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

Zhi Li, Zilin Bian, Zhibin Chen, Kaan Ozbay, Minghui Zhong

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number100128
JournalCommunications in Transportation Research
StatePublished - Dec 2024


  • Charging data
  • Conditional density network
  • Data augmentation
  • Data generation
  • Electric vehicles
  • Gibbs sampling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Transportation
  • Decision Sciences (miscellaneous)


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