Planning of Electric Vehicle Charging Infrastructure: A Review and a Conceptual Framework Based on Spatial and Predictive Analysis
Bibliographic record
Abstract
The widespread adoption of Electric Vehicles (EVs) has intensified the need for efficient and scalable Electric Vehicle Charging Infrastructure (EVCI).A critical aspect of this development is the optimal siting of charging stations, which involves complex multi-criteria decisionmaking based on spatial, economic, technical, and behavioral factors.This paper presents a comprehensive Systematic Literature Review on location analysis for EVCI planning, synthesizing findings from 91 peer-reviewed studies published between 2011 and 2024.We categorize and evaluate existing methodologies ranging from mathematical optimization models to Geographical Information System (GIS)-based and machine learning techniques and develop a comparative framework highlighting their strengths, limitations, and applicable contexts.In addition, we propose a unified taxonomy of influencing factors and a structured classification of decision-support approaches.Beyond summarization, the study identifies critical research gaps such as underexplored rural deployment models, limited real-time data integration, and inconsistent treatment of user behavior.To bridge these gaps, we suggest a hybrid GIS-Machine Learning (ML) conceptual framework and offer insights for future work aimed at scalable and equitable EVCI deployment.The outcomes provide urban planners, policymakers, and researchers with a roadmap for technically sound and sustainable infrastructure planning.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".