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 [29]:

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 [30]:

base_params = api.get_design_parameters("Molicel P45B")
base_params

base_params = api.get_design_parameters("Molicel P45B") base_params

Out[30]:

{'NPratio': 1.039106051327928,
 'Vmax_V': 4.2,
 'Vmin_V': 2.5,
 'aluminiumThickness_um': 22.2,
 'anode': 'molicel_p45b_anode',
 'anodeArealLoading_mAhcm2': 3.8996207810294305,
 'anodeMassLoading_mgcm2': 8.44896611475494,
 'anodePorosity': 0.16663641853607114,
 'anodeThickness_um': 48.90000000000002,
 'cathode': 'molicel_p45b_cathode',
 'cathodeArealLoading_mAhcm2': 3.7528611983790308,
 'cathodeMassLoading_mgcm2': 14.188761420434503,
 'cathodePorosity': 0.14216134985845785,
 'cathodeThickness_um': 37.48333333333334,
 'copperThickness_um': 11.2,
 'electrolyte': 'lp30',
 'electrolyteBuffer_rel': 0.2,
 'format': 'molicel_p45b',
 'lamne': 0.0,
 'lampe': 0.0,
 'lli': 0.0,
 'separatorThickness_um': 16.8}

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 [31]:

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, }, ]

You can view and switch between the different electrodes in your library. If you have multiple cells in Breathe Design, you can also switch between their electrodes.

In [32]:

api.get_active_materials()

api.get_active_materials()

Out[32]:

	name	formationLoss	trueDensity_gcm3	heatCapacity_kJkgK	specificCapacity_mAhg
0	molicel_p45b_anode	0.210554	2.15	0.71	496.290382
1	molicel_p45b_cathode	0.090000	4.68	0.80	275.515973

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

In [33]:

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

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

Running...: 100%|██████████| 5/5 [00:05<00:00,  1.11s/ designs]

In [34]:

results.get_kpis()

results.get_kpis()

Out[34]:

	Baseline	Lower NP	Higher Vmax	Thicker Cathode	Less Porous Anode	Thinner Separator
KPI
Capacity [Ah]	4.500000	4.668083	4.546820	4.646725	4.500000	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	16.991355	16.454837	16.608117
Gravimetric Energy Density [Wh/kg]	241.196608	248.788314	244.087566	248.185356	241.065108	242.447412
Volumetric Energy Density [Wh/l]	665.310409	690.849797	673.284752	687.003187	665.310409	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	68.462359	68.258890	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	0.052439	0.052162	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 [35]:

results.compare_designs("relative")

results.compare_designs("relative")

In [36]:

results.compare_designs("delta")

results.compare_designs("delta")