Utilizing Neural Networks for Optimizing the Performance of Solid Oxide Cell Stacks
Authors: Babaie Rizvandi Omid , Braun Robert 
Published: December 2024

Solid oxide cells (SOCs) are one of the promising energy conversion devices due to their high efficiency and relevance to many commercial and industrial applications. Much development progress has been made in the last 10-years improving the scale-up, degradation, and commercialization outlook globally. Further commercialization of the SOCs necessitates a deeper understanding of their complex internal processes. Experimental methods, albeit valuable, are constrained by cost and time and often cannot capture intricate details like distributions of species, current density, and temperature. Therefore, numerical analyses are essential as they offer a deeper understanding of the complex phenomena within SOCs.
Yet, high-fidelity modeling SOCs is computationally expensive as it includes a multitude of physical and chemical phenomena spanning various layers and length scales. Even at the single-cell and repeating unit levels, 3D simulations are computationally costly due to the intricate interplay of mass, momentum, species transport, charges, and heat. Cell-level models, while useful, cannot capture all of the thermophysical behavior of SOC stacks accurately, particularly along the height of the stack. Full-stack models, while more comprehensive, come with significant computational costs. A breakthrough in addressing this challenge comes through a multi-scale modeling approach, leveraging homogenization techniques to transform the layered complexity of SOC stacks into computationally tractable representations [1,2]. By replacing the layered domains of the stacks with equivalent mediums and calculating effective modeling variables, this approach makes the stack-scale simulations feasible on standard computational computers/workstations.
Even with homogenized models, the optimization at the stack scale is not feasible due to prolonged run times and high computational demands. To address this challenge, this study employs parametric studies on stack-scale models to train a neural network. The trained neural network correlates stack outputs such as voltage, outflow temperatures, and fuel utilization with operational parameters like load current, inlet temperatures, flow rates, and inlet fuel compositions. The neural network is utilized to optimize the stack performance under various conditions while ensuring adherence to manufacturer-defined limits. For instance, the neural network can be used to optimize stack output power under fuel cell mode while avoiding overheating or exceeding maximum current densities. In essence, this work combines the detailed physics of multi-scale stack models with the efficiency of neural networks, paving the way for faster and more comprehensive optimization of SOC stacks.

References

[1]

Navasa, M., Miao, X. Y., & Frandsen, H. L. (2019). A fully-homogenized multiphysics model for a reversible solid oxide cell stack. International Journal of Hydrogen Energy, 44(41), 23330-23347.

[2]

Rizvandi, O. B., Miao, X. Y., & Frandsen, H. L. (2021). Multi-scale modeling of degradation of full solid oxide fuel cell stacks. International Journal of Hydrogen Energy, 46, 27709-27730.