As Polymer Electrolyte Membrane Fuel Cells (PEMFCs) emerge as a promising technology for transport decarbonization, the development of durability assessment protocols tailored to specific applications, such as maritime operations, is becoming relevant for the identification of stressors and lifetime enhancement. This study presents a preliminary experimental campaign aimed at introducing a methodology to assess the degradation of PEMFCs subjected to Accelerated Stress Test (AST). In particular, the methodology encompasses the utilization of electrochemical characterization and, in this work, the fuel cell operating profile has been chosen to mimic the operation of a small passenger vessel. The tests were carried out on two single Membrane Electrode Assemblies (MEAs) for 500 h. One membrane was subjected to the AST, and a second sample, tested under constant load operation, served as a reference. Periodic electrochemical characterization was conducted to assess performance degradation through polarization curves, electrochemical impedance spectroscopy, and cyclic voltammetry. The electrochemical analysis of degradation was conducted through a dual-method approach combining model-free and model-based methods for impedance analysis, as well as catalyst active area evaluation from voltammograms. Results show that the dynamic operation characteristic of the passenger ferry increases degradation compared to constant operation, evidenced by increased ohmic and interfacial resistances and losses in catalyst active area. This work provides a framework for developing application-specific durability protocols, enriched with multi-method diagnostic approaches to assess PEMFC degradation under realistic maritime conditions. Such methodologies support the development of durability enhancement strategies tailored to maritime applications, allowing a broader application of the technology in the sector.

Low-Temperature Polymer Electrolyte Membrane Fuel Cells (LT-PEMFCs) have recently emerged as a promising solution for sustainable ship energy systems. However, enhancing durability is essential to enable their broader adoption in the maritime sector. Durability enhancement depends on a thorough understanding of degradation mechanisms and accurate prognostics, both of which are highly application-specific. The current literature lacks a comprehensive understanding of LT-PEMFC degradation under maritime operating conditions and its integration into reliable prognostic models. To address this gap, this review provides an overview of LT-PEMFC durability and prognostic models from the perspective of maritime applications. Through a comparative analysis of studies across various sectors, we identify and discuss maritime-specific degradation drivers, including ship load profiles, sodium chloride contamination, vibrations, and wave-induced inclinations. Building on this analysis, we critically evaluate existing prognostic models and their suitability for lifetime prediction in maritime applications. This review proposes durability enhancement strategies based on current knowledge and highlights key research gaps requiring further investigation. In addition, it outlines promising prognostic methodologies and identifies technical challenges for application to maritime LT-PEMFCs. In this way, this work lays the foundation for enhancing LT-PEMFC durability in maritime environments and supporting its broader adoption for zero-emission ships.

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.