Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder

Abstract:The increasing adoption of electric vehicles (EVs) is expected to place significant additional demand on residential distribution networks, creating a need for realistic charging datasets for planning and simulation studies. However, access to real-world EV charging data is often limited due to privacy constraints, incomplete records, and restricted availability. This paper proposes a conditional variational autoencoder (CVAE) for the generation of synthetic EV charging sessions from real transaction-level charging data. The model is trained on engineered session features describing plug-in duration, charging duration, delivered energy, charging delay, and cyclical time-of-week, while conditioning on day of week and managed charging status. A Gaussian negative log-likelihood (NLL) reconstruction loss is employed to model feature-wise heteroscedastic uncertainty, and the latent space is regularised using a Kullback-Leibler (KL) divergence term. The statistical fidelity of the generated data is evaluated using distributional metrics and downstream task performance through the Train-on-Synthetic-Test-on-Real (TSTR) protocol. Results demonstrate that the proposed approach produces synthetic EV charging sessions that preserve key statistical properties of the original dataset while supporting predictive modelling tasks.

5 pages, 4 figures, 4 tables. Accepted at IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) 2026

Systems and Control (eess.SY); Machine Learning (cs.LG)

arXiv:2609.17808 [eess.SY]

(or arXiv:2609.17808v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2609.17808 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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