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Prognostic is a potential tool for improving the durability of solid oxide fuel cells (SOFCs), which usually involves building a degradation model for prediction. However, the existing degradation models based on parallel constant operation datasets are inaccurate for integration with operation optimization and control problems of SOFCs under varying-load operation due to the nonuniform degradation behaviors. To address this issue, a link function degradation model is proposed, and its parameters are identified online with a cyclic batch identification procedure based on the maximum likelihood method, which provides results representing the degradation trend on a timescale of 103 h. The link function takes the form of an empirical function, which describes how operating parameters affect the degradation and is easy to integrate with control designs. The existence of the link function is proven on the varying-load experiment datasets of two flat-chip SOFCs because it statistically improves the prediction accuracy and stability compared with a constant degradation speed model. Furthermore, the effectiveness of the proposed identification procedure for time-varying degradation behaviors on the timescale of 104 h is also validated with 30,000-h simulation datasets. • Accurate degradation models are built, considering degradation nonuniformity. • The model form is preferred by operation control/optimization applications. • 1700-h varying-load degradation experiment datasets of two cells are obtained. • The existence of the link function is proved on the experiment datasets. • The modeling procedure adapts to time-varying degradation characteristics. [ABSTRACT FROM AUTHOR]

Alkaline electrolysis (AEL) is the most mature electrolysis technology and is widely used in large-scale power-to-gas scenarios. However, the loading range of AEL system is restricted by the minimum load, 10%–40% in general, which prevents system operation during low-load periods. This minimum load is caused by the flammable mixture formed by the impurity crossover and is related to operation conditions such as current and pressure. In this paper, we propose a novel pressure control strategy to extend the loading range of an AEL system by reducing the system pressure at low-load periods while also maximizing the system efficiency. A dynamic impurity accumulation model is established illustrating the flexibility limitation mechanism of the AEL system. Two pressure controllers are designed based on the steady-state and dynamic impurity accumulation model, including an operation curve tracking controller (OCT) and a model predictive controller (MPC). In the case study, both controllers achieve satisfying results under the peak shaving scenario, with an extension of the AEL system's minimum load from 27.5% by the traditional constant pressure controller to 10.5% by the OCT controller and 10% by the MPC controller. This pressure control strategy reduces the electricity abandonment and improves the system economy significantly. • Establish a dynamic impurity accumulation model for gas crossover. • Propose a novel pressure control strategy to extend the loading range. • Design two pressure controllers: an OCT controller and an MPC controller. • Improve the system economy by reducing the electricity abandonment. [ABSTRACT FROM AUTHOR]