**7. Conclusions**

EV session data collected from charging stations on a electricity gird can be used for flexibility analysis, making pricing decisions, etc., and are essential for advancement in the field of smart grids.

We defined a synthetic data generator (SDG) to generate samples of EV session data collected on charging stations. We modeled arrival times of EVs using inter-arrival time (IAT) and arrival counts (AC) methods. For generating the connection times and required energy, we used mixture models based on GMM. The generated sample of session data is statistically indistinguishable from the real-world data, as seen from the KDE test results. We can conclude that our proposed SDG is suited for generating a synthetic sample of EV session data.

This generated data sample will have the properties of a real-world EV sessions, and can be used for purposes such as flexibility analysis. We will release the trained SDG models that can be used to generate new samples of EV session data. Complete code for training and evaluating the SDG models is open source, and can be used to fit the models on a new EV session data (see Appendix A). These models can be shared without violating the privacy concerns of the real data collection companies.

For future work, further exploration is required in studying reduced variance in daily arrivals in AC models. IAT models for arrival times misses the first peak of weekdays, wherein improvements are possible. A deeper dive into the mixture models for estimating the conditional distribution of required energy can also provide an improvement to results.

**Author Contributions:** M.L. developed the methodology and performed data curation, validation, visualization, software and writing of the original draft; C.D. handled resources and funding acquisition; D.F.B. and C.D. jointly supervised the work presented, and performed review and editing of the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received funding from the Flemish Government under the "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen" programme.

**Acknowledgments:** We thank Nazir Refa from ElaadNL for the real-world EV session data.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **Appendix A. Code**

Code for training SDG models is open source, and can be accessed on GitHub: https:// github.com/mlahariya/EV-SDG. SDG models trained with a real-world dataset are also included with the code. These can be used to generate a random sample of EV session data using script SDG\_sample\_generate.py. Trained models that can be used as default models to generate samples with are located at modeling/default\_models, and include:


Users can also employ our code to fit *AM*, *MMc*, and *MMe* to their own datasets. For training a SDG model from scratch, this process will be followed: (i) Clean real-world EV session data (*preprocess*). (ii) Generate session and pole clusters (*preprocess*). (iii) Prepare data for SDG training (*preprocess*). (iv) Train *AM*, *MMc* and *MMe* models (*modeling*). (v) Save the model along with a log file in the 'res/' folder. A command line callable script SDG\_fit.py can be used to fit the models.

Please visit the repository for further details.
