Optimizing the homogeneity and efficiency of a solid oxide electrolysis cell based on multiphysics simulation and data-driven surrogate model

Inhomogeneous current and temperature distributions are harmful to the durability of solid oxide electrolysis cells (SOECs). A high steam utilization is favorable for system efficiency, but also enhances the inhomogeneity. This study combines segmented SOEC experiments, multiphysics simulation, and neural network to optimize the inhomogeneity and efficiency jointly. A three-dimensional (3D) cell model is built and experimental validation shows that the model correctly predicts the decreased down-stream current after the steam utilization exceeds 0.8. Fast surrogate models are trained with the simulation data and integrated into a multi-objective optimization problem for numerical solution. Its solutions form a Pareto front quantifying the conflicting relationship between the steam utilization, inhomogeneity, voltage, hydrogen production and working temperature, from which optimal solutions are chosen to achieve a trade-off. Under a power density of 1.11W cm−2, the ratio between the down-stream and up-stream currents drops from 63.1% to 55.2% when the steam utilization increases from 0.72 to 0.82. The Pareto fronts can enhance the collaboration between stack manufacturers and system operators by enabling the latter to optimize the operating point for a balance between system efficiency and inhomogeneity.