Electric vehicles charging infrastructure planning: a review
Bibliographic record
Abstract
Electric vehicles (EVs) have gained increasing attention as a more sustainable and environmentally friendly mode of transportation. Many countries should include electrification of their transport networks in their future smart city plans to ensure environmentally sustainable growth. The number of EVs in metropolitan areas is expected to experience extensive growth. EVs are often considered an alternative to addressing the challenges posed by increasing carbon emissions and dependence on fossil fuels. However, the acceptance of EVs tends to be slow due to concerns such as range anxiety, prolonged charge times, inconvenient charging locations, and inadequate charging infrastructure. To address these challenges, this paper discusses the planning and optimization of EV charging infrastructure based on existing literature. In recent years, there has been a significant increase in the number of publications focusing on EV charging stations. This demonstrates the growing interest and research activity in the field of EV charging infrastructure. Furthermore, the literature is categorized into recurring topics, specifically EV charging planning and optimization for EV charging infrastructure. This particular review paper includes empirical work on charging network planning for EVs. Therefore, this analysis provides the latest trends and findings for EV charging infrastructure planning.
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.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".