A Surrogate Model of the Butler-Volmer Equation for the Prediction of Thermodynamic Losses of Solid Oxide Fuel Cell Electrode

Szymon Buchaniec, Marek Gnatowski, Hiroshi Hasegawa and Grzegorz Brus ()
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Szymon Buchaniec: Department of Fundamental Research in Energy Engineering, AGH University of Krakow, 30-059 Krakow, Poland
Marek Gnatowski: Department of Fundamental Research in Energy Engineering, AGH University of Krakow, 30-059 Krakow, Poland
Hiroshi Hasegawa: Department of Machinery and Control Systems, Shibaura Institute of Technology, Tokyo 135-8548, Japan
Grzegorz Brus: Department of Fundamental Research in Energy Engineering, AGH University of Krakow, 30-059 Krakow, Poland

Energies, 2023, vol. 16, issue 15, 1-12

Abstract: Solid oxide fuel cells are becoming increasingly important in various applications, from households to large-scale power plants. However, these electrochemical energy conversion devices have complex behavior that is difficult to understand and optimize. A numerical simulation is a primary tool for analysis and optimization-design. One of the most significant challenges in this field is improving microscale transport phenomena and electrode reaction models. Two main categories of simulation are black-box and white-box models. The former requires large experimental datasets and lacks physical constraints, while the latter inherits the inaccuracy of typical electrochemical reaction models. Here we show a micro-scale artificial neural network-supported numerical simulation that allows for overcoming those issues. In our research, we substituted one equation in the system, an electrochemical model, with an artificial neural network prediction. The data-driven prediction is constrained and must satisfy all reminded balance equations in the system. The results show that the proposed model can simulate an anode-electrode’s thermodynamic losses with improved accuracy compared with the classical approach. The coefficient of determination R 2 for the proposed model was equal to 0.8810 for 800 °C, 0.8720 for 900 °C, and 0.8436 for 1000 °C. The findings open a way for improving the accuracy and computational complexity of electrochemical models in solid oxide fuel cell simulations.

Keywords: solid oxide fuel cell; grey-box models; artificial neural network; mathematical modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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