학술논문

Machine Learning Approaches for EV Charging Management: A Systematic Literature Review
Document Type
Conference
Source
2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023 IEEE International Conference on. :1-6 Sep, 2023
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Vehicle-to-grid
Renewable energy sources
Systematics
Publishing
Bibliographies
Electric vehicle charging
Reliability
Electrical Vehicle
EV charging management
battery management
vehicle-to-grid
machine learning
Language
Abstract
The article discusses the importance of EV charging management in meeting the growing demand for EV charging infrastructure. The review covers various topics related to EV charging management, including charging protocols, energy management, demand response, smart charging, vehicle-to-grid (V2G), load balancing, dynamic pricing, renewable energy integration, grid integration, interoperability, cybersecurity, battery management systems, data analytics, and artificial intelligence. The article also highlights the importance of machine learning-based approaches for the efficient performance of EV charging management. The systematic literature review is divided into three phases, i.e., identification, screening, and eligibility, and includes relevant research papers that address approaches for EV charging management. The article aims to provide insights into the development of effective policies and strategies for sustainable and reliable EV charging infrastructure. The overall findings show the growing significance and progress within EV charging management, spurred by the imperative to establish sustainable and effective charging infrastructure for electric vehicles.