Green energy vehicles (GEVs) are increasingly central to low-carbon energy transitions, but research remains fragmented across vehicle technologies, battery systems, critical materials, circularity, and sustainable supply chains. This bibliometric and thematic review maps the intellectual structure and evolution of GEV research using 904 English-language Scopus articles and reviews published during 2015–2025. Performance analysis, reference co-citation, author–keyword co-occurrence, longitudinal thematic analysis, and cluster-guided content synthesis were combined. The corpus accumulated 34,955 citations, and annual output rose from 13 publications in 2015 to 287 in 2025 (CAGR ≈ 36.3%). The intellectual base is strongly battery-centered, linking critical-material bottlenecks, recycling, second-life use, closed-loop systems, and circular business models. Six research streams were identified, spanning supply-chain and policy optimization, circular economy, critical-mineral security, battery life-cycle assessment, low-carbon mobility, and closed-loop recovery. Circular-economy and sustainable-supply-chain themes became markedly more prominent after 2019, while critical-mineral security regained attention in 2022–2025. The international dimension is framed primarily through material sourcing, battery value chains, resilience, and cross-border circularity. The study proposes a technology–resources–energy systems–governance–sustainability framework and priorities for integrating bibliometrics with energy-system, material-flow, trade, and geopolitical-risk data.

Green energy vehicles (GEVs) are increasingly central to low-carbon energy transitions, but research remains fragmented across vehicle technologies, battery systems, critical materials, circularity, and sustainable supply chains. This bibliometric and thematic review maps the intellectual structure and evolution of GEV research using 904 English-language Scopus articles and reviews published during 2015–2025. Performance analysis, reference co-citation, author–keyword co-occurrence, longitudinal thematic analysis, and cluster-guided content synthesis were combined. The corpus accumulated 34,955 citations, and annual output rose from 13 publications in 2015 to 287 in 2025 (CAGR ≈ 36.3%). The intellectual base is strongly battery-centered, linking critical-material bottlenecks, recycling, second-life use, closed-loop systems, and circular business models. Six research streams were identified, spanning supply-chain and policy optimization, circular economy, critical-mineral security, battery life-cycle assessment, low-carbon mobility, and closed-loop recovery. Circular-economy and sustainable-supply-chain themes became markedly more prominent after 2019, while critical-mineral security regained attention in 2022–2025. The international dimension is framed primarily through material sourcing, battery value chains, resilience, and cross-border circularity. The study proposes a technology–resources–energy systems–governance–sustainability framework and priorities for integrating bibliometrics with energy-system, material-flow, trade, and geopolitical-risk data.

Artificial intelligence (AI) is increasingly embedded in electric mobility, but evidence on electric two-wheelers (E2Ws) remains dispersed across engineering, safety, shared mobility, and user-oriented research. This bibliometric science-mapping study examines how AI paradigms have evolved and specialized across E2W problem domains from 2015 to 2025. A reproducible Scopus search retrieved 759 records; focality screening retained 568 Direct publications, 118 Adjacent records for sensitivity analysis, and 73 Exclusions. The analysis combined performance mapping, co-word/co-citation/coupling networks, non-exclusive AI-family and application-domain coding, specialization lift with enrichment testing, and robustness checks. Annual output rose from 8 papers in 2015 to 166 in 2025 (CAGR = 35.4%), with 52.5% of the Direct corpus published in 2024–2025. Computational-intelligence prevalence fell from 48.2% to 20.7%, while classical machine learning and deep learning each reached about 41% in 2023–2025. The strongest specialization signals were generative AI–design/HMI (lift = 4.70), reinforcement learning–routing/logistics (2.63), and computer vision–safety/perception (2.24). AI-family coding was highly reproducible, although several application-domain boundaries were less stable. Overall, AI–E2W research follows problem-dependent technological trajectories rather than a single linear maturity path.