design_parameters("Molicel P45B") eqm_results = api.get_eqm_kpis("Molicel P45B") designs = [{"designName": "Lower NP", "NPratio": base_params["NPratio"] * 0.95}] baseline_capacity = eqm_results.capacity cycler = Cycler(selected_unit="C", cell_capacity=baseline_capacity)

We can pass a list of SOC breakpoints to the initialSoC parameter of the run_sim function to simulate different SOCs.

In [14]:

# define some SOC breakpoints we want to simulate
soc_bps = [0.25, 0.5, 0.75]
# run a batch of simulations for different SOC breakpoints
output = api.run_sim(
    base_battery="Molicel P45B",
    cycler=cycler.cc_chg(1.0, 4.2),
    designs=designs,
    initialSoC=soc_bps,
    initialTemperature_degC=21.0,
)

# define some SOC breakpoints we want to simulate soc_bps = [0.25, 0.5, 0.75] # run a batch of simulations for different SOC breakpoints output = api.run_sim( base_battery="Molicel P45B", cycler=cycler.cc_chg(1.0, 4.2), designs=designs, initialSoC=soc_bps, initialTemperature_degC=21.0, )

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The dynamic output data is now returned as a list of dictionaries, where each dictionary contains the simulation results for a given SOC breakpoint.

In [15]:

output.plot_voltage_response()

output.plot_voltage_response()

Let's also take this a step further and run a DCIR test. Here we can define different arguments that can be tailored to your specific requirements.

In [16]:

cycler_dict = cycler.dcir(
    I_app=-1.0, t_dur=60.0, t_rest_before=3.0, t_rest_after=200.0, V_min=2.5, V_max=4.2
)

cycler_dict = cycler.dcir( I_app=-1.0, t_dur=60.0, t_rest_before=3.0, t_rest_after=200.0, V_min=2.5, V_max=4.2 )

We will also define the initial voltage instead of the inital SoC.

In [17]:

output = api.run_sim(
    base_battery="Molicel P45B",
    cycler=cycler_dict,
    designs=designs,
    initialVoltage=3.5,
    # initialSoC=0.5,
    initialTemperature_degC=25.0,
)

output = api.run_sim( base_battery="Molicel P45B", cycler=cycler_dict, designs=designs, initialVoltage=3.5, # initialSoC=0.5, initialTemperature_degC=25.0, )

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Lets take a look at the current profile, the discharge current for the baseline cell is lower since it has a lower nominal capacity compared to the virtual cell we have decided to call "Lower NP".

In [18]:

output.plot_dynamic_response("Charge current [A]")

output.plot_dynamic_response("Charge current [A]")

In [19]:

output.plot_voltage_response()

output.plot_voltage_response()

In [20]:

output.dynamic_kpis("DCIR")

output.dynamic_kpis("DCIR")

Out[20]:

	Design	Resistance (Ω)
0	Baseline	-0.025112
1	Lower NP	-0.024430

Now lets look at the rate capacity for different discharging rates down to 2.6 V, stoarting from 100% SOC.

In [21]:

rate_capacity_results = []

for rate in [1.0, 2.0, 3.0]:
    cycler_input = cycler.cc_dch(-1 * rate, 2.6)
    output = api.run_sim(
        base_battery="Molicel P45B",
        cycler=cycler_input,
        designs=designs,
        initialSoC=1.0,
        initialTemperature_degC=50.0,
    )
    dynamic_kpis = output.dynamic_kpis("RateCap")
    rate_capacity_results.append(
        {
            "Rate": rate,
            "Baseline": dynamic_kpis.query("Design == 'Baseline'")[
                "Capacity (Ah)"
            ].values[0],
            "Lower NP": dynamic_kpis.query("Design == 'Lower NP'")[
                "Capacity (Ah)"
            ].values[0],
        }
    )

print(rate_capacity_results)

rate_capacity_results = [] for rate in [1.0, 2.0, 3.0]: cycler_input = cycler.cc_dch(-1 * rate, 2.6) output = api.run_sim( base_battery="Molicel P45B", cycler=cycler_input, designs=designs, initialSoC=1.0, initialTemperature_degC=50.0, ) dynamic_kpis = output.dynamic_kpis("RateCap") rate_capacity_results.append( { "Rate": rate, "Baseline": dynamic_kpis.query("Design == 'Baseline'")[ "Capacity (Ah)" ].values[0], "Lower NP": dynamic_kpis.query("Design == 'Lower NP'")[ "Capacity (Ah)" ].values[0], } ) print(rate_capacity_results)

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[{'Rate': 1.0, 'Baseline': np.float64(4.437207460417406), 'Lower NP': np.float64(4.606167420206067)}, {'Rate': 2.0, 'Baseline': np.float64(4.430937494478882), 'Lower NP': np.float64(4.600110445356491)}, {'Rate': 3.0, 'Baseline': np.float64(4.418827133956763), 'Lower NP': np.float64(4.587609971434871)}]

Lets compare the rated capacity over C-Rate

In [22]:

# Create a line plot using Plotly Express
fig = px.line(
    rate_capacity_results, x="Rate", y=["Baseline", "Lower NP"], title="Rate Capacity"
)

# Show the plot
fig.show()

# Create a line plot using Plotly Express fig = px.line( rate_capacity_results, x="Rate", y=["Baseline", "Lower NP"], title="Rate Capacity" ) # Show the plot fig.show()