Getting started¶

In this notebook, we will demonstrate how to use the Breathe Design API to get started.

In [1]:

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()

Lets see what batteries are available on the service

In [2]:

# see what batteries are available on the service
batteries = api.get_batteries()
print(batteries)

# see what batteries are available on the service batteries = api.get_batteries() print(batteries)

['Molicel P45B']

Now we can access the design parameters of the battery

In [3]:

base_params = api.get_design_parameters("Molicel P45B")

base_params = api.get_design_parameters("Molicel P45B")

In [4]:

base_params

base_params

Out[4]:

{'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}

And finally, let's get the equilibrium KPIs for the battery. These are a high level summary of the battery's performance without doing any cycling.

In [5]:

eqm_kpis = api.get_eqm_kpis("Molicel P45B")

eqm_kpis = api.get_eqm_kpis("Molicel P45B")

In [6]:

eqm_kpis.get_kpis()

eqm_kpis.get_kpis()

Out[6]:

	Baseline
KPI
Capacity [Ah]	4.500000
Nominal Voltage [V]	3.656630
Energy [Wh]	16.454837
Gravimetric Energy Density [Wh/kg]	241.196608
Volumetric Energy Density [Wh/l]	665.310409
Minimum Anode Voltage [mV]	80.490746
Weight [g]	68.221675
Volume [l]	0.024733
Heat Capacity [kJ/K]	0.052220

The sensitivities of the battery's performance to changes in its design can be calculated using the get_sensitivities function.

In [7]:

eqm_kpis.plot_sensitivities()

eqm_kpis.plot_sensitivities()

This view shows how sensitive each equilibrium KPI (x-axis) is to a given adjustable cell-design parameter (y-axis).
Each cell is a normalised sensitivity relative to the baseline cell, capturing both direction and strength of impact.

• Green = increase in the KPI (uplift vs. baseline)

• Grey = decrease in the KPI (reduction vs. baseline)

• Whiter/near zero = little to no effect

• Larger absolute values = stronger sensitivity

• Scan across a row to see which KPIs respond most to changing that parameter.

• Scan down a column to spot the parameters that most influence that KPI.