This study develops an advanced electrochemical model integrated with the Teaching-Learning Based Collective

Intelligence (TLBCI) algorithm to investigate degradation mechanisms in solid oxide fuel cells (SOFCs), with a

focused analysis on nickel (Ni) agglomeration/oxidation at the anode and yttria stabilized zirconia (YSZ)

agglomeration at the cathode. Key model parameters are directly extracted from experimental data, enabling

accurate performance prediction. The model systematically evaluates the impact of temperature fluctuations on

long-term SOFC degradation. Compared to conventional methods (Kalman filters, particle filters) and datadriven approaches (Long Short-Term Memory networks (LSTM), Echo State Networks (ESN)), the proposed

mechanism-based model achieves superior accuracy, lower Mean Squared Error (MSE), and enhanced predictive

capability in both short- and long-term forecasts. Furthermore, the work provides an in-depth analysis of elec-trochemical performance decay, including the evolution of overpotential components and material properties.

This comprehensive degradation framework advances the understanding of SOFC longevity and provides a

theoretical foundation for optimizing cell design, improving reliability, and enhancing operational effi-ciency—thereby supporting their commercial and industrial deployment (e.g., in distributed generation and

backup power systems). The findings offer critical insights for boosting SOFC performance under real-world

operating conditions.