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Parameter optimization of solid oxide fuel cell parameters using quasi-affine transformation evolution with evolution matrix and selection operation algorithm
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AI
DOI：10.1007/s11581-025-06767-3.png)
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Abstract
En 中文
The accurate estimation of parameters in solid oxide fuel cells (SOFCs) is critical for improving their efficiency and performance. Existing optimization techniques often struggle with high-dimensional, nonlinear parameter spaces, leading to suboptimal results. This paper proposes the Quasi-Affine Transformation Evolution with Evolution Matrix and Selection operation (QUATRE-EMS) algorithm, an enhanced version of the QUATRE algorithm, integrating an evolution matrix and a novel selection strategy, to address this challenge. The QUATRE-EMS algorithm was tested against nine other metaheuristic algorithms in the context of SOFC parameter optimization, using a dynamic tubular SOFC model under varying thermal (1073 to 1273 K) and pressure (1 to 9 atm) conditions. The results demonstrate that QUATRE-EMS consistently outperforms the other algorithms in terms of mean squared error (MSE), computational time, and stability across multiple runs. The improved performance of QUATRE-EMS can be attributed to its enhanced search capabilities and efficient handling of the nonlinearities inherent in SOFC modeling. These findings have significant implications for the optimization of SOFC systems in practical applications, offering a more reliable and computationally efficient solution for parameter estimation. The improved performance is. attributed to the algorithm's robust search capabilities and efficient handling of complex nonlinearities. These findings indicate that QUATRE-EMS offers a more reliable and efficient solution for SOFC parameter estimation, with significant implications for the optimization of energy systems. Further exploration is warranted to adapt the algorithm for dynamic system modeling and real-time applications.
Keywords:
Solid oxide fuel cells (SOFC)
Parameter estimation
Metaheuristic algorithms
Fuel cell modeling
Energy systems
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2.6
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