Theoretical proposition of control and data acquisition system for a test stand for single-cell testing of pem hydrogen fuel cells
Mikołaj Klekowicki
a:1:{s:5:"en_US";s:32:"Poznań University of Technology";}Filip Szwajca
Poznań University of TechnologyGrzegorz M. Szymański
Poznań University of TechnologyKlaudia Strugarek
Poznań University of TechnologyAleksander Ludwiczak
Poznań University of TechnologyAbstract
Accurate, traceable characterisation of proton-exchange membrane (PEM) fuel cells at the
single-cell level is pivotal for material screening, degradation studies and control-algorithm
development. However, commercial diagnostic benches typically cost €20,000-150,000, limiting access for many research and teaching laboratories. This paper introduces a fully open-hardware, modular test stand that delivers 0.1 mV voltage resolution and a 0-50 A current envelope for a bill of materials of only €14,000. The architecture is split into a measurement & regulation layer built around temperature-controlled shunts and a 12-bit delta-sigma ADC, a control & SCADA layer based on an ESP32-S3 micro-controller and CompactDAQ interface, and a hydrogen-supply layer equipped with SIL-2 safety instrumentation. A rigorously quantified Type-A/Type-B uncertainty budget, prepared in accordance with ISO/IEC Guide 98-3 and validated via a 10,000-run Monte-Carlo simulation, yields an expanded cell-voltage uncertainty of ±0.38 % (k = 2). A built-in
real-time digital twin couples an equivalent-circuit model with reduced-order CFD to enable
what-if analyses and predictive maintenance. Comparative benchmarking against the AVL E-Load 2 and ZSW single-cell rigs shows equal or better metrological performance at ≤ 25% of their cost. A proof-of-
-concept dynamic-load experiment confirms the stand’s fidelity, establishing a low-cost pathway towards scalable, open and safe PEM fuel-cell diagnostics.
Keywords:
hydrogen, PEM, fuel cell, digital twin, ESP32References
E-Load 2 Product Brochure. 2024. AVL List GmbH, Graz. Retrieved from https://www.avl.com/e-load-2 (2.06.2025). Google Scholar
Fuel Cell Testing protocols: best practices for single-cell characterisation. 2023. US DOE. U.S. Department of Energy, Washington, DC. Retrieved from https://www.energy.gov/eere/fuelcells (2.06.2025). Google Scholar
Guide to the Expression of Uncertainty in Measurement (ISO/IEC Guide 98-3). 2008. Geneva: ISO/IEC. Retrieved from https://www.bipm.org/en/publications/guides/gum (2.06.2025). Google Scholar
Idea of digital twin in hydrogen test benches. 2025. Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (ZSW). Retrieved from https://www.zsw-bw.de/en/research/fuel-cells/topics/fuel-cell-tests-and-test-benches.html (19.04.2025). Google Scholar
IEC 60079-29-1. 2015. Explosive Atmospheres. Part 29-1: Gas Detectors – Performance Requirements of Detectors for Flammable Gases. International Electrotechnical Commission, Geneva. Retrieved from https://webstore.iec.ch/publication/20412 (2.06.2025). Google Scholar
IEC 61508. 2010. Functional safety of electrical/electronic/programmable electronic safety-related systems. International Electrotechnical Commission, Geneva. Retrieved from https://webstore.iec.ch/publication/554 (2.06.2025). Google Scholar
IEC 62282-2. 2012. Fuel cell technologies. Part 2: Fuel cell modules. International Electrotechnical Commission, Geneva. Retrieved from https://webstore.iec.ch/publication/606 (2.06.2025). Google Scholar
ISO TR 15916. 2023. Basic Considerations for the Safety of Hydrogen Systems. International Organization for Standardization, Geneva. Retrieved from https://www.iso.org/standard/75623.html (2.06.2025). Google Scholar
Khan S., Alzaabi A., Ratnarajah T., Arslan T. 2024. Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model. Computers in Biology and Medicine, 168: 107825. https://doi.org/10.1016/J.COMPBIOMED.2023.107825 Google Scholar
