Electric-vehicle energy management and charging scheduling system in sustainable cities and society

Plug-in Electric Vehicles (PEVs) have gained the user's attention due to their smart and cost-effective and environment-friendly services. With many benefits, the PEVs services have suffered from different challenges like battery charging management, increasing electric charges, and availability of charging stations and battery life estimation. Various different types of algorithms, deep learning and machine learning solutions, artificial intelligence solutions have been proposed for PEVs. However, the existing solutions have focused the one or two components of PEVs. To address these limitations, we propose an Electric Vehicle-Intelligent Energy Management and Charging's Scheduling System (EV-EMSS) for charging station and PEVs management system. The proposed system provides convenient energy management services by using battery control units and communication with charging stations for charging decisions. This system facilitates the drivers to take the best charging decision and communicate with charging stations for further decision. The proposed system has a secure mechanism to protect all the data from any unauthorized access. The results show the better performance of the proposed system in dense and sparse traffic conditions.