Energy Management Strategies for Hybrid Electric Vehicles

This review presents an in-depth examination of energy management strategies (EMS) utilized in hybrid electric vehicles (HEVs). The primary goal of EMS in HEVs is to improve fuel efficiency, reduce emissions, and extend battery life by optimizing power distribution between the internal combustion engine (ICE) and electric motor. The review classifies EMS into three categories: rule-based, optimization-based, and data-driven methods. Rule-based methods are relatively simple and computationally efficient but may not always yield optimal performance. On the other hand, optimization-based strategies, which include dynamic programming and genetic algorithms, offer more effective solutions but introduce greater complexity. Data-driven techniques, such as reinforcement learning and neural networks, show promise for adaptive real-time decision-making, thereby improving performance across various driving conditions. This paper highlights the advantages and challenges associated with each method and emphasizes the role of emerging technologies in improving HEV efficiency. By comparing different strategies, this review outlines future research directions for the development of more sophisticated EMS, fostering advancements in HEV technology.