Dynamic Kolmogorov–Arnold networks for time-varying degradation modeling in solid oxide fuel cells

Emerging power generation technologies such as solid oxide fuel cells (SOFCs) offer promising pathways for clean and efficient energy conversion. However, their material instability accelerates unexpected degradation, which remains a major barrier to large-scale commercialization. As a practical solution, control and diagnosis systems are integral to optimizing SOFCs’ lifetime and efficiency in real-world operations. Degradation mechanisms induce nonlinear, time-varying patterns in running fuel cells, affecting the effectiveness of control and diagnosis strategies. This study introduces a data-driven machine learning framework for predicting SOFC performance under degradation. Four lab-scale SOFCs were subjected to accelerated degradation tests, generating diverse run-to-failure datasets. To capture the complex, nonstationary dynamics in these data, a dynamic neural network based on Kolmogorov–Arnold approximation theory (DKAN) is developed. DKAN employs univariate splines as learnable activation functions, hierarchically adapting low-dimensional functions to diverse nonlinearities and temporal patterns. Comparative experiments against state-of-the-art sequence models, including LSTM, TCN, WaveNet, DGRU, Informer, and ConvRec, show that DKAN achieves on average 30% lower prediction error (across RMSE, WAPE, and MASE) and 55% faster inference relative to the baselines, while demonstrating superior generalization to unseen degradation patterns. Furthermore, statistical analyses using the Friedman–Nemenyi and ANOVA-Tukey tests confirm the significance of DKAN’s performance improvements across multiple datasets and metrics. These results highlight DKAN’s potential as a lightweight and scalable solution for real-time SOFC diagnostics and control.