ampworks.ici#

Tools to analyze Incremental Current Interruption (ICI) data. Includes functions to extract diffusion coefficients and equilibrium potentials from experimental measurements.

Functions#

extract_params(data, radius[, tmin, tmax, return_stats])

Extracts parameters from ICI data.

Package Contents#

ampworks.ici.extract_params(data, radius, tmin=1, tmax=10, return_stats=False)[source]#

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

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)