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 language | English |
|---|---|
| Article number | 105408 |
| Journal | Transportation Research Part D: Transport and Environment |
| Volume | 157 |
| DOIs | |
| Publication status | Published - Aug 2026 |
| Externally published | Yes |
Keywords
- Battery capacity
- Charging demand
- Electric vehicles
- Gaussian mixture regression
- Transformer
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