A systematic literature review of optimal placement of fast charging station
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric vehicles (EV) have increased in the last few decades due to their ability to reduce greenhouse gas emissions (GHG). Support for the electrification of the transportation sector has encouraged researchers to investigate the optimal placement of fast charging stations (FCS). In this study, we conducted a systematic literature review of 84 primary studies between 2019 and 2024 by identifying objective function and solution techniques, uncertainty, stakeholders, and network classification. We identified the objective functions most commonly used by authors related to technical and cost-solving problems using techniques: conventional (41.7%), metaheuristic (33.3%), hybrid (22.6%), and other (2.4%). Several researchers have also considered various uncertainty parameters from EV, FCS demand, and distributed generation (DG) power output with the most popular probabilistic method to solve problems. Furthermore, the role of stakeholders and network classification is also reviewed in this article. Our study contributes to the field by providing a comprehensive overview of the most significant journals and highlighting future research on the optimal placement of FCS. Future work must focus on improving parameters, models, methods, and using real data from various factors related to FCS demand. • A systematic literature review of problem formulation and optimization methods of FCS. • Overview of uncertainty parameters and modeling techniques of FCS demand. • Reviewing stakeholders and network classification of FCS. • Identifying journals related to FCS placement using PRISMA and Bibliometrix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it