Analytical Size Estimation Methodologies for Electrified Transportation Fueling Infrastructures Using Public–Domain Market Data
Bibliographic record
Abstract
The proliferation of electrified transportation systems is envisioned as a promising solution that could significantly contribute to the reduction of environmental pollution. Deployment of properly sized fueling stations is contemplated as a critical step to set the stage for the transportation electrification. While several optimization-based sizing models are presented in the literature, analytical models to estimate the size of electrified transportation infrastructures are missing from prior studies. Such analytical models can readily be developed at the backend of globally accessible websites or software applications since they do not require any optimization solver. In such a case, public-domain data, such as market prices or transportation demand, from everywhere around the world can be inputted to the model, and various sizing parameters related to each location are returned to the user. To that end, this paper presents new analytical methodologies for the application to the size estimation of electric and hydrogen-based fueling stations as the two major foundations for electrified transportation. The ratings of various components are expressed in terms of the system operation percentage using the proposed formulation, and the desired ratings are selected at which the net profit reaches to the maximum point. Historical public-domain data from real-world systems are utilized for numerical evaluation of the proposed formulation. In addition, the estimation error of the proposed model is analyzed and compared with artificial intelligence-based models for validation purposes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".