Source code for ampworks.ici._extract_params
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
from scipy.stats import linregress
if TYPE_CHECKING: # pragma: no cover
from ampworks import Dataset
[docs]
def extract_params(data: Dataset, radius: float, tmin: float = 1,
tmax: float = 10, return_stats: bool = False) -> Dataset:
"""
Extracts parameters from ICI data.
ICI, or incrememtal current interruption, is an experiment that interrupts
low-rate charge or discharge experiments with short rests. The experiments
can be used to extract important parameters for physics-based models. For
example, a pseudo open-circuit voltage and solid-phase diffusivity.
The following protocol was used to test this algorithm:
1. Rest for 5 min, log data every 10 s.
2. Charge at C/10 for 5 min; with a voltage limit. Log every 5 s or 5 mV.
3. Rest for 10 seconds, log data every 0.1 s.
4. Stop if voltage limit reached in (2), otherwise repeat (2) and (3).
The protocol assumes formation cycles have already been completed and that
the cell was rested until equilibrium before starting the steps above.
Implementation details are available in [1]_. This specific protocol assumes
the cell starts at a fully discharged state and only includes charge pulses;
however, you can similarly perform the experiment in the discharge direction
or in both directions. The only change would be to step (2) where you would
discharge at C/20 instead of charge. It is common to perform the ICI tests
in both directions, but you must process the charge and discharge segments
separately by slicing your data and calling this routine twice.
Parameters
----------
data : Dataset
The sliced ICI data to process. Must have, at a minimum, columns for
`{'Seconds', 'Amps', 'Volts'}`. See notes for more information.
radius : float
The representative particle radius of your active material (in meters).
It's common to use D50 / 2, i.e., the median radius of a distribution.
tmin : float, optional
The minimum relative rest time (in seconds) to use when fitting sqrt(t)
vs. voltage for time constants. Default is 1.
tmax : float, optional
The maximum relative rest time (in seconds) to use when fitting sqrt(t)
vs. voltage for time constants. Default is 10.
return_stats : bool, optional
If False (default), only the extracted parameters vs. state of charge
are returned. If True, also returns stats with info about each rest.
Returns
-------
params : Dataset
Table of parameters. Columns include 'SOC' (state of charge, -), 'Ds'
(diffusivity, m2/s), and 'Eeq' (equilibrium potential, V).
stats : Dataset
Only returned if `return_stats=True`. Provides additional stats about
each rest, including errors from the sqrt(t) vs. voltage regressions.
Raises
------
ValueError
'data' is missing columns, required=['Seconds', 'Amps', 'Volts'].
ValueError
'data' should not include both charge and discharge segments.
Notes
-----
Rests within the dataset are expected to have a current exactly equal to
zero. You can use `data.zero_below('Amps', threshold)` to manually zero
out currents below some tolerance, if needed. This should be done prior to
passing in the dataset to this function.
This algorithm expects charge/discharge currents to be positive/negative,
respectfully. If your sign convention is the opposite, the mapping to 'SOC'
in the output will be reversed. You must process data in one direction at
a time. In other words, if you performed the ICI protocol in both charge
and discharge directions you should slice your data into two datasets and
call this routine twice.
The default `tmin` and `tmax` values assume that rests occur for at
least 10 s. You should adjust these accordingly if you use a protocol with
shorter rests. Also, if a rest has fewer than two data points between the
set relative `tmin` and `tmax` then the linear regression performed to
find the diffusivity and equilibrium potential will return `NaN` for both.
References
----------
.. [1] Z. Geng, Y. Chien, M. J. Lacey, T. Thiringer, and D. Brandell,
"Validity of solid-state Li+ diffusion coefficient estimation by
electrochemical approaches for lithium-ion batteries," EA, 2022,
DOI: 10.1016/j.electacta.2021.139727
Examples
--------
>>> import ampworks as amp
>>> data = amp.datasets.load_datasets('ici/ici_discharge')
>>> params, stats = amp.ici.extract_params(data, 1.8e-6, return_stats=True)
>>> params.plot('SOC', 'Eeq')
>>> params.plot('SOC', 'Ds', logy=True)
>>> print(params)
>>> print(stats)
"""
import ampworks as amp
from ampworks._checks import _check_columns, _check_only_one
from ampworks._auxiliary import _infer_state, _calc_soc, _calc_relative_time
_check_columns(data, {'Seconds', 'Amps', 'Volts'})
charging = any(data['Amps'] > 0.)
discharging = any(data['Amps'] < 0.)
_check_only_one(
conditions=[charging, discharging],
message="'data' cannot include both charge and discharge segments.",
)
ds = data.copy()
ds = ds.reset_index(drop=True)
# States based on current direction: charge, discharge, or rests
_infer_state(ds)
# Add in state-of-charge column to map each value to an SOC
_calc_soc(ds, charging)
# Count each time a rest/charge or rest/discharge changeover occurs
rest = (ds['State'] != 'R') & (ds['State'].shift(fill_value='R') == 'R')
ds['Rest'] = rest.cumsum()
# Relative time of each rest/charge or rest/discharge step
_calc_relative_time(ds, ['Rest', 'State'], col_name='StepTime')
# Remove last cycle if not complete, i.e., ended on charge or discharge
if ds.iloc[-1]['State'] != 'R':
ds = ds[ds['Rest'] != ds['Rest'].max()].reset_index(drop=True)
# Record summary stats for each loop, immediately before the rests
groups = ds[ds['State'] != 'R'].groupby('Rest', as_index=False)
summary = groups.agg(lambda x: x.iloc[-1])
# Store slope and intercepts (V = m*t^0.5 + b) for each rest
groups = ds.groupby('Rest')
regression = None
for idx, g in groups:
if idx > 0:
rest = g[g['State'] == 'R']
pulse = g[g['State'] != 'R']
dt_rest = rest['StepTime'].max() - rest['StepTime'].min()
dt_pulse = pulse['StepTime'].max() - pulse['StepTime'].min()
rest = rest[
(rest['StepTime'] >= tmin) &
(rest['StepTime'] <= tmax)
]
x = np.sqrt(rest['StepTime'])
y = rest['Volts']
if len(x) <= 1:
x, y = [0, 1], [np.nan, np.nan]
result = linregress(x, y)
new_row = pd.DataFrame({
'Rest': [idx],
'Eeq': [result.intercept],
'Eeq_err': [result.intercept_stderr],
'dUdrt': [result.slope],
'dUdrt_err': [result.stderr],
'dt_rest': [dt_rest],
'dt_pulse': [dt_pulse],
})
regression = pd.concat([regression, new_row], ignore_index=True)
stats = pd.merge(summary, regression, on='Rest')
stats['dEdt'] = np.gradient(stats['Volts'], stats['Seconds'])
params = amp.Dataset({
'SOC': stats['SOC'],
'Ds': 4./9./np.pi * (radius * stats['dEdt']/stats['dUdrt'])**2,
'Eeq': stats['Eeq'],
})
params.sort_values(by='SOC', inplace=True, ignore_index=True)
if return_stats:
return params, amp.Dataset(stats)
return params