Parameter Identification of Solid Oxide Fuel Cell Using Teaching-Learning Based Collective Intelligence

<div class="section abstract"><div class="htmlview paragraph">The accurate extraction of internal operating parameters associated with multi-physicochemical processes forms the basis for precise modelling of solid oxide fuel cells (SOFCs), which serves as the foundation for predicting performance degradation and estimating the lifespan of SOFCs. In this work, a novel integration of the teaching-learning based optimization (TLBO) and collective intelligence (CI), referred as the teaching-learning based collective intelligence algorithm (TLBCI), is introduced. This algorithm utilizes diverse characteristic patterns, including current-voltage (I-V) curves and sequential output data, to enhance the overall identification of degradation process. Experimental data was gathered from a 3-cell SOFC short stack during a 640-hour durability test. The proposed parameter identification algorithm employs a collective intelligence framework, wherein sub-optimizers are based on genetic algorithm (GA) and individually tasked with processing specific formats of cell characteristics and identifying parameters from their degradation time-dependent changes. After each identification iteration, all sub-optimizers undergo an assessment to evaluate their prediction accuracy with respect to current, voltage, and degradation rate respectively. Within the TLBO framework, individuals with lower accurate results are designated as “students,” whereas the best individual in better performing sub-optimizer assumes the role of “teacher.” The students engage in a process of learning from the teacher individual, thereby augmenting the overall precision of the sub-optimizer collection across current, and voltage metrics. Compared to several state-of-the-art algorithms previously employed for parameter identification in SOFCs, the proposed TLBCI algorithm achieves superior optimization of both the prediction of output current and voltage decline, as well as the degradation of performance, with a high degree of precision quantified by mean squared error (MSE) and less computation time.</div></div>