ML-Enhanced Resource Optimization & Sensor Synchronization in IIoT-Integrated V2L via Edge Intelligence & Adaptive Visualization

This research explores the use of Electric Vehicles (EVs) as backup power for households during peak hours, integrating IoT-enabled sensors for performance monitoring. It focuses on designing and analyzing vehicle-to-load (V2L) systems combined with IoT technologies, offering an energy-efficient solution for remote industrial utility monitoring. The study includes developing a DAB converter, PLL-based synchronization, and an LCL filter to improve power quality by reducing THD. By incorporating IoT-equipped sensor nodes, the system enables real-time monitoring, aiming to deliver cost-effective, scalable solutions for industrial efficiency in the Industry 4.0 era. Additionally, machine learning-driven predictive analytics optimize energy distribution and sensor scheduling, reducing latency and enhancing operational reliability. The proposed system not only supports grid resilience but also establishes a framework for smart energy applications.