The growth exploiting renewable energies is only possible thanks to the development of adequate energy storage systems . Li-ion batteries have become one of the fastest growing electric energy storage systems in the automotive market . Cycling these batteries through charging and discharging is one of the cornerstone experiments to understand their working performance and ageing behaviour, which are essential to help improving their design  and the lifespan prediction . This cycling can be done in different types of testers, using a range of batteries not limited to the Li-ion ones. Novonix is a relatively new company in the market of battery-testing systems, catering to high-precision coulometry . An accurate coulombic efficiency tracking can provide insights for battery ageing mechanism and lifetime prediction at early experimental stages. The preparenovonix package prepares the raw data exported from Novonix battery-testers so it can be later analysed with ease. Traditionally, this type of code is not widely shared among different groups working on battery research. However, opening this code to the community has the potential to benefit all users of the Novonix battery-testers and to promote further collaboration developing code relevant for the battery research field.
The preparenovonix package prepares exported data files produced by Novonix battery-testers4 by (i) cleaning them and (ii) adding derived information to the file. The package also allows reading an individual column given its name. The derived information includes:
Combining the state and step number values makes it possible to select the capacities from a charge or discharge experimental step. These are needed for estimating the coulombic efficiency. This combination of state with step number also allows the computation of resistances based on current experimental steps or pulses. Specific cycles or individual sections of the experiment can be selected combining the loop number with either the state and step number values or the protocol line values.
The example data provided within the repository for this code is shown in Figure 1. This figure compares the raw Novonix data with the data after being processed by the preparenovonix package. The example raw data contains individual measurements for which the experimental run time decreases. As it can be seen in Figure 1, these measurements are removed by the preparenovonix package. The example raw data file also includes a failed test. The preparenovonix package takes the capacity from the failed test and adds it to the capacities from the completed experiment. This shifts the result capacity curve by a constant value, as it can be seen in Figure 1. This figure also shows the increasing loop number when the measurements are within a repeat loop and the protocol line each measurement corresponds to.
The main functions available in the preparenovonix package5 are listed below in alphabetical order. The list contains the module name followed by the function name with the expected input parameters in brackets.
In what follows, the above functions will be referred by simply their name, without stating the modules they belong to.
As it is shown in Figure 2, the preparenovonix package only cleans data files that are consider to be exported from the Novonix battery-testers and it only derives information for cleaned Novonix files. The master function prepare_novonix allows the user to call either the cleaning process or the addition of extra columns ensuring that these dependencies are taken into account. The input parameters for this function are the path to a file and four boolean optional parameters: addstate, lprotocol, overwrite and verbose. The last parameter provides the option to output more information about the run. If the overwrite parameter is set to False, a new file will be generated with a name similar to the input one, except for the addition of _prep before the extension of the file.
The function isnovonix decides if a file has the expected structure (including a full header) for an exported file produced by the Novonix battery-testers. If the file is lacking the header or if it has not been exported with a Novonix battery-tester using the covered software,6 the code will exit with an error message and without generating a new file.
The function cleannovonix produces a new Novonix type file after performing the following tasks:
A State column can be added to a cleaned Novonix file by calling the function novonix_add_state or setting to True the parameter addstate when calling the function prepare_novonix. This State column can have the following values:
0 for the first measurement of a given type (for example, a constant current charge).
1 for measurements between the first and last of a given type.
2 for the last measurement of a given type.
–1 for single measurements. This can happen under different circumstances. A type of measurement can end after a single measurement when some experimental conditions are met, this usually happens while the time resolution is coarse. At times, the current can overshoot from negative to positive values at the beginning of a measurement. A bug in the Novonix software that locks certain values, etc. If two single measurements happen together, the two lines are discarded in the new file containing the additional State column.
The State column is generated based on the following quantities provided in the raw Novonix data files: Step number (integer indicating the type of measurement) and Step time (this time is assumed to reset to 0 each time a new type of measurement starts).