Li C., Liu Y., Xu B., Ma Z. 2019. Finite time thermodynamic optimization of an irreversible proton exchange membrane fuel cell for vehicle use. Processes, 7(7). https://doi.org/10.3390/pr7070419 Google Scholar
Li X., Qi Y., Li S., Tunestål P., Andersson M. 2021. A multi-input and single-output voltage control for a polymer electrolyte fuel cell system using model predictive control method. International Journal of Energy Research, 45(9): 12854-12863. https://doi.org/10.1002/ER.6616 Google Scholar
Li X., Wang Z., Jiang S., Chen L., Xu H. 2019. Low-cost measurement strategies for single-cell PEM fuel cells. International Journal of Hydrogen Energy, 44(42): 23456-23470. https://doi.org/10.1016/j.ijhydene.2019.12.001 Google Scholar
Muhida R., Riza M., Harsoyo A., Murwadi H., Legowo A. 2025. Utilization of IoT for measuring hydrogen production in a photovoltaic-solid polymer electrolyte (PV-SPE) System. Journal of Applied Science and Advanced Engineering, 3(1): 37-41. https://doi.org/10.59097/jasae.v3i1.55 Google Scholar
Nexa Fuel Cell Data Sheet v.3.1. 2024. Ballard Power Systems, Burnaby. Retrieved from https://www.ballard.com/files/downloads/nexa_datasheet_v3_1.pdf (2.06.2025). Google Scholar
Pourkiaei S.M., Ahmadi M.H., Hasheminejad S.M. 2016. Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network. Mechanics and Industry, 17(1). https://doi.org/10.1051/MECA/2015050 Google Scholar
Single-Cell Test-Rig Datasheet. 2024. Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg, Stuttgart. Retrieved from https://www.zsw-bw.de/en/media/e-lab (2.06.2025). Google Scholar
Tao F., Cheng J., Qi Q., Zhang M., Zhang H., Sui F. 2018. Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9-12): 3563-3576. https://doi.org/10.1007/S00170-017-0233-1 Google Scholar
Tao F., Zhang H., Liu A., Nee A.Y.C. 2019. Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4): 2405-2415. https://doi.org/10.1109/TII.2018.2873186 Google Scholar
Tellez-Cruz M.M., Escorihuela J., Solorza-Feria O., Compañ V. 2021. Proton exchange membrane fuel cells (Pemfcs): Advances and challenges. Polymers. MDPI. https://doi.org/10.3390/polym13183064 Google Scholar
Test Rigs. 2025. EBZ Entwicklungs- und Vertriebsgesellschaft Brennstoffzelle mbH. Retrieved from www.ebz-dresden.de/fuel-cells/test-rigs (23.04.2025). Google Scholar
Wilberforce T., Biswas M. 2022. A study into proton exchange membrane fuel cell power and voltage prediction using artificial neural network. Energy Reports, 8: 12843-12852. https://doi.org/10.1016/J.EGYR.2022.09.104 Google Scholar
Wilberforce T., Olabi A.G. 2020. Fuel cell applications: a perspective. Renewable and Sustainable Energy Reviews, 122: 109620. https://doi.org/10.1016/j.rser.2019.109620 Google Scholar
Wilberforce T., Olabi A.G. 2020. Performance prediction of proton exchange membrane fuel cells (PEMFC) using adaptive neuro inference system (ANFIS). Sustainability (Switzerland), 12(12). https://doi.org/10.3390/SU12124952 Google Scholar
Yang B., Li D., Zeng C., Chen Y., Guo Z., Wang J., Shu H., Yu T., Zhu J. 2021. Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms. Energy, 228. https://doi.org/10.1016/j.energy.2021.120592 Google Scholar
Yang B., Wang J., Yu L., Shu H., Yu T., Zhang X., Yao W., Sun L. 2020. A critical survey on proton exchange membrane fuel cell parameter estimation using meta-heuristic algorithms. Journal of Cleaner Production, 265. https://doi.org/10.1016/j.jclepro.2020.121660 Google Scholar
Ziegler F., Petersen J., Müller S., Hartmann L. 2022. Cost analysis of PEM fuel-cell diagnostics. International Journal of Hydrogen Energy, 47(28): 15123-15134. https://doi.org/10.1016/j.ijhydene.2021.11.040 Google Scholar
a:1:{s:5:"en_US";s:32:"Poznań University of Technology";}
Poznań University of Technology
Poznań University of Technology
Poznań University of Technology
Poznań University of Technology

