Optimization of Cathode Catalyst Layer Composition of Polymer Electrolyte Fuel Cell for Maximum Cell Performance Using Machine Learning and Genetic Algorithm
Authors: Nguyen Van Lap , Permatasari Agnesia , So Magnus , Yano Takeru , Inoue Gen 
Published: December 2024

The Polymer electrolyte fuel cell (PEFC) is a promising technology for power generation, noted for its potential as a zero-carbon technology at the point of use. However, PEMFC faces challenges that must be addressed to enhance its viability, such as its high cost and inefficient output power density. A key component influencing these factors is the cathode catalyst layer (CCL), which is critical for the mass transport flux and electrochemical reactions within the fuel cell. A strategy to lower costs while maintaining performance involves optimizing the CCL composition, including the ionomer-to-carbon (I/C) ratio, Pt-to-carbon (Pt/C) ratio, CCL thickness, and the type of carbon black used, alongside minimizing the Pt loading. Such optimization involves consideration of multiple factors and, consequently, adjusting the CCL composition might have a high potential to balance cost and performance. In this study, therefore, our goal is to propose an optimized catalyst composition that enables high-cell performance with low Pt loading. In particular, we integrated a surrogate machine learning model with an optimization algorithm to determine the optimization of CCL composition.

The composition of each CCL is characterized by several variables, including the Pt/C and I/C, the thickness of the catalyst layer, the ionomer coating condition (uniform and non-uniform coatings), and the type of carbon used, which varied between porous (Ketjen) and non-porous (Vulcan) carbon [1]. These variables and output power density were assigned as input and output variables of the machine learning models, respectively. The dataset for the machine learning model was prepared by conducting cell performances with various CCL compositions by the multi-block method detailed in our previous work [2,3]. A total of 390 simulations were used as the dataset, with 312 data points used for training and 78 data points for testing.

An assembled machine-learning algorithm known as Extreme Gradient Boosting (XGB) was employed to develop the surrogate model. To identify the optimal CCL composition that generates the highest output power density at a given Pt loading, the XGB-based surrogate model was integrated with an optimization algorithm based on the genetic algorithm (GA). In particular, the fitness of each individual within the GA population is determined by the XGB-based surrogate model, and the GA iteratively evaluates the fitness using operations such as selection, crossover, and mutation, to converge toward the composition that results in the highest power density.

The performance of the XGB-based surrogate model in predicting cell performance is validated by its root mean squared error (RMSE) and square correlation coefficient (R²) on the test set, which are 0.07 and 0.89, respectively. This means the cell performance can be predicted by the surrogate model. In the importance feature analysis, the I/C ratio, carbon type, and thickness were identified as the most important parameters of CCL structures. This suggests that we can reduce the Pt load by optimizing the I/C ratio, CCL thickness, and type of carbon black. From the GA results, an optimal starting Pt loading of 0.25 mg/cm² was refined by adjusting the I/C ratio to 0.95, selecting Vulcan as the carbon type, and setting the CCL thickness to 6.54 µm. Through optimization, the Pt load was successfully reduced to 0.198 mg/cm², while maintaining the same power density as the initial loading of 0.25 mg/cm². This outcome highlights the efficacy of the surrogate model and optimization approach in enhancing the cost-efficiency of PEMFCs without compromising their performance.

Acknowledgments

This research was supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan (grant number P20003-20001327-0).

References:

[1] K. Park et al., J. Power Sources Advances, 2022, 15,100096

[2] G. Inoue et al., J. Power Sources 2019 439, 227060

[3] G Inoue et al., Int. J. Hydrogen Energy 2022, 47 (25), 12665