Planning of Electric Vehicle Charging Infrastructure: A Review and a Conceptual Framework Based on Spatial and Predictive Analysis

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.