This study presents a framework for designing and optimizing ship energy systems including weather-driven speed variability and navigation safety constraints. Navigation risks including resonance, surf-riding, and successive high-wave impacts, are calculated using five years of hourly weather data. Random speed variations (up to ±5%) are applied to a baseline speed profile to capture operational uncertainty, and safety-based speed reductions (up to 40%) are applied when required. Course changes are excluded. Treating navigation risks as constraints, operating profiles are generated for different weather conditions. For a conceptually retrofitted cargo ship, hydrogen fuel cell and battery capacities, and their power distribution, are optimized for each operating profile to minimize lifetime energy system cost and assess the effects of weather-induced power variation. Results show that speed and weather variability can significantly change power demand, requiring fuel cell capacities between 700 and 1500 kW. The most common configuration is a 1200 kW fuel cell system with 180 kWh of battery capacity, covering 39% of laden profiles, while full power coverage requires 1500 kW. Lifetime cost outcomes exhibit a 5th–95th percentile spread of −10.3% to +11.1% relative to mean cost. The results demonstrate the significant influence of weather variability on system sizing and cost.

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.

Electrification of ship power systems plays a central role in the mobility transition toward sustainable transportation. The integration of a large number of components with distinct characteristics into a shipboard microgrid benefits from a modular design and standardized interfaces. Key challenges lie in the variety of component characteristics, and an evolution of parameters during the power system operation. Further, topology alterations can occur over time, requiring a reformulation of the optimal power dispatch problem. Accordingly, a modular energy management strategy must be adaptive to these changes. This work explores a distributed energy management architecture with a central coordinating agent, realized via Lagrangian dual decomposition and a gradient-based solver. This architecture ensures both local feasibility while reaching global optimality and a power balance through a consensus mechanism. Parameter changes are incorporated in local cost functions, making extensive data exchange with a central unit obsolete. Handling a variable number of power system components, this approach is resilient to component faults, topology redesigns, and component degradation. The method is applied to a fuel-cell battery hybrid harbor tug equipped with multiple parallel modules with unique ratings and state-of-health. The energy management strategy minimizes total operating costs, based on hydrogen fuel consumption and cell degradation. Extensive mission simulations show similar performance for the distributed approach and a centralized equivalent. The predictive strategy is demonstrably superior to instantaneous optimization, yielding a cost reduction of 18.3% with a 15 min prediction horizon. The model predictive control performance increases with the horizon length, reducing operation costs by an additional 6.0% at 60 min. In addition, a local decision-making heuristic shows promising potential for the cell degradation via optimized timing of ON- and OFF switching. At 15 min, this reduces operation costs by 3.0% and at 60 min by 12.7%. Finally, the distributed optimization is deployed on real-time target machines to showcase the applicability of the approach on actual controller and communication hardware.

Hydrogen fuel-cell and battery systems can reduce ship emissions, but their early-stage design requires the simultaneous consideration of component sizing, operational dispatch, hydrogen production pathway, onboard storage, and component degradation. This study proposes a degradation-aware two-stage optimisation framework for the design and assessment of hydrogen hybrid marine energy systems. The first stage applies mixed-integer linear optimisation to determine the fuel-cell and battery capacities and their mission-level power allocation, while the second stage evaluates the resulting operation using fuel-cell voltage-loss and battery-ageing models. The framework is demonstrated for the retrofit of an 89.9 m short-sea cargo vessel over a representative 194 h mission. For electrolytic hydrogen produced using proton-exchange-membrane electrolysis, the selected fully electrified configuration comprises five 150 kW fuel-cell units and three 100 kWh battery modules and consumes 7189 kg of hydrogen per mission. Under the adopted accounting boundary, the system reduces mission emissions by 92.4% relative to the diesel reference case. However, hydrogen storage represents 87.0% of the total capital cost at a storage-specific cost of USD 700 kg⁻¹, while the required fuel volume is approximately 300 m³ for compressed hydrogen at 350 bar or 103 m³ for liquid hydrogen. At three missions per month, the fuel-cell health indicator reaches its prescribed limit after approximately 31 months, whereas the battery retains 93.0% of its initial capacity. The results identify hydrogen storage and fuel-cell degradation as the principal constraints governing the technical and economic feasibility of hydrogen-powered ship retrofits.

