Getting started¶
In this notebook, we will demonstrate how to use the Breathe Simulate API to get started.
from breathe_simulate import api_interface as api
from breathe_simulate import enable_notebook_plotly
enable_notebook_plotly()
Lets see what batteries are available on the service
# 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
base_params = api.get_design_parameters("Molicel P45B")
base_params
{'NLiRatio': 1.192712619876931,
'NPratio': 1.039106051327928,
'Vmax_V': 4.200003037059671,
'Vmin_V': 2.4999805201342147,
'aluminiumThickness_um': 22.2,
'anode': 'molicel_p45b_anode',
'anodeArealLoading_mAhcm2': 3.8996207810294305,
'anodeCathodeAreaRatio': 1.065290648694187,
'anodeMassLoading_mgcm2': 8.44896611475494,
'anodePorosity': 0.16663641853607114,
'anodeTabResistance_uOhmAh': 0.0,
'anodeThickness_um': 48.90000000000002,
'capacityVariationPercent': 0.0,
'cathode': 'molicel_p45b_cathode',
'cathodeArealLoading_mAhcm2': 3.7528611983790308,
'cathodeMassLoading_mgcm2': 14.188761420434503,
'cathodePorosity': 0.14216134985845785,
'cathodeTabResistance_uOhmAh': 0.0,
'cathodeThickness_um': 37.48333333333334,
'concentration_molL': 1.0,
'copperThickness_um': 11.2,
'electrodeArea_cm2': 1495.6199999999994,
'electrolyte': 'molicel_p45b_electrolyte',
'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.
eqm_kpis = api.get_eqm_kpis("Molicel P45B")
eqm_kpis.get_kpis()
| Baseline | |
|---|---|
| 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 |
| thermal_conductivity_in_w_mk | 291.658683 |
| thermal_conductivity_through_w_mk | 274.511028 |
| price_usd | 1.157604 |
| price_usdkwh | 70.350411 |
| price_usdkg | 16.968278 |
The sensitivities of the battery's performance to changes in its design can be calculated using the get_sensitivities function.
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.