Comprehensive review of electric vehicle charging infrastructure: Technologies, standards, and smart energy management

The growing adoption of electric vehicles (EVs) over the past decade has significantly increased the demand for reliable, efficient, and sustainable charging infrastructure. This paper presents a comprehensive review of EV charging station (EVCS) research published between 2015 and 2025, covering EV adoption trends, emissions reduction, cost dynamics, and policy drivers. It evaluates key charging technologies, including AC, DC, wireless, and ultra-fast charging, with emphasis on integration with renewable energy systems. The study further analyzes global standardization efforts and intelligent load management strategies for efficient grid operation, along with roles of vehicle-to-grid (V2G) and vehicle-to-home (V2H) in enhancing grid resilience and distributed energy storage. The review synthesizes findings from empirical studies and technical reports, highlighting advancements in conductive and wireless charging, GIS- and optimization-based siting methods, and hybrid renewable-powered EVCS architectures. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly utilized to optimize charging schedules, forecast grid demand, and detect system anomalies. For example, reinforcement learning-based approaches enable cost-efficient dynamic scheduling, while deep learning models improve fault detection accuracy. Despite these developments, challenges such as interoperability, rural infrastructure gaps, battery degradation trade-offs, and cybersecurity vulnerabilities persist.This review uniquely integrates techno-economic, environmental, and cybersecurity perspectives, providing a comparative assessment of emerging technologies and intelligent energy management strategies. It offers actionable insights and future research directions to support development of resilient, secure, and sustainable EV charging ecosystems. These contributions help policymakers, utilities, and researchers design scalable, interoperable, and economically viable charging infrastructures for future mobility. • Comprehensive review (2015–2025) of EV adoption, emission, cost, and policy drivers. • Evaluation of AC, DC, wireless, and ultra-fast charging for renewable systems. • Analysis of global standardization and intelligent load management strategies. • Assessment of V2G and V2H roles in grid resilience and energy storage. • Cybersecurity integrated with techno-economic evaluation of smart charging.

Authors

Institutions

Publication Details

Journal
Future Batteries
Published
2026-04-11
DOI
https://doi.org/10.1016/j.fub.2026.100177
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comprehensive review of electric vehicle charging infrastructure: Technologies, standards, and smart energy management

Hemant Kumar Meena, Kirti Pal, Anand Mohan
Future Batteries
Electric Vehicles and Infrastructure
article

Comprehensive review of electric vehicle charging infrastructure: Technologies, standards, and smart energy management

Hemant Kumar Meena, Kirti Pal, Anand Mohan
article en

Abstract

The growing adoption of electric vehicles (EVs) over the past decade has significantly increased the demand for reliable, efficient, and sustainable charging infrastructure. This paper presents a comprehensive review of EV charging station (EVCS) research published between 2015 and 2025, covering EV adoption trends, emissions reduction, cost dynamics, and policy drivers. It evaluates key charging technologies, including AC, DC, wireless, and ultra-fast charging, with emphasis on integration with renewable energy systems. The study further analyzes global standardization efforts and intelligent load management strategies for efficient grid operation, along with roles of vehicle-to-grid (V2G) and vehicle-to-home (V2H) in enhancing grid resilience and distributed energy storage. The review synthesizes findings from empirical studies and technical reports, highlighting advancements in conductive and wireless charging, GIS- and optimization-based siting methods, and hybrid renewable-powered EVCS architectures. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly utilized to optimize charging schedules, forecast grid demand, and detect system anomalies. For example, reinforcement learning-based approaches enable cost-efficient dynamic scheduling, while deep learning models improve fault detection accuracy. Despite these developments, challenges such as interoperability, rural infrastructure gaps, battery degradation trade-offs, and cybersecurity vulnerabilities persist.This review uniquely integrates techno-economic, environmental, and cybersecurity perspectives, providing a comparative assessment of emerging technologies and intelligent energy management strategies. It offers actionable insights and future research directions to support development of resilient, secure, and sustainable EV charging ecosystems. These contributions help policymakers, utilities, and researchers design scalable, interoperable, and economically viable charging infrastructures for future mobility. • Comprehensive review (2015–2025) of EV adoption, emission, cost, and policy drivers. • Evaluation of AC, DC, wireless, and ultra-fast charging for renewable systems. • Analysis of global standardization and intelligent load management strategies. • Assessment of V2G and V2H roles in grid resilience and energy storage. • Cybersecurity integrated with techno-economic evaluation of smart charging.

Future BatteriesVol. 10
Gautam Buddha University (IN), Malaviya National Institute of Technology Jaipur (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 29%
Electric Vehicles and Infrastructure
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.