Compare chemistry¶

In this example, we will apply different chemistries when redesigning a cell. We will then compare the equilibrium KPIs.

In [20]:

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 [21]:

base_params = api.get_design_parameters("Molicel P45B")

base_params = api.get_design_parameters("Molicel P45B")

🔐 No active session. Please sign in to continue.

🚀 Open this URL in a browser and verify the code shown is BWJH-JMVV
🔗 https://breathe-platform.uk.auth0.com/activate?user_code=BWJH-JMVV

⏳ Waiting for user authorization...

✅ Authentication successful!

In [22]:

base_params

base_params

Out[22]:

{'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}

Let's explore the active materials available in standard material library.

In [23]:

api.get_active_materials()

api.get_active_materials()

Out[23]:

	name	side	specificCapacity_mAhg	trueDensity_gcm3
0	Graphite	anode	371.068808	2.20
1	Group14	anode	1618.685374	2.26
2	Silicon_nano	anode	3592.146052	2.33
3	LFP	cathode	154.674394	3.60
4	NCM111	cathode	166.135551	4.68
5	NCM622	cathode	184.845912	4.68
6	NCM811	cathode	200.500445	4.68
7	NCM955	cathode	222.052063	4.68

We can explore the effect of changing the active materials on the battery performance.

In [24]:

designs = [
    {"designName": "Group14 Anode", "anode": "Group14"},
    {"designName": "NCM111 Cathode", "cathode": "NCM111"},
    {"designName": "NCM955 Cathode", "cathode": "NCM955"},
    {"designName": "LFP Cathode ", "cathode": "LFP"},
]

designs = [ {"designName": "Group14 Anode", "anode": "Group14"}, {"designName": "NCM111 Cathode", "cathode": "NCM111"}, {"designName": "NCM955 Cathode", "cathode": "NCM955"}, {"designName": "LFP Cathode ", "cathode": "LFP"}, ]

Now we can calculate all the equilibrium key performance indicators (KPIs) for each design using the get_eqm_kpis function.

In [25]:

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

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

In [26]:

results.get_kpis()

results.get_kpis()

Out[26]:

	Baseline	Group14 Anode	NCM111 Cathode	NCM955 Cathode	LFP Cathode
KPI
Capacity [Ah]	4.500000	7.125602	3.325415	4.328095	3.336657
Nominal Voltage [V]	3.656630	3.363872	3.649592	3.736356	3.275997
Energy [Wh]	16.454837	23.969612	12.136409	16.171302	10.930879
Gravimetric Energy Density [Wh/kg]	241.196608	312.139231	167.670080	230.708811	146.690931
Volumetric Energy Density [Wh/l]	665.310409	969.151645	490.705519	653.846394	441.962901
Minimum Anode Voltage [mV]	80.490746	36.693380	80.566125	72.555628	80.819703
Weight [g]	68.221675	76.791410	72.382675	70.093995	74.516391
Volume [l]	0.024733	0.024733	0.024733	0.024733	0.024733
Heat Capacity [kJ/K]	0.052220	0.058865	0.055453	0.053677	0.062563

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 [27]:

results.compare_designs("relative")

results.compare_designs("relative")

In [28]:

results.compare_designs("delta")

results.compare_designs("delta")

We can also explore the effect of changing the composition of the materials on the battery performance by using blends of materials. A typical blend is Graphite and Silicon for the anode

In [29]:

designs = [
    {"designName": "5% Si", "anode": {"Graphite": 0.95, "Silicon_nano": 0.05}},
    {"designName": "10% Si", "anode": {"Graphite": 0.9, "Silicon_nano": 0.1}},
    {"designName": "15% Si", "anode": {"Graphite": 0.85, "Silicon_nano": 0.15}},
    {"designName": "20% Si", "anode": {"Graphite": 0.8, "Silicon_nano": 0.2}},
]

designs = [ {"designName": "5% Si", "anode": {"Graphite": 0.95, "Silicon_nano": 0.05}}, {"designName": "10% Si", "anode": {"Graphite": 0.9, "Silicon_nano": 0.1}}, {"designName": "15% Si", "anode": {"Graphite": 0.85, "Silicon_nano": 0.15}}, {"designName": "20% Si", "anode": {"Graphite": 0.8, "Silicon_nano": 0.2}}, ]

In [30]:

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

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

We can also normalize the results based on the baseline cell for an even more digestible comparison.

In [31]:

results.get_normalized_kpis()

results.get_normalized_kpis()

Out[31]:

	Baseline	5% Si	10% Si	15% Si	20% Si
KPI
Capacity [Ah]	1.0	1.127918	1.272624	1.373875	1.448175
Nominal Voltage [V]	1.0	0.969409	0.950582	0.938571	0.930199
Energy [Wh]	1.0	1.093414	1.209734	1.289479	1.347091
Gravimetric Energy Density [Wh/kg]	1.0	1.080399	1.158076	1.207416	1.240898
Volumetric Energy Density [Wh/l]	1.0	1.093414	1.209734	1.289479	1.347091
Minimum Anode Voltage [mV]	1.0	0.724081	0.630519	0.495257	0.371651
Weight [g]	1.0	1.012046	1.044607	1.067966	1.085578
Volume [l]	1.0	1.000000	1.000000	1.000000	1.000000
Heat Capacity [kJ/K]	1.0	1.013945	1.046634	1.070031	1.087646

In [32]:

results.plot_radar()

results.plot_radar()

Dynamic simulations is currently disabled for designs with modified chemistries. Reliable prediction of these changes will become available once further experimental data on the materials is incorporated.

In [33]:

from breathe_design import Cycler

cycler_dict = Cycler("C", 1.5).cc_chg(I_chg=0.5, V_max=4.0)
designs = [{"designName": "5% Si", "anode": {"Graphite": 0.95, "Silicon_nano": 0.05}}]

from breathe_design import Cycler cycler_dict = Cycler("C", 1.5).cc_chg(I_chg=0.5, V_max=4.0) designs = [{"designName": "5% Si", "anode": {"Graphite": 0.95, "Silicon_nano": 0.05}}]

In [34]:

output = api.run_sim("Molicel P45B", cycler_dict, designs)

output = api.run_sim("Molicel P45B", cycler_dict, designs)

In [35]:

output.get_dynamic_data("5% Si")

output.get_dynamic_data("5% Si")

Out[35]:

{'5% Si': None}

In [36]:

output.plot_voltage_response()

output.plot_voltage_response()