Enhancing IoEV Efficiency: Smart Charging Stations and Battery Management Solutions

Abstract

The proliferation of electric vehicles (EVs) necessitates a robust infrastructure for efficient charging and battery management. This research work focuses on the and aims to enhance the charging processes, optimize charging station operations, and improve battery management using advanced machine learning techniques. The study proposes a comprehensive framework integrating IoT and machine learning to monitor, analyze, and predict charging needs, station availability, and battery health in real-time. By leveraging predictive analytics, the research seeks to minimize charging times, prevent overloading of charging stations, and extend battery lifespan. The findings from this study will contribute to the development of a smarter, more efficient, and sustainable EV ecosystem, addressing the critical challenges of scalability, reliability, and user convenience in the growing field of electric transportation. This research work explores the integration of IoT and machine learning to create a smart ecosystem for electric vehicles (EVs). Focusing on charging, charging stations, and battery management, the study develops predictive models to optimize charging schedules, reduce wait times at stations, and enhance battery longevity. The proposed system aims to provide real-time insights and automation, improving the efficiency and user experience of EV charging networks. By addressing key challenges in EV infrastructure, this research contributes to the advancement of sustainable and intelligent transportation solutions.