Smart Power Control in Electric Vehicles (EVs): Machine Learning and Control Strategies for Vehicle-to-Grid Integration

Abstract

The transition towards Electric Vehicles (EVs) and their integration into smart grids has sparked interest in advanced power management strategies that optimize energy usage and support grid stability. Vehicle-to-Grid (V2G) technology, enabling bidirectional energy flow between parked EVs and the grid, plays a crucial role in this landscape. V2G systems leverage the energy storage capacity of EV batteries to act as distributed resources, facilitating energy transfer during peak demands or renewable energy fluctuations. Traditionally, these systems rely on two dedicated power conversion stages: an AC-DC converter for power factor correction and a DC-DC converter for voltage matching. However, a single bidirectional ac-dc conversion stage can also effectively manage both V2G and Grid-To-Vehicle (G2V) energy exchanges. This paper introduces a novel approach combining Machine Learning (ML) techniques with advanced control strategies to optimize power flow and enhance the efficiency of EV-to-grid interactions. By integrating intelligent algorithms with sophisticated power electronics controls, we explore how V2G operations can be optimized for energy efficiency, grid stability, and overall performance. The research highlights the potential for ML-driven predictive models to improve decision-making in dynamic energy environments while supporting the growing role of renewable energy sources in the grid. This study offers insights into the future direction of smart power control in EVs and the evolving synergy between control systems, power electronics, and machine learning.