Zhan, W. orcid.org/0000-0001-7137-4489, Liao, Y. orcid.org/0000-0002-6982-1654, Yeh, S. orcid.org/0000-0002-4852-1177 et al. (3 more authors) (2026) From bigger batteries to new charging realities. Transportation Research Part D: Transport and Environment, 157. 105408. ISSN: 1361-9209
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.
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2026 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Electric vehicles, Charging demand, Battery capacity, Transformer, Gaussian mixture regression |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
| Date Deposited: | 18 May 2026 11:26 |
| Last Modified: | 18 May 2026 11:26 |
| Status: | Published |
| Publisher: | Elsevier |
| Identification Number: | 10.1016/j.trd.2026.105408 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:241166 |
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Filename: 1-s2.0-S1361920926002014-main.pdf
Licence: CC-BY 4.0

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