Abstract

Hybrid Electric Vehicles (HEVs) are known for their ability to reduce carbon emissions and fuel consumption. However, managing the thermal aspects of HEVs, especially concerning their powertrains and battery systems, remains a significant challenge. Traditional cooling mechanisms often result in inefficiencies due to their static nature. This paper proposes an AI-based thermal management system designed to address these limitations by offering dynamic, adaptive thermal regulation for HEVs. The system integrates real-time data monitoring with AI algorithms to optimize the cooling process based on vehicle operating conditions, thereby reducing energy wastage and enhancing overall vehicle performance. By employing machine learning models, the system can predict thermal anomalies and adjust cooling efforts accordingly, thereby preventing overheating, improving battery life, and minimizing wear on the electric motor and power electronics. The proposed AI-based thermal management system combines predictive analytics, data-driven decision-making, and advanced cooling methodologies to ensure optimal thermal regulation under varying driving conditions. This paper provides a comprehensive overview of existing thermal management technologies in HEVs, identifies current challenges, and presents the AI-enhanced system as a cutting-edge solution for improving HEV efficiency and reliability