The function create_reduced_protocol reads the complete header from the input file and generates (or reads) the reduced protocol. This function returns the reduce protocol itself and a boolean flag, viable_prot. The reduced protocol consist of an array of strings. Each string contains a line number, a command from the experimental protocol and the corresponding experimental conditions (if aplicable); for example: [4 : Repeat 49 times :]. Only commands referring to the following processes will appear in the reduced protocol:7
The reduced protocol is tested against the number of unique measurements in the file, determined using the column State. If the number of measurements expected from the protocol is less than the actual number of measurements, the flag viable_prot is set to False, indicating that the construction of the reduced protocol was not viable.
The Protocol line and Loop number columns can be generated by either calling directly the function novonix_add_loopnr or by setting to True the parameter lprotocol when calling the function prepare_novonix. The column Protocol line associates a measurment with its corresponding line in the reduced protocol. The Loop number column has a value of 0 if a measurement does not correspond to any repetition statement in the protocol and otherwise it grows monotonically with each repetition (see Figure 1).
If the flag viable_prot was set to False by the reduced_protocol function, the Protocol line and Loop number columns are populated with the value –999.
Each function in the preparenovonix package is tested with internal checks and with pytest both locally and through the Travis Continuous Integration service.8 The tests have been performed in different platforms and using different Python versions. The tests use an example data file. This file is automatically retrieved when the dedicated GitHub repository is either cloned or downloaded (see the ‘Software location’ section for the relevant urls).
Each function is documented with an example of usage. The expected result when used on the example data is also provided. Moreover, an example script, example.py, is provided at the root directory of the dedicated GitHub repository. This script also produces Figure 1.
The complete documentation for the preparenovonix package can be found at: https://prepare-novonix-data.readthedocs.io/.
Windows, OSX, Linux
Python 3.5 and above.
The code presented here uses as input the data files exported directly from the Novonix battery-testers. The on-line documentation described in the ‘Quality control’ section, provides an updated list of the Novonix software versions that the code presented here has been tested against.
This software requires the numpy Python library. Matplotlib is also required for using the plotting routine compare.plot_vct.py. Further details on how to install these libraries or how to install the software using ‘pip’ can be found in the ‘Readthedocs’ documentation mentioned in the ‘Quality control’ section.
The list of contributors comprises the author list and the contributors reported in the dedicated GitHub repository (see the url below).
Persistent identifier: http://doi.org/10.5281/zenodo.3081471
Licence: MIT License
Publisher: Andrew Dawson
Version published: 0.0.1
Date published: 21/05/2019
Persistent identifier: https://github.com/BatLabLancaster/preparenovonix
Licence: MIT License
Date published: 16/05/19
All documentation is provided in English. For a translation into an other language, contact the corresponding author.
The software presented here can clean, enhance and facilitate the use of data produced by Novonix battery-testers. The potential for reusing this software is large among users of these testers, both in academic research and industry. Two aspects that are particularly fundamental are the cleaning of raw files, as described above, and the possibility to read a specific column for a range of formats from different software versions from Novonix. The software presented here can be modified and enhanced by contributing to the dedicated GitHub repository. Support can be provided by raising issues in the same repository.
5The full list of functions, including those auxiliary of the ones presented here, can be found in https://prepare-novonix-data.readthedocs.io/.
6See an up-to-date list in https://github.com/BatLabLancaster/preparenovonix.
The authors have no competing interests to declare.
Burns, J C, Stevens, D E, Dahn, J R 2015 In-Situ Detection of Lithium Plating Using High Precision Coulometry. J. Electrochem. Soc., 162(6): A959–A964. DOI: https://doi.org/10.1149/2.0621506jes
Csala, D, Hoster, H E 2017 Emissions: Step on the natural gas for German cars. Nature, 541: 157. DOI: https://doi.org/10.1038/541157b
Li, Y, Zou, C, Berecibar, M, Nanini-Maury, E, Chan, J C W, van den Bossche, P, Van Mierlo, J, Omar, N 2018 Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy, 232: 197–210. DOI: https://doi.org/10.1016/j.apenergy.2018.09.182
Yang, Z 2011 Electrochemical Energy Storage for Green Grid. Chemical reviews, 111(5): 3577–3613. DOI: https://doi.org/10.1021/cr100290v
Wu, B 2015 Differential thermal voltammetry for tracking of degradation in lithium-ion batteries. Journal of power sources, 273: 495–501. DOI: https://doi.org/10.1016/j.jpowsour.2014.09.127