AI and Big Data Integration for Real-Time Decision Support in Electric Vehicle Ecosystems

Abstract

Electric vehicle (EV) adoption is reshaping the landscape of transportation by creating an ecosystem that requires real-time decision-making through intelligent solutions based on the increasing amount of data that will be generated by current and future EVs, charging stations, smart grids, user behaviours and evolving environmental sensors that will all produce an enormous number of different types of data. The rapid speed in which these data streams arrive and the volume of data generated from these diverse sensors make it impossible for traditional analytic methods to process and react accordingly. The aim of this study is to demonstrate how AI analytics with scalable big data architectures together can provide a system framework for intelligent predictive, adaptive, and autonomous decision-making in EV ecosystem environments. The study will evaluate existing technology platforms, identify gaps in the existing body of research, and propose a unified approach to support the real-time acquisition, processing and intelligent optimization of data generated by EV ecosystems. Some of the core areas of focus for this study will include load forecasting, charge scheduling, route optimisation, battery health and charging, and grid stability. Through the integration of AI & big data, EV networks are able to operate more efficiently, with decreased energy usage while providing better end-user experiences through quicker responses to requests. The study discusses how developing intelligent data pipelines, machine learning models & real-time controls can help develop sustainable & robust EV charging infrastructure. Based on the results of this study the authors suggest that decisions concerning EV charging will require the use of AI supported decision support systems as there will be considerable complexity and scope in future electric mobility systems.