DEVELOPMENT OF AN AI-BASED ENERGY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLES

– Electric vehicles (EVs) are emerging as a viable alternative to conventional internal combustion engine vehicles due to their environmental benefits and energy efficiency. However, efficient energy management remains a critical challenge, affecting EV performance, battery life, and overall sustainability. The integration of artificial intelligence (AI) into energy management systems (EMS) offers promising solutions for optimizing energy distribution, improving battery health, and enhancing driving range. AI-based EMS can dynamically adjust power flow, predict energy consumption, and optimize charging strategies based on real-time data. This paper explores the development of an AI-based energy management system for EVs, focusing on machine learning algorithms, predictive modeling, and real-time decision-making. The study highlights various AI techniques such as deep learning, reinforcement learning, and fuzzy logic for optimizing energy efficiency in EVs. Additionally, it discusses the role of AI in load forecasting, route optimization, and battery state-of-charge (SoC) prediction. A literature review identifies existing methods, limitations, and research gaps in AI-based EMS for EVs. The methodology section outlines the system architecture, data acquisition, algorithm selection, and evaluation metrics. Experimental results demonstrate the effectiveness of AI-driven energy management in enhancing vehicle efficiency and reducing energy wastage. The findings suggest that AI-based EMS can significantly contribute to sustainable transportation by improving energy efficiency and battery longevity.