Abstract:

Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of hydrogen electric vehicles (HEVs) relies heavily on energy management strategies (EMSs), which directly impact fuel consumption. Developing suitable EMSs for HEVs is challenging, as maximizing fuel economy and performance is crucial. Traditionally, EMSs have been developed using optimal control theory, but recent advancements in machine learning and artificial intelligence (AI) have led to a shift in performance. The study examines numerous case studies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advancements in machine learning and AI, researchers have explored innovative approaches to optimize HEVs’ performance and fuel economy. Key conclusions from the investigation show that machine learning has made a substantial contribution to solving the complex problems associated with HEV energy management. The authors emphasize how machine learning algorithms may be adjusted to dynamic operating environments, how well they can identify intricate patterns in hybrid electric vehicle systems, and how well they can manage non-linear behaviors.