Ranking of novel criteria for prioritising production-relevant quality-critical product parameters of electrolysers and fuel cells

One promising approach to accelerate the adoption of fuel cells and electrolysers is the use of Digital Concepts (DCs), such as Digital Twins (DTs) which simulate with quality-critical product parameters in manufacturing. Despite potential benefits, the application of mature DCs for optimising processes and products remains limited. One key reason for this is the unfamiliarity regarding the connection between quality-critical parameters and DCs. To address this gap, a multi-criteria decision-making framework is proposed, which employs novel criteria conceived for prioritising quality-critical parameters for simulation applications, referred to as Simulation Decision Matrix. Criteria are weighted using the analytical hierarchy process in expert interviews, based on a novel use case. The interviews revealed that a profound understanding of differential equations, formulas, and physical parameters of the system to be modelled is essential for developing a DC. Additionally, knowledge of model assumptions and simulation limits is crucial for the proper application of a DC. The integration of quality-critical parameters into the model requires finesse and was highly rated by the experts. Furthermore, validation is paramount, as the robustness of the DC and its seamless integration into the production environment are key for their successful application. The experts showed sensitivity to the use case, ranking the relevant criteria accordingly. Their feedback underscores both the relevance and complexity of this topic, positioning this work as a valuable starting point for further exploration.