AlphaPEM: an open-source dynamic 1D physics-based PEM fuel cell model for embedded applications

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 fuel cell. 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.

💡 Research Summary

The paper presents AlphaPEM, an open‑source, Python‑based dynamic one‑dimensional (1D) physics‑based model of a proton exchange membrane fuel cell (PEMFC) designed for embedded applications. AlphaPEM balances computational speed and accuracy by employing a 1D spatial discretization that captures the two‑phase (liquid water and vapor) behavior of the gas diffusion layer, catalyst layer, and membrane under isothermal conditions. The model solves coupled nonlinear ordinary differential equations using SciPy’s solve_ivp with an implicit BDF solver, ensuring numerical stability for stiff problems.

Key features include: (1) real‑time access to internal states such as hydrogen concentration in the catalyst layer and water content in the diffusion layer, which are otherwise inaccessible to sensors; (2) the ability to generate polarization curves, step‑response voltage transients, and electrochemical impedance spectroscopy (EIS) data from arbitrary current profiles; (3) modular architecture with directories for model equations, auxiliary functions, calibration routines, and a graphical user interface (GUI). The GUI allows users to configure operating conditions, select auxiliary system configurations (e.g., forced‑convective cathode with recirculation), and run simulations without writing code, while advanced users can modify the source for custom behavior.

AlphaPEM incorporates an automated calibration module that employs a genetic algorithm (population 100‑200, 1500 generations) to fit undetermined physical parameters (e.g., GDL tortuosity, porosity) to experimental polarization data. Calibration is parallelized across CPU cores; the authors report a calibration error of 1.06 % after two weeks on an 80‑core Xeon cluster.

The software is released under the GNU GPL‑v3 license, encouraging community contributions, code inspection, and collaborative development. It is written entirely in Python, making it portable across Linux, macOS, and Windows platforms. The code follows standard software engineering practices, includes comprehensive documentation, and is structured to facilitate extension (e.g., adding heat transfer models or multi‑cell stack dynamics).

In summary, AlphaPEM fills a gap in the PEMFC modeling landscape by providing an open, fast, and physically detailed tool suitable for model‑based control, diagnosis, and optimization in embedded systems. Its combination of dynamic simulation, real‑time internal state estimation, and automated calibration makes it a valuable resource for researchers and engineers seeking to improve fuel‑cell efficiency, power density, and longevity through advanced control strategies. Future work may extend the model to non‑isothermal operation, multi‑cell stacks, and hardware‑in‑the‑loop testing, further broadening its applicability.