Authors: Catalina A. Pino-Muñoz | Nigel P. Brandon | Aayan Banerjee
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Generalised Neural Network Surrogate Model for Electrolyte Supported Solid Oxide Fuel Cells and Electrolysers Limiting global warming to 1.5°C above pre-industrial levels by 2050 is imperative to stop the impact of climate change. Urgent global actions are needed to transition toward a net-zero and hydrogen-fueled economy based on renewable energy. Solid oxide cells (SOCs) are a promising technology to help advance this transition by balancing the intermittency of renewable energy sources via energy storage. They comprise of a dense ceramic electrolyte between two porous electrodes and convert chemicals (e.g., hydrogen) into electricity when running in fuel cell mode. While the reverse process occurs when running in electrolyser mode. Operating at high-temperature (e.g., 600-900°C), SOCs display higher efficiencies than their low-temperature counterparts and can also utilize carbon dioxide to form synthesis gas. However, a major drawback limiting their widespread commercialisation is the tpyically shorter lifetime due to material degradation. Thus, characterizing and understanding degradation and the respective performance loss is essential to improving lifetime. Accelerated methods including computational modelling can play a significant role by supporting the limited and very time-consuming data collection (e.g., 5-10 years). Faster evaluation models capable of simulating electrochemical and transport processes in reactive flows from Positive electrode-Electrolyte-Negative electrode (PEN) assemblies up to full SOC stacks are critically needed. The computational efficiency gained by such models could enable the integration of lower-dimensional physics (e.g. meso-scale structural and chemical degradation) into lifetime simulation of SOC stack under realistic duty cycles. Previous studies have successfully simulated stack performance using multi-scale modeling approaches, where physics-based descriptions are applied hierarchically at the PEN, cell, and stack levels [1-3]. While this method provides detailed spatial distributions of key variables (e.g., temperature, gas composition, and reaction rates), its high computational cost (several hours per simulation) limits its practicality for lifetime studies. Here, we present a generalised neural network surrogate model for the rapid simulation of electrochemical surface reactions and mass and charge transport across PEN assemblies. The surrogate models replace traditional physics-based one-dimensional steady-state models, enabling fast and accurate performance predictions while significantly reducing computational cost (&lt; seconds) when scaling high-resolution simulations to the stack level. We demonstrate this approach for an electrolyte-supported cell architecture. The key aspects of our approach (Figure 1) include: (1) Developing generalised neural network models for electrolyte-supported cells, where geometrical, operational, gas, and material parameters can all vary; (2) Applying dimensional and nonlinear analysis to identify dominant physicochemical processes, reducing the input parameter space; and (3) Transforming inputs and outputs to enhance uniformity and improve model performance. Our approach enables the generation of accurate neural network surrogate models for a wide range of cell materials, geometries, and operating conditions. The models are validated against multiple experimental datasets of SOCs operating in both fuel cell and electrolyser modes and benchmarked against physics-based numerical solutions. Existing data-driven models for PEN assemblies [4]-[5], have inputs that only consider cell operating conditions, thus limiting their applicability. Our approach overcomes these limitations by developing a generalised surrogate model to simulate a wide design space ( i.e. , including a range of material, microstructure, layer thickness, among others, in addition to operating conditions) via a novel treatment of the inputs. This allows for a reduced number of inputs to be used, and therefore a reduced data set and training time, while also boosting training accuracy. Feature engineering is used to identify key characteristic numbers that govern transport and reaction phenomena while accounting for model nonlinearities. We present a comprehensive analysis of the design space in terms of these reduced characteristic inputs, operating conditions, and relevant output metrics (e.g., PEN overpotential) for a wide range of possible electrolyte-supported PEN assemblies. As part of our future work, we plan to integrate our neural network surrogate model of PEN assemblies with a one-dimensional thermofluidic model that captures axial heat and mass transport along the gas channels surrounding the PEN. Additionally, we aim to incorporate a three-dimensional heat transport model of the kW-scale stack, with and without insulation, to enable fast evaluation of stack performance under realistic duty cycles while preserving spatial resolution. Caption Figure 1: Modeling workflow for training a neural network surrogate model for electrolyte-supported fuel cells and electrolysers. References: [1] L. Wehrle et al., Chemie Ing. Tech., vol. 91, no. 6, pp. 833–842, Jun. 2019, doi: 10.1002/CITE.201800188. [2] Y. Wang et al., EnergyConvers. Manag., vol. 196, 2019, doi: 10.1016/j.enconman.2019.05.099. [3] A. Banerjee, Chemie Ing. Tech, vol. 94, no. 6, pp.766-773, Mar. 2022, doi: 10.1002/cite.202100199 [4] H. Xu et al., Energy AI, vol. 1, p. 100003, Aug. 2020, doi: 10.1016/J.EGYAI.2020.100003. [5] M. Kishimoto et al., Appl. Energy, vol. 283, p. 116305, Feb. 2021, doi: 10.1016/J.APENERGY.2020.116305. Figure 1 Stack (abstract data type) ; Renewable energy ; Solid oxide fuel cell ; Electrolyte ; Oxide ; Multiscale modeling ; Electricity generation ; Ceramic ; Process (computing) Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Model Reduction and Neural Networks