# DATTES: Data Analysis Tools for Tests on Energy Storage

Eduardo Redondo-Iglesias, Marwan Hassini, Pascal Venet, Serge Pelissier

► To cite this version:

Eduardo Redondo-Iglesias, Marwan Hassini, Pascal Venet, Serge Pelissier. DATTES: Data Analysis Tools for Tests on Energy Storage. SoftwareX, 2023, 24, pp.101584. 10.1016/j.softx.2023.101584. hal-04164735v2

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Submitted on 21 Nov 2023

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Original software publication

# DATTES: Data analysis tools for tests on energy storage

Eduardo Redondo-Iglesias<sup>a,c,*</sup>, Marwan Hassini<sup>a,b,c</sup>, Pascal Venet<sup>b,c</sup>, Serge Pelissier<sup>a,c</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Energy storage

Experiments

Matlab

GNU octave

Data analysis

Open science

FAIR

## ABSTRACT

Experiments are essential to understand the behaviour and performance of energy storage systems. In this field, a considerable amount of experimental data is generated and data processing is a tedious task. To date, research teams working in the field of energy storage tend to focus on developing their own analysis tools rather than using existing open source software. This strategy can be detrimental to the quality and reproducibility of the research. This paper presents DATTES, a free and open source software for analysing experimental battery data. The software provides a comprehensive and customizable toolkit for extracting, analysing and visualizing experimental data. It also creates gateways to other open software and tools. In this way, DATTES enables users to get the most out of their experimental data and engage in open and reproducible science.

### Code metadata

Current code version

Permanent link to code/repository used for this code version

Code Ocean compute capsule

Legal Code License

Code versioning system used

Software code languages, tools, and services used

Compilation requirements, operating environments & dependencies

If available Link to developer documentation/manual

Support email for questions

DATTES 23.05

https://github.com/ElsevierSoftwareX/SOFTX-D-23-00363

GNU GPL v3

git

Matlab/GNU Octave

Matlab or GNU Octave on Linux, OSX or Windows.

https://dattes.gitlab.io/

dattes@univ-eiffel.fr

## 1. Motivation and significance

Data processing is playing an increasingly important role in scientific studies. This time-consuming activity typically consumes 30 percent or more of a scientist's research time [1]. Software such as MATLAB, Python or R is commonly used, and according to a study by Hettrick et al. 70% of scientists admit that their research would not even be possible without it [2].

In the field of energy storage, there are several data processing limitations to overcome. Firstly, a wide variety of tools and methods are used to generate experimental data. As a result, it is difficult to compare the experimental data generated. In addition, most research teams have developed their own data processing tools adapted to their specific needs and data sets. Although this organization can be explained by the researchers' need to process the data from their

projects quickly, such a development strategy limits the reproducibility of the work and tends to significantly increase the time spent by the researchers on programming. Furthermore, as the vast majority of researchers are self-taught developers, they may lack experience of good software development practices. As a result, the quality of the research may be compromised. Comparison between studies from two different laboratories is also made difficult because each team is likely to use its own methodology for generating and processing data. This lack of common data processing tools is a major barrier to sharing and comparing experimental results [3].

Open source software has recently gained increasing interest in the field as it allows for the sharing of programming efforts, best practices, and analysis methodologies. In the current landscape of open source software for energy storage systems, Python dominates.

<footnote><sup>a</sup> Univ Lyon, Univ Eiffel, ENTPE, LICIT-ECO7 Lab, 69500 Bron, France</footnote>

<footnote><sup>b</sup> Univ Lyon, Université Claude Bernard Lyon 1, INSA Lyon, Ecole Centrale de Lyon, CNRS, Ampère, UMR5005, 69622 Villeurbanne, France</footnote>

<footnote><sup>c</sup> ERC GEST (Eco7/Ampère Joint Research Team for Energy Management and Storage for Transport), 69500 Bron, France</footnote>

<footnote>* Corresponding author at: Univ Lyon, Univ Eiffel, ENTPE, LICIT-ECO7 Lab, 69500 Bron, France.
E-mail address: eduardo.redondo@univ-eiffel.fr (Eduardo Redondo-Iglesias).</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 3. Pseudo OCV (left) and ICA (right) plots. In pseudo OCV plot line colours are: red for discharge half cycle, blue for charge half cycle, black for pseudo OCV; continuous lines for C/20 cycle, dashed lines for 1C cycle. In ICA plot line colours are: red for discharge half cycle, blue for charge half cycle. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

developers hope that the energy storage community will take advantage of the software, as new users are seen as potential reviewers who can catch bugs, ask for new features, and accelerate the rise of computing standards.

