OPTIMISATION OF SOC CELLS: PATTERNS AND PREDICTIONS OF EXPERIMENTAL DATA

The global energy landscape is undergoing a significant transformation as nations strive to meet sustainability goals and reduce reliance on fossil fuels. A central player in this transition is Solid Oxide Cells (SOCs) technology, which is redefining energy conversion and storage by functioning as dual-purpose devices: generating electricity in fuel cell mode and producing hydrogen or synthetic fuels in electrolysis mode. These highly efficient electrochemical cells can operate using a wide variety of fuel sources, including natural gas, hydrogen, and biogas, making them versatile tools for enhancing modern energy systems.[1] This research investigates the predictive analysis of solid oxide cell (SOC) performance. The preliminary phase involved the compilation of experimental data obtained parametrically under both fuel cell and electrolysis conditions, encompassing both electrolyte-supported and anode-supported cells. This approach guaranteed a sufficiently extensive dataset to substantiate the model's accuracy. Following this, two models were concurrently developed: one deterministic and one stochastic, aimed first at simulating the experimental data and subsequently at predicting the behaviour of short stacks. Both models exhibited congruent predictive behaviours, thereby affirming their validity for the analysis of short stacks.