Iot and AI Framework for Real-Time Control of Electrical Vehicles
DOI:
https://doi.org/10.31838/NJAP/08.02.04Keywords:
artificial intelligence, driving behaviour, Internet of Things, machine learningAbstract
Utilizing the Industrial Internet of Things in electric vehicles represents a major shift toward transportation systems that are more sophisticated, interconnected, and efficient. This research provides a thorough examination of the importance of the Industrial Internet of Things in improving multiple facets of electric vehicle technology, such as predictive maintenance, vehicle connectivity, personalized user management, energy and fleet optimization, and autonomous features. Alongside case studies showcasing practical implementations, important IoT applications are analyzed, including advanced driver-assistance systems and vehicle-to-grid connections. The findings show that although grid stabilization lowers electricity consumption and improves functional sustainability, it is the advanced charging stations enabled by IoT that shorten charging durations. Battery Management Systems (BMSs) assist in minimizing maintenance frequency and extending battery life. The combination of the Internet of Things (IoT) and artificial intelligence (AI) improves the safety and effectiveness of autonomous EV operations through the optimization of driving behavior, route planning, and energy consumption. This report addresses a number of challenges, including cybersecurity, connectivity, and integration with antiquated systems. It also discusses new trends driven by AI, machine learning, and developing IoT technologies. By analyzing the overlap between IoT and EVs, this study highlights how IoT may accelerate the global transition to smart and environmentally friendly transportation solutions.The rapid adoption of electrical vehicles (EVs) demands intelligent frameworks capable of ensuring efficiency, safety, and sustainability in real-world environments. This study proposes an integrated IoT and AI-based framework for the real-time control and monitoring of EVs. The IoT layer enables seamless data acquisition from onboard sensors, charging infrastructure, and traffic systems, while cloud and edge computing platforms ensure continuous communication and interoperability. Artificial Intelligence algorithms are employed for predictive analytics, including energy consumption forecasting, battery health estimation, adaptive route optimization, and driver behavior analysis. Furthermore, the framework supports Vehicle-to-Grid (V2G) communication, facilitating smart energy distribution in renewable-integrated grids. Experimental simulations and prototype implementation highlight improvements in battery utilization, reduction in charging delays, and enhanced driving safety. The proposed IoT–AI framework demonstrates its potential as a robust solution for advancing next-generation smart mobility and sustainable transportation ecosystems.
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