Fuel cells (FCs) are promising eco-friendly power sources. Nevertheless, there are challenges to overcome if they are to be widely deployed in areas such as degradation avoidance and control, where the knowledge of the unavailable concentrations is crucial. In this respect, observers can provide unavailable quantities based on an estimation algorithm and available measurements. This paper presents an FC concentration observer design workflow, covering the model-based design of experiments (DOE), their execution, systematic nonlinear identification, and measurement-based validation. The model-based DOE and the validation with a mass spectrometer, including dynamic operation, are unique for PEMFC observers. The workflow is demonstrated with a constrained extended Kalman filter observer on a $\mathrm {30 \text {k} \text {W} }$ polymer electrolyte membrane FC (PEMFC) test stand. A control-oriented model serves as the workflow basis, and the DOE is based on optimizing the parameter sensitivity. The test stand delivers the measurements, the parametrization comprises a sensitivity analysis, and the experimentally validated observer yields outstanding concentration estimation performance.

This paper proposes a new efficient two-step method for parametrizing control-oriented zero-dimensional physical polymer electrolyte membrane fuel cell (PEMFC) models with measured stack data. Parametrizations of these models are computationally intensive due to the numerous unknown parameters and the typically nonlinear, stiff model properties. This work reduces an existing model to decrease its stiffness for accelerated numerical simulations. Subdividing the parametrization into two consecutive subproblems (thermodynamic and electrochemical ones) reduces the solution space significantly. A parameter sensitivity analysis further reduces each sub-solution space by excluding non-significant parameters. The method results in an efficient parametrization process. The two-step approach minimizes each sub-solution space’s dimension by two-thirds, respectively three-fourths, compared to the global one. An achieved $R^2$ value between simulation and measurement of 91% on average provides the required accuracy for control-oriented models.

Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency.

Polymer electrolyte membrane fuel cells (PEMFCs) supplied with green hydrogen from renewable sources are a promising technology for carbon dioxide-free energy conversion. Many mathematical models to describe and understand the internal processes have been developed to design more powerful and efficient PEMFCs. Parameterizing such models is challenging, but indispensable to predict the species transport and electrochemical conversion accurately. Many material parameters are unknown, or the measurement methods required to determine their values are expensive, time-consuming, and destructive. This work shows the parameterization of a quasi-3D PEMFC model using measurements from a stack test stand and numerical optimization algorithms. Differential evolution and the Nelder–Mead simplex algorithm were used to optimize eight material parameters of the membrane, cathode catalyst layer (CCL), and gas diffusion layer (GDL). Measurements with different operating temperatures and gas inlet pressures were available for optimization and validation. Due to the low operating temperature of the stack, special attention was paid to the temperature dependent terms in the governing equations. Simulations with optimized parameters predicted the steady-state and transient behavior of the stack well. Therefore, valuable data for the characterization of the membrane, the CCL and GDL was created that can be used for more detailed CFD simulations in the future.

Switch-point heating systems are essential for railway reliability and safety in winter, but present logistical and economic challenges in remote regions. This study presents a novel application of a hydrogen-enabled microgrid as an off-grid energy solution for powering a switch-point heating system at a rural Austrian railway station, offering an alternative to conventional grid-based electricity with a specific focus on enhancing the share of renewable energy sources. The proposed system integrates photovoltaics (PV), optional wind energy, and hydrogen storage to address the seasonal mismatch between a high energy supply in the summer and peak winter demand. Three energy supply scenarios are analysed and compared based on local conditions, technical simplicity, and economic viability. Energy flow modelling based on site-specific climate and operational data is used to determine hydrogen production rates, storage capacity requirements and system sizing. A comprehensive cost analysis of all major subsystems is conducted to assess economic viability. The study demonstrates that hydrogen is a highly effective solution for seasonal energy storage, with a PV-only configuration emerging as the most suitable option under current site conditions. Thus, it offers a replicable framework for decarbonising critical stationary railway infrastructure.