# Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder

arXiv: https://arxiv.org/abs/2609.17808 · HTML: https://mlsanity.com/paper/2609.17808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations](https://mlsanity.com/paper/2210.01167/) (2022)
- [The Creation and Validation of Load Time Series for Synthetic Electric Power Systems](https://mlsanity.com/paper/1911.06934/) (2019)
- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Faraday: Synthetic Smart Meter Generator for the smart grid](https://mlsanity.com/paper/2404.04314/) (2024)

## Abstract (arXiv)

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.