Floating offshore wind turbines can harness the more consistent and powerful winds in deep-water offshore areas. However, they face challenges due to complex loading mechanisms across various load cases. Fully coupled nonlinear time-domain response and load analyses are computationally expensive, making them unsuitable for highly iterative design optimisation under all limit states’ constraints. This research presents a hybrid domain (frequency and time) and hybrid paradigm (physics-based, surrogate-augmented) approach that strikes a balance between accuracy and computational efficiency. The motion response is obtained in the frequency domain, while structural loads are computed from the inverse fast Fourier transform of the motion spectra. This approach combines the efficiency of the frequency-domain approach with the ability to model some load nonlinearities in the time domain. To further enhance efficiency, two dedicated surrogate models have been developed and embedded into the workflow. The first surrogate predicts hydrodynamic potential coefficients over the relevant range of wave frequencies, thereby replacing the need for repeated potential-flow computations in the design loop. The second surrogate emulates the time-domain mooring line loads as a function of mooring design and fairlead motion, acting as a fast proxy for more computationally demanding dynamic mooring analyses. The mooring surrogate is agnostic to platform design and environmental conditions, offering broader applicability than existing approaches. Both surrogates are constructed as data-driven, multi-output regression models trained on large datasets generated from higher-fidelity numerical simulations, tailored to provide accurate predictions in milliseconds. This novel approach provides accurate design trend predictions while significantly reducing solution time. It enables fast and comprehensive FOWT design optimisation, accelerating early-stage development and allowing greater complexity from the outset.

As navies and maritime organisations transition towards low-emission propulsion systems, spark-ignited (SI) gas engines capable of operating on sustainable, low-reactivity fuels are gaining renewed interest. These engines, while offering potential for fossil-free operation, present significant challenges under transient conditions due to complex interactions between throttle control, fuel regulation, and combustion stability. Accurate dynamic modelling is critical to integrate these engines into resilient naval power systems and to support the development of advanced control strategies. This study evaluates several high-fidelity mean value first principle engine modelling (MVFPEM) approaches for simulating the dynamic gas path behaviour of a large, high-speed, SI marine engine under rapid load changes. Models with varying levels of complexity, including simplified and full turbocharger implementations and different gas path volume resolutions, were calibrated using a single measurement campaign and validated against measured transient data. Several methods for turbocharger performance mapping (Stapersma, Casey & Robinson, and Jensen) were evaluated for their applicability in predicting the engine behaviour in dynamic operating scenarios. The results highlight that models incorporating three control volumes and full turbocharger dynamics achieve the highest accuracy, particularly during rapid load increases and recovery phases. Simplified models fail to capture turbocharger inertia and pressure transients, limiting their applicability to investigate naval propulsion or electric power generation plant behaviour under transient load conditions. This work provides guidance on selecting and validating engine models for marine applications and reinforces the role of high-fidelity MVFPEMs in the design and simulation of future naval energy systems.

The trend of electrification of propulsion systems also introduced all-electric drive in the maritime sector. Maritime all-electric drive systems operate using an energy system containing a variety of components, such as batteries, internal combustion engines, or fuel cells. The introduction of new components in the energy system increases both the flexibility as well as the complexity of the system operation. The most commonly used rule-based control is no longer sufficient to solve the control problem. Consequently, the usage of advanced control strategies in maritime has become a topic of research in recent years. In the operation of a maritime energy system, several objectives are of interest as targets of the optimisation, including cost, emission, or an enlargement of component lifetime. Depending on the choice of objective, the control strategy can differ. By integrating multiple objectives in control, the operation is optimised to find the best working point to fulfil the different interests. This article first reviews the commonly used advanced control structures in the maritime, automotive, and building control sectors. A comparison is used to identify further potential for advanced control usage in marine applications. In addition, the implementation of advanced control is reviewed in architecture and optimisation algorithms. Secondly, the control objectives used in the literature are presented and analysed in terms of their usage and potential of the combination. Thirdly, the currently used validation strategies and published results are reviewed and interpreted in terms of potential and required future work. Lastly, open gaps in the state of research are identified and potential for future work is outlined.

