A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems

Abstract

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm’s performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values.