Future Urban Charging Solutions for Electric Vehicles
Bibliographic record
Abstract
The numbers of electric vehicles (EV) will increase as many countries perceive EVs as a solution to reduce the emissions of transportation and therefore incentivize their adoption. However, the deployment of public charging infrastructure is lagging behind that of EVs, which represents a potential barrier to their wide-scale adoption. The objective of this paper is to develop a comprehensive overview of potential EV charging solutions to be deployed in urban areas. Using a micro-Delphi approach, experts from transport, energy and urban planning were consulted and identified 15 realistic options for charging electric vehicles in urban environments by 2035. The solutions range from purely technical to more service oriented. Most of these solutions already exist today, although some remain at a very early stage of deployment. The five most likely options were on-street public charging points, charging at work, fast-charging stations, using building domestic plugs and semi-fast charging in public areas. When combined with the typical mobility and residential profiles, our results show that EV drivers will most likely rely on a mix of solutions, when they have no home chargers. As such, no breakthrough or major shift is expected in charging infrastructures, rather a scale-up of existing solutions. Our analysis concludes that urban charging options will be numerous and no single solution is expected to dominate as users with different EV user profiles will charge at different times and locations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".