A model for public fast charging infrastructure needs: Presentation at EVS29 - 29th International Electric Vehicle Symposium, Montréal, Québec, Canada, 19-22, 2016
Bibliographic record
Abstract
Plug-in electric vehicles can reduce GHG emissions although the low availability of public charging infra-structure combined with short driving ranges prevents potential users from adoption. The rollout and operation, especially of public fast charging infrastructure, is very costly. Therefore, policy makers, car manufacturers and charging infrastructure providers are interested in determining a number of charging stations that is sufficient. Since most studies focus on the placement and not on the determination of the number of charging stations, this paper proposes a model for the quantification of public fast charging points. We first analyze a large database of German driving profiles to obtain the viable share of plug-in electric vehicles in 2030 and determine the corresponding demand for fast charging events. Special focus lies on a general formalism of a queuing system for charging points. This approach allows us to quantify the capacity provided per charging point and the required quantity. Furthermore, we take a closer look on the stochastic occupancy rate of charging points for a certain service level and the distribution of the time users have to wait in the queue. When applying this model to Germany, we find about 15,000 fast charging points with 50 kW necessary in 2030 or ten fast charging point per 1,000 BEVs. When compared with existing charging data from Sweden, this is lower than the currently existing 36 fast charging points per 1,000 BEVs. Furthermore, we compare the models output of charging event distribution over the day with that of the real data and find a qualitatively similar load of the charging network, though with a small shift towards laterin the day for the model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".