SOFC stack modeling: a hybrid RBF-ANN and flexible Al-Biruni Earth radius optimization approach

Abstract

Abstract This study introduces a novel hybrid methodology for model identification of solid oxide fuel cell (SOFC) stacks by integrating a radial basis function-based artificial neural network (RBF-ANN) with a flexible Al-Biruni Earth radius optimizer (FA-BERO). The primary objective of the proposed method is to augment the precision and efficiency of SOFC stack modeling by considering the advantages of both RBF-ANN and FA-BERO algorithms. The main purpose of using these two methods is to optimize the structure of the RBF-ANN based on the suggested FA-BERO algorithm. The other contribution of this study is improving the efficiency of the Al-Biruni Earth radius optimizer (A-BERO) by applying two improvements on it, including constriction factor and elimination phase to increase the exploration and exploitation strength of the basic A-BERO. To validate the effectiveness of the proposed model, it is compared with some state-of-the-art models in the field, such as the artificial neural network and multi-armed bandit algorithm (ANN/MABA) and rotor Hopfield neural network and grey wolf optimization (RHNN/GWO). Furthermore, the model is validated by experimental data, and the final results demonstrate the efficacy of the hybrid approach in accurately representing the intricate behavior of SOFC stacks. The proposed model achieves lower error rates (ERs) and root mean squared errors (RMSEs) than the comparative methods across different network arrangements and temperature conditions. The results show that, for instance, for the 2/12/1 network arrangement at 900°C, the proposed model attains an ER of 6.69% and an RMSE of 2.13, while the ANN/MABA and RHNN/GWO methods obtain ERs of 9.67% and 8.54%, as well as RMSE values of 24.48 and 9.23, respectively. The proposed model also exhibits superior accuracy and convergence speed compared to the comparative methods, as shown by the current–voltage curves and the convergence analysis. Consequently, this novel hybrid methodology offers a valuable tool for researchers and engineers working in the domain of fuel cell technology, enabling them to better understand and optimize SOFC stack performance.