Getting started¶
In this notebook, we will demonstrate how to use the Breathe Design API to get started.
from breathe_design import api_interface as api
from breathe_design import enable_notebook_plotly
enable_notebook_plotly()
In order to use the API, we need to log in. Before we can do this please send a registration request to the service using the following link: https://api.breathe-design.com/register
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
{'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}
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] | 16.454837 |
| 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.
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.