Mapping three decades of research on artificial intelligence in electric vehicles: A comprehensive bibliometric and knowledge domain analysis (1995–2024)

Abstract

Purpose

This study aims to map the intellectual landscape of Artificial Intelligence (AI) applications in Electric Vehicles (EVs) from 1995 to 2024, highlighting global research trends, key contributors, and emerging thematic areas through a comprehensive bibliometric analysis.

Design/methodology/approach

This is a quantitative study employing bibliometric methods to analyze 1245 English-language documents retrieved from the Scopus database. Using Bibliometrix (R-package) and VOSviewer, the analysis covered publication trends, citation patterns, co-authorship networks, institutional and country-level contributions, and keyword co-occurrences. Key bibliometric laws (e.g., Lotka’s and Bradford’s) and performance indicators (e.g., h-index, g-index) were used to assess productivity and impact.

Findings

The results reveal a strong upward trajectory in AI–EV research, with an annual growth rate of 22.69% and a surge in publications after 2016. China, India, and the U.S. are the most prolific contributors, while collaboration networks show increasing global integration. Core research themes include battery management, smart charging, autonomous driving, and reinforcement learning, with deep learning and predictive modeling playing central roles.

Originality

This is the first large-scale, longitudinal bibliometric study to systematically analyze the evolution of AI–EV research over a 30-year span. The findings offer a structured knowledge base for academics, policymakers, and industry stakeholders, highlighting key trends, research gaps, and opportunities for future innovation in AI-driven electric mobility.