A python module to analyze fast impedance tortuosity measurements.
FIPT: Fast impedance tortuosity
FIPT is a measurement technique that allows a fast determination of the ionic resistance for battery electrodes. The technique is based on the work by J. Landesfeind (DOI: 10.1149/issn.1945-7111) and then optimized for execution speed at Battrion.
The measurement setup can be built based on https://github.com/deniz195/fipt
The data aquired in an FIPT measurement can be analyzed by the code in this repository. The code fits an analytical model to the data to determine the relevant parameters of the measurement:
r_ion - Ionic resistance r_sep - Seperator resistance chisqr - χ2 as quality of fit gamma - Phase exponent q_s - Capacitance factor
The ionic resistance can be used to calculate the MacMullin number (and the tortuosity), which are important performance parameters of battery electrodes.
If you want to quickly analyze your impedance data:
pip install fipt
Put the test data
test_data_001.csv from the
examples folder in your current folder. Analyze the data:
python -m fipt ./my_fipt_data_1234.csv
To analyze your own data, put it in a file with the same format as
test_data_001.csv, which is:
Format: CSV 1st column, Frequency in [Hz] 2nd column, Z' in [Ohm] 3rd column, Z'' in [Ohm] (Name of columns is not important)
To see how to analyze fipt data in your own python code, refer to the jupyter notebook
demo_fipt.ipyn in the examples folder.
Features and known issues
The code was optimized to allow the fitting and analysis of large numbers of data files, with minimum user intervention. The key features of the code are:
- Robust estimation of starting parameters from raw data
- Resistance to outlier data points through use of Student-T likelihood function
- Ability to verify each fit, through automatic generation of result files (plots, statistics, etc.)
- The code is currently provided with a minimum of documentation.
- Calculation of MacMullin number and tortuosity not yet included
Required packages are
scipy for data and statistical models and
lmfit to perform the model fitting.
It is recommended (but not necessary) to install matplotlib installed, so that fipt-analysis will be able to create plots of the fitting.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
For questions please feel free to reach out to Deniz Bozyigit (email@example.com)
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