The paper focuses on a model-based methodology aimed at developing suitable diagnostics strategies to detect degradation level and faulty operation in solid oxide fuel cell (SOFC) systems. The methodology is based on the “inverse” use of a 1-D SOFC stack model to estimate cell parameters from measured variables. Modeling features allow simulating both co- and counter-flow planar SOFC with a good compromise between accuracy and computational burden, thus enhancing final implementation in a variety of optimization procedures. Main objective is to identify those model parameters that are not directly measurable in the real SOFC system, e.g. electrolyte and electrode Ohmic resistance. The inputs are the real-system measurable variables, such as stack voltage and current, inlet mass flow and temperatures. Once unmeasurable variables are identified, they are compared to corresponding reference values to generate suitable residuals, depending on which SOFC stack faulty conditions can be eventually detected and isolated and the stack degradation state can be estimated. The proposed model-based algorithm is suitable in SOFC stack monitoring and diagnosis, thus offering a high potential tool for improving SOFC system safety and durability for on-field applications.

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