AI-IOT-Based Adaptive Control Techniques for Electric Vehicles

Abstract

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed the field of sustainable transportation planning, notably in the context of Electric Vehicles (EVs). The Problems in all the Existing models are not having proper coordination of optimizing the elements of EV functioning. This research proposes a novel strategy to improving sustainable transportation planning by utilizing AI and IoT. AI-powered algorithms analyze real-time data from IoT sensors installed in EVs and the surrounding environment. These adaptive control algorithms are designed to solve issues including range anxiety, charging infrastructure optimization and energy efficiency. Predictive analytics, route optimization, energy management, and grid interface are all part of the proposed system. Energy management algorithms alter EV settings dynamically to maximize efficiency while taking into account real-time traffic conditions thereby increasing the range extension is upto 2.5% and the total energy efficiency is improved upto 92%. Furthermore, bidirectional connection allowed by IoT devices facilitates the integration of EVs into the energy grid. EVs may engage intelligently in Vehicle-to-Grid (V2G) interactions, providing grid services such as energy delivery during peak demand periods and grid stability.