Estimation of the performance parameters for a direct methanol fuel cell with Fe–N–C cathodes using gradient boost models

Abstract

Direct methanol fuel cells (DMFCs) represent a promising energy conversion technology that offers compactness, high efficiency, and low emissions that are suitable for portable and clean energy applications. However, high market costs and resource limitations of platinum-group metal (PGM)-based catalysts remain a significant obstacle to their commercialized adoption. Despite PGM-free catalysts attracting recent attention, their design and development remain challenging, largely due to the complex non-linear correlation between control and performance parameters. This paper presents a machine-learning-based surrogate framework to predict the polarization curve and subsequently calculate power densities based on a set of model inputs: (i) temperature, (ii) Nafion concentration, (iii) methanol concentration, (iv) catalyst loading, and (v) current densities. Four separate gradient-boosting machine learning (ML) models, i.e. eXtreme gradient boosting (XGBoost), categorical boosting (CatBoost), histogram gradient-boosting regressor (HistGBR) and a light-gradient-boosting machine (LightGBM), were trained on experimental data corresponding to the set of predefined control parameters. The performance of each model also depends on how their hyperparameters were optimized using Bayesian optimization (BO), a tree-structured Parzen estimator (TPE), and Grey-Wolf Optimization (GWO). Interpolative analysis shows that the Grey-Wolf optimized CatBoost (CatGWO)-model was the best-performing model-optimizer framework, achieving an average R2 of 99.72% and an average mean absolute relative error of 2.16% with fairly limited standard deviation. To demonstrate its performance for possible out-of-the-range DMFC design applications, the model was subjected to extrapolation study with completely unseen hold-out datasets. Compared with prior studies, our CatGWO framework delivers promising cell-voltage predictions, and demonstrated only a modest gap between hold-in and hold-out performance. The proposed model offers a novel approach to improving the accuracy of machine learning predictions at very low computational latency. Potential applications include rapid pre-screening of operating conditions prior to multi-physics simulations, deployment as a surrogate model for optimization-inverse-AI modelling, and real-time use within model predictive control systems, accelerating the design and operational viability of practical Fe–N–C-based DMFCs.