In this paper, a meticulous modeling approach is proposed not only for a fuel cell stack itself but also for all auxiliary components that collectively form the fuel cell system. This comprehensive modeling approach encompasses a wide range of components, including, but not limited to, the hydrogen recirculation pump and the air compressor. Each component is thoroughly analyzed and modeled based on the detailed specifications provided by suppliers. This involves considering factors such as efficiency, operating parameters, response times, and interactions with other system elements. By integrating these detailed models, a holistic understanding of the entire fuel cell system’s performance can be attained. Such an approach enables engineers and designers to simulate various operating scenarios, predict system behavior under different conditions, and optimize the system design for maximum efficiency and reliability. Moreover, it allows for informed decision-making throughout the system’s development, deployment, and operational phases, ultimately leading to more robust and effective energy systems. The model validation is performed by comparing experimental data to theoretical results, and the observed difference does not exceed 3%.

The distribution of the magnetic field generated by an electromagnetic device can provide insights into their operational condition. This paper focuses on diagnosing electrolyzers through magneto-tomography, a method which involves mapping the magnetic field generated by electrical devices. Since current density and magnetic field are directly linked, this study can characterize the normal and abnormal operation of an electrolyzer. To achieve this, a two-dimensional (2-D) Biot–Savart model in polar coordinates is developed. To validate this model for electrolyzer diagnostics, an experimental setup was established to replicate the electrolyzer operation. The purely analytical model was successfully validated, with the average difference between the analytical results and experimental measurements established. The model validation is performed by comparing the experimental data to the theoretical results, and it is observed that the difference does not exceed 5%.

Shunting locomotives exhibit a wide and unpredictable range of power profiles. This unpredictability makes it impossible to rely on offline optimizations or predictive methods combined with online optimization. To maintain optimal performance across this broad range of operating conditions, the online control strategy must be robust. This article proposes a robust method to determine the optimal parameter combinations for an online energy management strategy of a hybrid fuel cell battery shunting locomotive, ensuring optimality across all scenario conditions. The first step involves extracting a statistically representative subspace for simulation, both in terms of parameter combinations and scenario conditions. A response surface model (numerical twin) is then constructed to extrapolate results across the entire space based on this simulated subspace. Using this model, the optimal solution is identified through metaheuristic algorithms (minimization search). Finally, the proposed solution is validated against a set of expert-defined scenarios. The result of the methodology ensures robust optimization across an infinite number of scenarios by minimizing the impact on both the fuel cell and the battery, without increasing mission costs.

Aircraft design is an ever-expanding field of research. Disruptive aircraft architectures and the long-standing need for fast design processes are the main drivers behind the domain growth. Novel concepts like distributed propulsion, Vertical Take-Off and Landing, electrification, hybridization, etc., require new models and design strategies to achieve a significant degree of fidelity at every stage of the design. This paper proposes a framework targeting key techniques and assumptions to improve the accuracy of the preliminary aircraft design stage. Based on a review of modern design strategies, a model-based method has been developed. Two distinct approaches to component modeling have been compared for a hybrid-electric distributed propulsion aircraft. To complement this comparative study, the second modeling approach has been tested for three different hybrid electric architectures. The results showcase the feasibility of the three architectures, with promising results for the hydrogen powertrain system.

Proton Exchange Membrane Fuel Cells (PEMFCs) offer a compelling solution for heavy-duty and light-duty vehicle electrification due to their high efficiency and zero-emission operation. Despite these advantages, limited operational durability remains a critical barrier to widespread commercial adoption. In automotive applications, frequent power transients and Stop–Start events create aggressive electrochemical environments that accelerate stack aging. This review provides a systematic analysis of the fundamental degradation mechanisms occurring during these critical transitions. We focus on coupled degradation processes within the Membrane Electrode Assembly (MEA), detailing platinum catalyst dissolution, carbon support corrosion, and membrane thinning. By bridging fundamental interfacial science with realistic automotive duty cycles, this work identifies current research gaps and evaluates the technical feasibility of existing mitigation strategies. Ultimately, this review provides a comprehensive technical overview that can help guide efforts to extend PEMFC lifetime and support the transition to fuel-cell-based mobility.