Redesign a battery¶

In this example we will redesign a battery. As before we import the module and access the baseline design parameters for the Molicel P45B battery. We then create a list of designs that we want to explore. Each design is represented as a dictionary with a design name and the corresponding equilibrium KPIs.

In [49]:

from breathe_design import api_interface as api
from breathe_design import enable_notebook_plotly

enable_notebook_plotly()

from breathe_design import api_interface as api from breathe_design import enable_notebook_plotly enable_notebook_plotly()

In [50]:

base_params = api.get_design_parameters("Molicel P45B")

base_params = api.get_design_parameters("Molicel P45B")

In [51]:

base_params

base_params

Out[51]:

{'anode': 'Molicel P45B Anode',
 'cathode': 'Molicel P45B Cathode',
 'NPratio': 1.039106051327928,
 'Vmin_V': 2.5,
 'Vmax_V': 4.2,
 'cathodePorosity': 0.14216134985845785,
 'anodePorosity': 0.16663641853607114,
 'cathodeThickness_um': 37.483333333333334,
 'anodeThickness_um': 48.900000000000006,
 'copperThickness_um': 11.2,
 'aluminumThickness_um': 22.2,
 'separatorThickness_um': 16.8,
 'format': 'Molicel P45B',
 'electrolyte': 'LP30',
 'electrolyteBuffer_rel': 0.2,
 'lampe': 0,
 'lamne': 0,
 'lli': 0}

Each of the design parameters can be adjusted to explore different designs. It is best not to make too large adjustments as this could lead to unrealistic results.

In [52]:

designs = [
    {"designName": "Lower NP", "NPratio": base_params["NPratio"] * 0.95},
    {"designName": "Higher Vmax", "Vmax_V": base_params["Vmax_V"] + 0.05},
    {
        "designName": "Thicker Cathode",
        "cathodeThickness_um": base_params["cathodeThickness_um"] * 1.05,
    },
    {
        "designName": "Less Porous Anode",
        "anodePorosity": base_params["anodePorosity"] * 0.95,
    },
    {
        "designName": "Thinner Separator",
        "separatorThickness_um": base_params["separatorThickness_um"] * 0.95,
    },
]

designs = [ {"designName": "Lower NP", "NPratio": base_params["NPratio"] * 0.95}, {"designName": "Higher Vmax", "Vmax_V": base_params["Vmax_V"] + 0.05}, { "designName": "Thicker Cathode", "cathodeThickness_um": base_params["cathodeThickness_um"] * 1.05, }, { "designName": "Less Porous Anode", "anodePorosity": base_params["anodePorosity"] * 0.95, }, { "designName": "Thinner Separator", "separatorThickness_um": base_params["separatorThickness_um"] * 0.95, }, ]

Now we can calculate all the equilibrium KPIs for each design using the get_eqm_kpis function.

In [53]:

results = api.get_eqm_kpis("Molicel P45B", designs)

results = api.get_eqm_kpis("Molicel P45B", designs)

In [54]:

results.get_kpis()

results.get_kpis()

Out[54]:

	Baseline	Lower NP	Higher Vmax	Thicker Cathode	Less Porous Anode	Thinner Separator
KPI
Capacity [Ah]	4.500000	4.668083	4.546820	4.541323	4.520975	4.541918
Nominal Voltage [V]	3.656630	3.660280	3.662353	3.656630	3.656630	3.656630
Energy [Wh]	16.454837	17.086491	16.652063	16.605938	16.531535	16.608117
Gravimetric Energy Density [Wh/kg]	241.196608	248.788314	244.087566	244.134852	241.873341	242.447412
Volumetric Energy Density [Wh/l]	665.310409	690.849797	673.284752	671.419814	668.411531	671.507913
Minimum Anode Voltage [mV]	80.490746	75.325513	80.035853	80.490746	80.490746	80.490746
Weight [g]	68.221675	68.678834	68.221675	68.019531	68.347902	68.501935
Volume [l]	0.024733	0.024733	0.024733	0.024733	0.024733	0.024733
Heat Capacity [kJ/K]	0.052220	0.052576	0.052220	0.052093	0.052232	0.052303

These can be plotted using the compare_designs function in two different ways: one for a relative change in the KPIs and one for a delta change in the KPIs.

In [55]:

results.compare_designs("relative")

results.compare_designs("relative")

In [56]:

results.compare_designs("delta")

results.compare_designs("delta")