IoT-Driven Predictive Analytics for Electric Vehicle Usage Patterns in Digital Societies

Abstract

The speed in which digital, or connected, societies have progressed has increased the acceptance of Electric Vehicles (EVs) and resulted in generating enormous amounts of data through interconnected devices and real-time collection point data via​ the Internet of Things (IoT). The IoT is an integral part of collecting real-time data on the vehicle, the user, and the environment to assist in the utilization of predictive analytics to understand how EVs will be used and make predictions on future use based on those patterns. This research explores how IoT predictive models will support mobility planning, energy optimization, and the sustainability of transportation systems. The integration of sensor networks, cloud-based systems, and machine-learning techniques in developing robust EV ecosystems provides the ability to predict driving patterns, charging demand, battery performance, and traffic interaction. The study proposes an organized framework for acquiring data, processing the data, developing predictive models, and building decision support systems to optimize the utilization of EVs in a digital connected world. This framework shows how predictive analytics will provide benefits for a variety of stakeholders including urban planners, electrical grid operators, transportation service providers, and end-users. The study also addresses challenges regarding data privacy, interoperability, scale, and infrastructure readiness. The outcomes of both simulated and real-life inspired sets of data have shown that there is an increase in charging efficiency, accurate load forecasts, and predictions of travel behaviour. These results indicate that IoT powered predictive analytics will promote the development of smart, efficient, user-oriented electric mobility systems through promoting sustainable urban growth and improving digital society.