The electrification of shipboard power systems (SPSs), combined with the introduction of heterogeneous power sources and energy storage technologies, is driving a need for more advanced and structured control strategies. This review examines control methods and architectures for DC ships, with a specific interest in power systems integrating energy storage systems and zero-emission power generation. Control methods are categorized based on both their functionality and architecture, evaluating their resilience, adaptability, and scalability. Different hierarchical layers are reviewed, distinguishing local control, coordinated control, and energy management methods. Key challenge in the coordinated control arise due to large load fluctuations, constant-power loads, low inertia, and diverse dynamic capabilities of power sources and storage systems. These characteristics complicate voltage stability, dynamic power sharing, and state-of-charge management. Decentralized, centralized, and distributed control architectures are reviewed with respect to scalability, communication requirements, and fault tolerance. At the high-level layer, energy management strategies are discussed in terms of operational efficiency and resiliency, with predictive and distributed methods forming key trends in shipboard power system control. The review highlights the need for resilient, adaptive, and scalable control solutions tailored to future DC SPSs, particularly those integrating fuel cells and energy storage technologies.

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.

Modern power grids are becoming increasingly complex with the integration of heterogeneous distributed energy resources, underscoring the need for accurate and efficient Power Flow Analysis to ensure stability, reliability, and market operations. Existing methods generally rely on iterative numerical techniques (INT) or machine learning (ML). While INT is physically consistent and highly accurate, it can be computationally expensive and vulnerable to slow or non-convergence. ML methods offer faster solutions but often require extensive data, suffer from limited extrapolation capabilities, and lack physical consistency. Physics-informed ML (PIML) bridges these gaps by embedding domain knowledge before, during, and after training. However, current PIML approaches typically do not leverage this full range of opportunities. In this paper, we propose a novel PIML framework for Power Flow Analysis that integrates physical insights at all three stages (pre, in, and post-processing) to achieve superior accuracy and efficiency. Notably, we introduce a new post-processing technique that partitions the power network into its mesh and radial components: the mesh portion is handled via PIML, while the radial portion is efficiently solved with a convex optimization approach informed by the PIML outputs. This approach is efficient with radial topologies, especially in power distribution networks where the radial part is predominant. Experiments on realistic power networks demonstrate that our method outperforms state-of-the-art approaches in both accuracy and computational performance.

Low total lifetime cost is essential for the adoption of zero-emission ship energy systems, which must meet operational power demands while complying with onboard safety regulations. However, many studies rely on a simplified, averaged or insufficiently representative load profile and treat system design, operation, and integration feasibility separately, which can distort lifetime cost assessments and result in practically infeasible retrofit concepts. This study investigates how a hydrogen-based ship energy system can be optimally sized, operated, and arranged onboard to minimize total lifetime cost while satisfying operational constraints and stability requirements for a general cargo vessel retrofit. A representative power profile is synthesized from one year of operational data using a probability-based downsampling method and then used in a mixed-integer nonlinear lifetime cost optimization with discrete placement and ballast decisions, solved using the SCIP solver. The optimal retrofit comprises 1.4 MW of fuel cells, 180 kWh of batteries, and a 146 m³ liquefied hydrogen (LH₂) tank, requires 171 t of ballast to satisfy trim and vertical stability constraints, and is primarily driven by fuel costs, which account for 74% of the total lifetime cost. Overall, the results indicate that the viability of hydrogen-based ship retrofits primarily depends on LH₂ storage integration constraints and hydrogen price assumptions, and that the proposed framework provides a practical basis for lifetime cost assessment of feasible retrofit designs.

