Survey of energy management systems in hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) play a crucial role in promoting sustainable mobility by reducing fuel consumption and emissions. Their effectiveness relies on a well-designed Energy Management System (EMS) that balances power between the internal combustion engine and the electric motor. This article reviews Hybrid Electric Vehicle Energy Management Systems (HEV-EMS), introducing a classification based on practical implementation that distinguishes between Offline and Online methods. It outlines the evolution of energy management strategies, moving from global optimization and rule-based methods to instantaneous, predictive, and learning-based approaches. The focus is on adaptive, data-driven frameworks that operate in real-time. The review also discusses emerging AI trends, such as Physics-Informed Machine Learning and Multi-Agent Reinforcement Learning, while addressing challenges including computational demands and battery degradation. By linking theoretical advancements to industrial applications, this study paves the way for scalable and efficient HEV energy management systems.