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A solid oxide electrolysis cell (SOEC) stack tower formed by piling several stacks is a general approach to scaling up SOEC due to its simplicity and compactness. Enhanced fluid and thermal inhomogeneity are the main factors limiting the stack tower performance when increasing serial cell number and single-cell area. Multiphysics models widely used in stack-level research can accurately simulate stack tower inhomogeneity. However, their complex forms and high computational costs hinder investigating the impacts of various operating and geometrical parameters, such as current density, serial cell number, and single-cell area. To fill the gap, this study proposes a coupled fluid-thermal-electric equivalent circuit model of an SOEC stack tower, which reduces the computational cost by more than three orders of magnitude with an error of less than 0.8% compared with the 3D multiphysics model. With this model, explicit expressions for inhomogeneity indices and corresponding maximum current density are derived, which quantify the relationship between the maximum current density and the size of a stack tower. A 21.7% and a 13.9% reduction in maximum current density are observed when the serial cell number is increased from 30 to 150, and the single-cell area is reduced from 68 cm 2 to 544 cm 2 , respectively. Moreover, the optimal scale-up path to improve the economy shows that the serial cell number should be preferentially increased when it is lower than 80, after which the single-cell area should be enlarged. However, even if following the optimal path, scaling up the total cell area from 5000 cm 2 to 25000 cm 2 reduces the maximum current density from 0. 75 A ⋅ cm − 2 to 0. 62 A ⋅ cm − 2 , indicating that the contradiction between the maximum current density and the size of a stack tower is inevitable. [Display omitted] • A coupled fluid-thermal-electric equivalent circuit model is proposed. • The resistance terms of the model are identified from a multiphysics model. • The expressions of the maximum current density limited by inhomogeneity are derived. • The inhomogeneity impacts on a stack tower in the scale-up process are investigated. • The optimal scale-up path is revealed to improve the economy. [ABSTRACT FROM AUTHOR]

Solid oxide cells (SOCs) have multiple advantages, including high efficiency and reversibility, but they are still characterized by low maturity and high investment costs. Their cost-effectiveness remains to be evaluated from an investment viewpoint for their ability to compete with mature and low-cost alkaline electrolysis (ALK). To this end, this study considers the application of SOC in the production of green hydrogen and electricity to decarbonize a refinery plant, a potential application scenario favoring the efficient SOC technology due to its requirement for stable operation and lack of inexpensive renewable electricity. A planning model is proposed to optimize the system configuration and minimize the system cost, based on which the acceptable cost threshold enabling SOC to compete with ALK is determined, defined as the unit investment cost of SOC at which substituting ALK with SOC will not increase the system cost. The results show that the high efficiency of SOC reduces the investment in renewable generation to fulfill the hydrogen demand, and its reversibility eliminates additional hydrogen-to-electricity and ammonia-to-electricity investments to realize long-duration energy storage. These factors enable SOC to compete with ALK at an investment cost of 11.6 CNY/W, three times the investment cost of ALK. Moreover, when SOC and ALK are combined, SOC can operate at a capacity factor above 75%, 1.5 times the capacity factor of ALK. This further boosts the threshold to 20.4 CNY/W, revealing that SOC and ALK, instead of competing, can work together to lower the system cost and drive commercialization of the emerging SOC technology. • The acceptable cost threshold enabling SOC to compete with ALK is determined. • A conceptual direct-ammonia RSOC system is proposed and modeled. • Refinery is selected as a potential scenario that highly values SOC's advantages. • Collaboration of ALK and SOC can boost the threshold from 11.4 CNY/W to 20.4 CNY/W. • Critical factors affecting the threshold are identified via sensitivity analysis. [ABSTRACT FROM AUTHOR]

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]

Surrogate models that predict the behaviors of solid oxide cells (SOCs) accurately at low computational cost are crucial to the control and optimization of SOC plants. Lumped physical models of SOCs, while widely used in such applications, lack accuracy because of neglected physical details. Data-driven models are the other options of surrogate models, which are proved to be more accurate because these models are identified directly from experiments or numerical simulations. However, due to the time cost of experiments and numerical simulations of SOCs, it is hoped that data-driven models can be constructed from a minimum amount of data. Also, the trained data-driven models should be robust, in other words, insensitive to the data set as well as the initial settings. These requirements are hard to be achieved by existing data-driven models of SOCs, such as lookup tables and artificial neural networks (ANNs). Aiming to preserve robustness and reduce the required amount of data, this paper introduces an adaptive polynomial approximation (APA) method, which is derived from the latest findings of numerical computation science, to the surrogate modeling of SOCs. The obtained models by the APA method are validated by both experiments and simulations. By analyzing the models, the coupling relationship among operating parameters of SOCs is revealed. The physical interpretability makes the APA method distinctive from common data-driven modeling methods. Performance comparison shows that the APA method is more accurate and robust than the existing ones with similar sampling costs. Additionally, the APA method can control the accuracy of the model by setting an error criterion in the algorithm iteration, endowing the APA method with an error control ability as per different accuracy requirements for SOC modeling. • A data-driven method to construct surrogate models of SOC is introduced. • The models built by this method are validated by both experiments and simulations. • The models built by this method are physically interpretable. • The accuracy of this method is directly controlled by an error control parameter. • With similar time cost, this method is more robust and accurate than existing ones. [ABSTRACT FROM AUTHOR]