Synthesis of electric vehicle charging data: A real-world data-driven approach

Nowadays, electric vehicles (EVs) are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data. The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes, particularly in the deployment of charging infrastructure and the formulation of EV-focused policies. Nevertheless, the challenges of collecting these data are significant, primarily due to privacy concerns and the high costs associated with data access. In response, this study introduces an innovative methodology for generating large-scale and diverse EV charging data, mirroring real-world patterns for cost-efficient and privacy-compliant use. Specifically, this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs (BEVs) in Shanghai over a year. Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data, enabling the generation of synthetic samples that closely resemble real-world charging events. The approach is readily employed for data imputation and augmentation, and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.