An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10⁻⁶ at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC.

中文翻译：

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基于现代优化的固体氧化物燃料电池最佳参数估计方法

提出了一种利用现代优化技术的固体氧化物燃料电池（SOFC）最优参数估计方法。借助实验结果，平衡优化器（EO）已用于识别SOFC等效电路的不确定参数。这是通过将建模过程公式化为一个优化问题而提出的，该优化问题考虑了观测电压和计算电压之间的总均方误差（SMSE）作为目标。在可变工作条件下，研究了基于SOFC的模型的两种模式，即基于稳态的模型和基于动态状态的模型。将拟议的EO结果与通过阿基米德优化算法（AOA），基于堆的优化器（HBO），海鸥优化算法（SOA），基于学生心理的优化算法（SPBO）获得的结果进行比较，海洋捕食者算法（MPA），蝠ta觅食优化（MRFO）以及全面学习的动态多群海洋捕食者算法。通过提议的EO值为1.5527×10获得稳态模型下的最小适应度函数在1173 K时为^-6。在基于动态的模型中，最小SMSE为1.0406。获得的结果证实了所提出的EO在构建SOFC的可靠模型中的可靠性和优越性。