A Review on Smart Charging Approaches for Electric Vehicle

The transportation industry has become a significant contributor to the rising usage of fuel as well as greenhouse gas (GHG) emissions. In order to overcome the problems, we have introduced Electric vehicles (EV) which are an alluring answer to such issues. The significant penetration of electric cars may result in various issues with the distribution network and its dependability owing to the fluctuation in charging demands. Therefore, a variety of strategies are used to forecast the demand for charging EVs and minimize the associated difficulties. Artificial intelligence (AI) approaches are very interesting for the development of electric vehicles (EV) as well as their energy management systems (EMS). Because of EVs high potential for performing complicated parameterization jobs in an efficient manner, AI approaches can be a perfect option. The goal of this article is to offer a comprehensive understanding of smart energy management techniques by reviewing the literature in these domains. EVs should have charge schedules to communicate with power sources, and charging stations and manage charging schedules. Blockchain technology and federated learning (FL) are two new approaches to handling data privacy issues. The analysis of different machine learning approaches for current EV energy management and charging of vehicles, as well as energy trading and challenges of EVs analyzed through the literature is presented in this paper.