AI-Driven Smart Charging Infrastructure for Enhancing IoEV Efficiency and Battery Lifecycle Optimization

Abstract

The increased pace of the electric vehicles (EVs) implementation has increased the urgent demand on effective and smart charging systems to meet the pitfalls associated with the energy issues, grid stability and battery degradation. This paper suggests an intelligent charging system collectively referred to as smart charging, which works alongside the Internet of Electric Vehicles (IoEV) to increase the efficiency of the system and optimize the performance of battery life cycles. The suggested model integrates the use of the Artificial Neural Networks (ANN), Reinforcement Learning (RL), Genetic Algorithms (GA), and Long Short-Term Memory (LSTM) to facilitate predictive, adaptive, and optimized charging decisions. The results of a 500-EV 50-charging station simulated dataset were employed to compare system performance by key metrics. The outcomes show that the efficiency in the charging process was greatly improved with the process achieving a maximum of 96 percent, energy cost saving by 28 percent and grid stability has taken up to 93 percent. Also, battery life cycle was improved by 22 percent and the degradation rates were managed by 8 percent which was deemed to be sustainable in the long run. LSTM and RL were the best algorithms in terms of prediction accuracy and optimalizing with adaptation. The results affirm that smart charging systems are a strong and scalable solution of integrating several AI methods in a framework of IoEV and systems. The study will help establish a sustainable, affordable, and intelligent EV infrastructure in the future to develop smart mobility.