Enhancing PEM fuel cell dynamic performance: Co-optimization of cathode catalyst layer composition and operating conditions using a novel surrogate model

Abstract

Optimizing the cathode catalyst layer (CCL) composition and operating conditions to enhance the dynamic performance of proton exchange membrane fuel cells garners significant attention. Although machine learning surrogate models are efficient for fuel cell analysis and optimization, the varied voltage dynamic response patterns (e.g., loading failure, voltage undershoot, and voltage hysteresis) challenge regression surrogate models designed for steady-state performance predictions. In response, this study introduces a joint framework combining classification and regression models for dynamic performance prediction. For training, a transient, two-phase, non-isothermal fuel cell model with integrated catalyst agglomerate is developed. The dynamic voltage deviation (${\sigma }_V$) is proposed as an index to characterize the dynamic performance of the fuel cell. This joint surrogate model achieves correlation coefficients of 0.9976 and 0.9961 for predicting ${\sigma }_V$ in training and test sets, respectively. Through this model, sensitivity analyses of the CCL composition and operating conditions are conducted to quantify their impact and interactions on the fuel cell's dynamic performance. Besides, the analysis reveals a trade-off between dynamic performance and steady-state output. To balance these, a multi-objective optimization is conducted. The results indicate that, compared to the base case, dynamic and steady-state performance improved by 44 % and 8 %, respectively.