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The use of hydrogen as a fuel is positioned as one of the most advanced alternatives for abandoning fossil fuels, already being used for power generation, industrial applications, and transportation. This article proposes the use of intelligent techniques to model the behavior of a hydrogen fuel cell to predict the input rate of molecular hydrogen into the system based on its operating point. To this end, three intelligent techniques—Polynomial Regression, Decision Trees, and Multi-Layer Perceptron—are employed to obtain the model with the best performance, determined through cross-validation. The models are subsequently validated, achieving accurate predictions and enabling better energy management and use.

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Predicting Hydrogen Consumption in a Fuel Cell Using Intelligent Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Antonio Díaz-Longueira,
  • Francisco Zayas-Gato,
  • Tomás González-Ayuso,
  • José Luis Calvo-Rolle

摘要

The use of hydrogen as a fuel is positioned as one of the most advanced alternatives for abandoning fossil fuels, already being used for power generation, industrial applications, and transportation. This article proposes the use of intelligent techniques to model the behavior of a hydrogen fuel cell to predict the input rate of molecular hydrogen into the system based on its operating point. To this end, three intelligent techniques—Polynomial Regression, Decision Trees, and Multi-Layer Perceptron—are employed to obtain the model with the best performance, determined through cross-validation. The models are subsequently validated, achieving accurate predictions and enabling better energy management and use.