Solid Oxide Fuel Cells (SOFCs) represent a promising technology in the field of electric power generation, particularly suited for alternative fuels and large-scale applications. With the marine industry targeting a full decarbonization by year 2050, there is a significant effort towards the adoption of SOFCs in cargo ships and other vessels. However, their effective adoption in marine transportation requires improved reliability, especially regarding cell degradation, which directly affects their functional lifetime. This paper proposes a novel Degradation-Conscious control strategy for SOFCs, integrating direct control of cell voltage degradation. First, we propose a novel state space model comprising a reduced order model of SOFC dynamics and the voltage degradation model of the cell. Second, we develop the Degradation-Conscious controller using a nonlinear Model Predictive Controller, which integrates degradation management into standard SOFC dynamical control. Simulation results demonstrate the proposed strategy’s ability to reduce degradation while meeting dynamical performance requirements.

Solid Oxide Fuel Cells are promising power generation technologies, especially for large-scale applications. As the marine industry is targeting a full de-carbonization by year 2050, increasing attention is being directed toward the implementation of these technologies. Solid Oxide Fuel Cells are complex systems where thermodynamics and electrochemical reactions are coupled, resulting in highly non-linear dynamics, tight operational constraints, and multiple distributed sensors. Those quantities that cannot be directly measured, need to be estimated. Among these, the so called Area Specific Resistance is an indicator of cell's health condition, related to the cell degradation. This paper proposes a Moving Horizon Estimator based on an extended state-space model of a methane-fueled Solid Oxide Fuel Cell, to estimate in real time the Area Specific Resistance of the cell. Using the estimated value, along with its maximum and average rates, a predictive framework is developed to estimate the Remaining Useful Life of the cell. Simulations are used to illustrate the application and the efficiency of the proposed method.