A Review of Charging Schemes and Machine Learning Techniques for Intelligent Management of Electric Vehicles in Smart Grid

Abstract

Dynamic charging Data-driven techniques Load prediction Signal processing Machine learning

Data-driven techniques Load prediction Signal processing Machine learning

Load prediction Signal processing Machine learning

Signal processing Machine learning

Machine learning

The evolution of information and communication technology (ICT) contributes to the realization of smart cities. A smart grid is a vital element of any smart city. One of the major focuses of the upcoming smart cities is the deployment of ecofriendly intelligent systems to sustainably improve the quality of life of its habitants. To attain sustainable and green transportation, the deployment of electric vehicles (EVs) is evolving. Integration of EVs has raised various challenges such as charging infrastructures and load forecasting. Therefore, intelligent management techniques are required in this context. Various appealing tactics have been presented to solve such challenges. These are mainly based on the Internet of Things (IoT), machine learning algorithms and automata models. This chapter presents a comprehensive review of the electric vehicle charging schemes, standards, and application of various machine learning algorithms to intelligently manage the electric vehicle in the smart grid based future cities.