State-of-Art Energy Management Strategies for Hybrid Fuel Cell Applications for Ships

Abstract

The maritime industry increasingly adopts hybrid fuel cell systems to reduce emissions and improve energy efficiency. This chapter examines the current state-of-the-art energy management strategies (EMS) for hybrid fuel cell applications in ships. It provides an in-depth analysis of various strategies, including rule-based, optimization-based, and learning-based approaches, highlighting their benefits, challenges, and real-world applications. The review begins with an overview of hybrid fuel cell systems, their configurations, and control strategies, followed by a detailed examination of EMS. Rule-based strategies are discussed in terms of their simplicity and effectiveness in dynamic marine environments. Optimization-based strategies are evaluated for their ability to enhance system and performance through advanced computational techniques. Learning-based strategies, particularly those leveraging machine learning and reinforcement learning, are explored for their potential to adapt to varying operational conditions. The chapter concludes by identifying the technical, economic, and regulatory challenges facing the adoption of these strategies and proposing future research directions.