Analysing GITT data#
PyProBE includes built-in analysis methods for pulsing experiments, which this example will demonstrate.
First import the required libraries and data:
[1]:
import pyprobe
info_dictionary = {'Name': 'Sample cell',
'Chemistry': 'NMC622',
'Nominal Capacity [Ah]': 0.04,
'Cycler number': 1,
'Channel number': 1,}
data_directory = '../../../tests/sample_data/neware'
# Create a cell object
cell = pyprobe.Cell(info=info_dictionary)
cell.add_procedure(procedure_name='Sample',
folder_path = data_directory,
filename = 'sample_data_neware.parquet')
print(cell.procedure['Sample'].experiment_names)
['Initial Charge', 'Break-in Cycles', 'Discharge Pulses']
We will plot the Break-in Cycles and Discharge Pulses:
[2]:
fig = pyprobe.Plot()
fig.add_line(cell.procedure['Sample'].experiment('Break-in Cycles'), 'Time [hr]', 'Voltage [V]', label = 'Break-in Cycles', color = 'blue')
fig.add_line(cell.procedure['Sample'].experiment('Discharge Pulses'), 'Time [hr]', 'Voltage [V]', label = 'Discharge Pulses', color = 'red')
fig.show_image()
State-of-charge is a useful metric when working with battery data, however it must be carefully defined. PyProBE doesn’t automatically calculate a value for cell SOC until instructed to by the user for this reason.
To add an SOC column to the data, we call set_SOC() on the procedure. We are going to provide an argument to reference_charge. This will be the final charge of the break-in cycles. This argument instructs PyProBE that the final data point of this charge is our 100% SOC reference.
[3]:
reference_charge = cell.procedure['Sample'].experiment('Break-in Cycles').charge(-1)
cell.procedure['Sample'].set_SOC(reference_charge=reference_charge)
fig = pyprobe.Plot()
fig.add_line(cell.procedure['Sample'].experiment('Break-in Cycles'), 'Time [hr]', 'SOC', label = 'Break-in Cycles', color = 'blue')
fig.add_line(cell.procedure['Sample'].experiment('Discharge Pulses'), 'Time [hr]', 'SOC', label = 'Discharge Pulses', color = 'red')
fig.show_image()
Then we’ll filter to only the pulsing experiment:
[4]:
pulsing_experiment = cell.procedure['Sample'].experiment('Discharge Pulses')
fig = pyprobe.Plot()
fig.add_line(pulsing_experiment, 'Experiment Time [hr]', 'Voltage [V]')
fig.show_image()
And then create our pulsing analysis object.
[5]:
from pyprobe.analysis import pulsing
pulse_object = pulsing.Pulsing(input_data=pulsing_experiment)
With the pulsing object we can separate out individual pulses:
[6]:
fig = pyprobe.Plot()
fig.add_line(pulse_object.input_data, 'Experiment Time [hr]', 'Voltage [V]', color='blue', label='Full Experiment')
fig.add_line(pulse_object.pulse(4), "Experiment Time [hr]", "Voltage [V]", label = 'Pulse 5', color = 'red')
fig.show_image()
We can also extract key parameters from the pulsing experiment, with the get_resistances() method.
[7]:
pulse_resistances = pulsing.get_resistances(input_data=pulsing_experiment)
print(pulse_resistances.data)
shape: (10, 5)
┌──────────────┬──────────────────────────┬──────────┬─────────┬───────────┐
│ Pulse Number ┆ Experiment Capacity [Ah] ┆ SOC ┆ OCV [V] ┆ R0 [Ohms] │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════╪══════════════════════════╪══════════╪═════════╪═══════════╡
│ 1 ┆ 0.0 ┆ 1.0 ┆ 4.1919 ┆ 1.805578 │
│ 2 ┆ -0.004 ┆ 0.903497 ┆ 4.0949 ┆ 1.835632 │
│ 3 ┆ -0.008001 ┆ 0.806994 ┆ 3.9934 ┆ 1.775612 │
│ 4 ┆ -0.012001 ┆ 0.710493 ┆ 3.8987 ┆ 1.750596 │
│ 5 ┆ -0.016001 ┆ 0.613991 ┆ 3.8022 ┆ 1.725532 │
│ 6 ┆ -0.020002 ┆ 0.517489 ┆ 3.7114 ┆ 1.705558 │
│ 7 ┆ -0.024002 ┆ 0.420988 ┆ 3.665 ┆ 1.705622 │
│ 8 ┆ -0.028002 ┆ 0.324487 ┆ 3.6334 ┆ 1.735555 │
│ 9 ┆ -0.032002 ┆ 0.227986 ┆ 3.5866 ┆ 1.795638 │
│ 10 ┆ -0.036003 ┆ 0.131485 ┆ 3.5164 ┆ 1.900663 │
└──────────────┴──────────────────────────┴──────────┴─────────┴───────────┘
The get_resistances() method can take an argument of a list of times at which to evaluate the resistance after the pulse, for instance at 10s after the pulse:
[8]:
pulse_resistances = pulsing.get_resistances(input_data=pulsing_experiment, r_times=[10])
print(pulse_resistances.data)
shape: (10, 6)
┌──────────────┬──────────────────────────┬──────────┬─────────┬───────────┬──────────────┐
│ Pulse Number ┆ Experiment Capacity [Ah] ┆ SOC ┆ OCV [V] ┆ R0 [Ohms] ┆ R_10s [Ohms] │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════╪══════════════════════════╪══════════╪═════════╪═══════════╪══════════════╡
│ 1 ┆ 0.0 ┆ 1.0 ┆ 4.1919 ┆ 1.805578 ┆ 2.910931 │
│ 2 ┆ -0.004 ┆ 0.903497 ┆ 4.0949 ┆ 1.835632 ┆ 2.805967 │
│ 3 ┆ -0.008001 ┆ 0.806994 ┆ 3.9934 ┆ 1.775612 ┆ 2.735943 │
│ 4 ┆ -0.012001 ┆ 0.710493 ┆ 3.8987 ┆ 1.750596 ┆ 2.685915 │
│ 5 ┆ -0.016001 ┆ 0.613991 ┆ 3.8022 ┆ 1.725532 ┆ 2.640815 │
│ 6 ┆ -0.020002 ┆ 0.517489 ┆ 3.7114 ┆ 1.705558 ┆ 2.400785 │
│ 7 ┆ -0.024002 ┆ 0.420988 ┆ 3.665 ┆ 1.705622 ┆ 2.345855 │
│ 8 ┆ -0.028002 ┆ 0.324487 ┆ 3.6334 ┆ 1.735555 ┆ 2.390765 │
│ 9 ┆ -0.032002 ┆ 0.227986 ┆ 3.5866 ┆ 1.795638 ┆ 2.565912 │
│ 10 ┆ -0.036003 ┆ 0.131485 ┆ 3.5164 ┆ 1.900663 ┆ 3.026056 │
└──────────────┴──────────────────────────┴──────────┴─────────┴───────────┴──────────────┘
As a result object, the pulse summary can also be plotted:
[9]:
fig = pyprobe.Plot()
fig.add_line(pulse_resistances, 'SOC', 'R0 [Ohms]', color='blue', label='R0')
fig.add_line(pulse_resistances, 'SOC', 'R_10s [Ohms]', color='red', label='R_10s')
fig.yaxis_title = 'Resistance [Ohms]'
fig.show_image()