Finally, DATTES is designed to be modular and easily coupled with other existing software. It can enable the energy storage community to design better batteries, build robust models, and make more accurate predictions with standardized, peer-reviewed methodologies.

## 5. Future work

DATTES already offers a comprehensive platform for processing battery data from experimentation to analysis and visualization. However, it is also continually evolving and future releases may introduce new features.

One potential enhancement is reliability analysis. By evaluating the quality of raw data and applied analysis methods, the reliability of resultant output could be assessed and reported within the DATTES data structure.

Another possible feature could be the linking of results from multiple tests on a single or batch of cells, for example monitoring performance over temperature or time. In the current iteration of DATTES, each processed test generates a DATTES data structure that contains raw data, metadata, and results specific to that particular test. By utilizing the metadata included in each DATTES data structure, results can be categorized based on date, cell unique identifier, or test temperature.

## 6. Conclusions

Processing experimental data is a central task for energy storage researchers on a daily basis. Until now, most research teams have focused on proprietary or laboratory-made tools, which severely limits the reproducibility of research. The lack of open tools available for MATLAB, the most popular language in the field, has been a significant obstacle to the widespread use of such tools. This paper introduces DATTES, the first experimental data processing software for batteries written in MATLAB code and distributed under an open licence.

DATTES contributes to existing open tools by being the first MATLAB software to support the entire data processing chain for battery experiments. This software provides a comprehensive and configurable toolkit for extracting, analysing and visualizing experimental data. The number and variety of input data formats that DATTES can handle is very large, as is the number of analysis tools it provides. DATTES is also

one of the first software packages to consider data traceability through the use of metadata. The software also provides gateways to other open software and tools.

DATTES thus enables users to make good use of their experimental data and to participate in open and reproducible science.

## Declaration of competing interest

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

## Data availability

Data to reproduce the illustrative example is shared by link in the code.

## References

[1] Hannay JE, MacLeod C, Singer J, Langtangen HP, Pfahl D, Wilson G. How do scientists develop and use scientific software? In: 2009 ICSE workshop on software engineering for computational science and engineering. IEEE; 2009, p. 1–8.

[2] Hettrick S. 2014: Software in research survey. 2018, http://dx.doi.org/10.5281/zenodo.1183562.

[3] Hassini M, Redondo-Iglesias E, Venet P. Lithium-ion battery data: From production to prediction. Batteries 2023;9:385. http://dx.doi.org/10.3390/batteries9070385.

[4] Herring P, Gopal CB, Aykol M, Montoya JH, Anapolsky A, Attia PM, et al. Beep: A python library for battery evaluation and early prediction. SoftwareX 2020;11:100506. http://dx.doi.org/10.1016/j.softx.2020.100506.

[5] Sulzer V, Marquis SG, Timms R, Robinson M, Chapman SJ. Python battery mathematical modelling (pybamm). J Open Res Softw 2021;9(1). http://dx.doi.org/10.5334/JORS.309.

[6] Murbach MD, Gerwe B, Dawson-Elli N, Tsui L-k. Impedance. py: A python package for electrochemical impedance analysis. J Open Source Softw 2020;5(52):2349. http://dx.doi.org/10.21105/joss.02349.

[7] DATTES: Data analysis tools for tests on energy storage. URL https://gitlab.com/dattes/dattes/.

[8] VEHLIB. URL https://gitlab.univ-eiffel.fr/eco7/vehlib.

[9] Hassini M, Redondo-Iglesias E, Venet P. Second-life batteries modeling for performance tracking in a mobile charging station. World Electr Veh J 2023;14(4):94. http://dx.doi.org/10.3390/wevj14040094.

[10] Jiménez RC, Kuzak M, Alhamdoosh M, Barker M, Batut B, Borg M, et al. Four simple recommendations to encourage best practices in research software. F1000Research 2017;6. http://dx.doi.org/10.12688/f1000research.11407.1.