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From bigger batteries to new charging realities

  • Weipeng Zhan
  • , Yuan Liao
  • , Sonia Yeh*
  • , Junjun Deng*
  • , Zhenpo Wang
  • , Dingsong Cui
  • *Corresponding author for this work
  • Beijing Institute of Technology
  • Chalmers University of Technology
  • Technical University of Denmark
  • Lund University
  • University of Leeds

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid adoption of electric vehicles (EVs) with increasingly advanced battery technologies is reshaping electricity demand patterns. Yet existing studies often assume static behaviors, overlooking that real-world charging patterns are transient and evolve in response to technological change. This study applies a scenario-aware generative modeling framework to project weekly EV charging demand in Beijing for 2030, capturing behavioral shifts driven by evolving battery technologies, usage patterns, and infrastructure conditions. Results indicate that total charging load could increase by 457–509% compared to a 2021 baseline under two different scenarios of battery size growth. Though medium-power (4–20 kW) charging remains dominant in event frequency, high-power ( >  20 kW) charging contributes substantially to loads, implying the growing risk of stress on the power grid in the absence of coordinated scheduling. The proposed framework provides a scalable, data-driven method to simulate EV usage and load patterns and offers valuable insights for transportation and energy planners confronting the rapid electrification of private mobility.

Original languageEnglish
Article number105408
JournalTransportation Research Part D: Transport and Environment
Volume157
DOIs
Publication statusPublished - Aug 2026
Externally publishedYes

Keywords

  • Battery capacity
  • Charging demand
  • Electric vehicles
  • Gaussian mixture regression
  • Transformer

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