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Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To develop such models, a comprehensive study is required, encompassing both well-established and the latest governing laws on matter transport and voltage polarisation for Proton Exchange Membrane Fuel Cells (PEMFCs). Recent articles often rely on outdated or inappropriate equations, lacking clear explanations regarding their background. Indeed, inconsistent understanding of theoretical and experimental choices or model requirements hinders comprehension and contributes to the misuse of these equations. Additionally, specific researches are needed to construct more accurate models. This study aims to offer a comprehensive understanding of the current state-of-the-art in PEMFC modeling. It clarifies the corresponding governing equations, their usage conditions, and assumptions, thus serving as a foundation for future developments. The presented laws and equations are applicable in most multi-dimensional, dynamic, and two-phase PEMFC models., Comment: Journal of The Electrochemical Society, 2024

The urgency of the energy transition requires improving the performance and longevity of hydrogen technologies. AlphaPEM is a dynamic one-dimensional (1D) physics-based PEM fuel cell system simulator, programmed in Python and experimentally validated. It offers a good balance between accuracy and execution speed. The modular architecture allows for addition of new features, and it has a user-friendly graphical interface. An automatic calibration method is proposed to match the model to the studied machine. The software provides information on the internal states of the system in response to any current density and can produce polarization and EIS curves. AlphaPEM facilitates the use of a model in embedded conditions, allowing real-time modification of the fuel cell's operating conditions.

A one-dimensional, dynamic, two-phase, isothermal and finite-difference model of proton exchange membrane fuel cell (PEMFC) systems has been developed. It is distinct from most existing models which are either fast but imprecise, such as lumped-parameter models, or detailed but computationally intensive, such as computational fluid dynamics models. This model, partially validated using experimental polarisation curves, provides a comprehensive description of cell internal states while maintaining a low computational burden. Additionally, a new physical quantity, named the limit liquid water saturation coefficient ($s_{lim}$), is introduced in the overvoltage calculation equation. This quantity replaces the limit current density coefficient ($i_{lim}$) and establishes a connection between the voltage drop at high current densities, the amount of liquid water present in the catalyst layers of the cell, and the operating conditions. At high current densities, a significant amount of liquid water is generated, which limits the accessibility of reactants to certain triple point zones within the catalyst layers by covering them. This, in turn, increases overpotential. It has also been observed that $s_{lim}$ is influenced, at minimum, by the gas pressure imposed by the operator.

To face the dependency on fossil fuels and limit carbon emissions, fuel cells are a very promising technology and appear to be a key candidate to tackle the increase of the energy demand and promote the energy transition. To meet future needs for both transport and stationary applications, the time to market of fuel cell stacks must be drastically reduced. Here, a new concept to shorten their development time by introducing a disruptive and highefficiency data augmentation approach based on artificial intelligence is presented. Our results allow reducing the testing time before introducing a product on the market from a thousand to a few hours. The innovative concept proposed here can support engineering and research tasks during the fuel cell development process to achieve decreased development costs alongside a reduced time to market.

This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extracting features from both real and imaginary parts of the impedance spectrum. A parametric model is considered in the case of the real part, whereas regression model with latent variables is used in the latter case. Then, a linear regression model using different subsets of extracted features is used fo r the estimation of fuel cell time duration. The performances of the proposed approach are evaluated on experimental data set to show its feasibility. This could lead to interesting perspectives for predictive maintenance policy of fuel cell., Comment: In Proceeding of the 8th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'09), pages 632-637, 2009, Miami Beach, FL, USA