To reduce emissions, methanol is a favorable carbon-neutrally-producible alternative fuel, which can substitute gasoline in direct-injection spark-ignition (DISI) engines. Robust DISI engine operation relies on a consistent air-fuel mixture. To understand the physical processes that characterize the mixture formation, predictive computational fluid dynamics (CFD) simulations are used to improve the understanding, operation, and emissions of these engines. However, using an alternative fuel such as methanol often poses challenges to the CFD simulations’ validity due to the alteration of the fuel properties. This study presents the validation of a CFD modeling approach that can be applied to the predictive modeling of DISI methanol engines. Our methodology uses Lagrangian-Eulerian methods to model the methanol eight-hole counter-bore style Spray M injector from the Engine Combustion Network (ECN). We used the Spray M1 condition, which represents a late-injection spray under a high ambient pressure and temperature environment. For the present study, we employed both a Reynolds Averaged Navier Stokes (RANS) and a Large Eddy Simulation (LES) turbulence approach in CONVERGE-CFD. To validate our models, we used the projected liquid volume (PLV) maps generated by the tomographic liquid volume fraction (LVF) based on methanol. Subsequently, we tuned our models based on the corresponding numerical predictions of the liquid penetration and LVF distributions. The results demonstrated that both the RANS and LES models could replicate the spray morphology and liquid length. While the RANS model was unable to fully capture the complex phenomena of spray collapse and sweeping in the methanol multi-hole spray, the LES model effectively reproduced these behaviors without excessive tuning effort.

The maritime sector aims to achieve short and medium-term sustainability targets through the conversion of Internal Combustion Engines to methanol operation. For small to medium sized engines, Port Fuel Injection (PFI) is the most viable injection method to achieve this conversion. However, the knowledge of the behaviour of methanol in combustion engines, particularly its spray characteristics under PFI conditions, is limited. To better understand liquid methanol sprays, this paper studies the injection of methanol in marine PFI conditions through Computational Fluid Dynamics (CFD) modelling. The CFD models use the Lagrangian-Eulerian (LE) coupling method within the Reynolds Averaged Navier Stokes (RANS) turbulence framework. Numerical results were validated using dedicated methanol experiments from the literature for both high and low injection pressures. Subsequently, this predictive CFD framework was used in a number of different injection pressures with scaled injection quantities that represent marine applications. Moreover, we demonstrated that high injection pressure improves atomisation and, thus, evaporation prior to wall impingement. This work strongly contributes to our understanding of marine PFI methanol engines by modelling fuel quantities relevant for ship applications. Our approach can be implemented in full engine simulations to solve evaporation challenges often found in small-bore methanol marine engines.

Renewably produced methanol is a promising fuel for internal combustion engines in long-range transportation thanks to its scalability, liquid storage, and favorable combustion properties. However, the distinction between different injection and ignition strategies for methanol engines and the resulting combustion mechanisms has not been consistently defined. Moreover, diffusion combustion strategies are favored over premixed strategies in large engines because of higher methanol energy fractions, disregarding the advantages of premixed approaches, such as reduced nitrogen oxide emissions and retrofitting opportunities. To address ambiguity in terminology, this paper proposes a classification framework for injection and ignition strategies and applies it to methanol-fueled internal combustion engines. Subsequently, this review focuses on experimental studies of methanol-fueled heavy-duty engines, which are crucial for transitioning to renewable and sustainable energy in long-range transportation. This research summarizes the impact of the reviewed injection and ignition strategies on combustion characteristics, engine performance and emissions to identify key trends. Furthermore, this review highlights how specific design and operating parameters influence premixed dual-fuel combustion, offering insights into optimizing performance and emissions. While mono-fuel and premixed dual-fuel strategies with methanol can significantly promote methanol use in heavy-duty engines and reduce harmful emissions like nitrogen oxides, a rise in unburned hydrocarbon emissions may also be expected, necessitating further research in this area. Additionally, methanol injection location in premixed dual-fuel schemes affects its cooling effect, influencing volumetric and thermal efficiency. Overall, this study deepens our understanding of methanol's impact on heavy-duty engine performance, highlighting critical challenges to be addressed for advancing sustainable transportation.