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The integration of Proton Exchange Membrane Fuel Cells (PEMFCs) with power converters requires effective control strategies to optimize energy conversion and ensure system stability. This paper presents an advanced control approach for a boost converter coupled with a PEMFC using Artificial Neural Networks (ANNs). The proposed ANN-based controller is designed to enhance the dynamic performance, improve voltage regulation, and adapt to the nonlinear behavior of the fuel cell. The performance of the developed control law is validated through simulation results using Matlab / Simulink. The results demonstrate that the ANN controller significantly improves the system’s response time, reduces voltage ripple, and enhances overall efficiency, making it a promising solution for fuel cell energy applications.

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Modeling and Control of a PEMFC Based on ANN Approach for Smart Grid applications

  • 会议论文

  • 发表日期：

    2026年

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  • Lakhsim Tarik,
  • Azougagh Mohammed,
  • Moutabir Ahmed,
  • Barra Adil

摘要

The integration of Proton Exchange Membrane Fuel Cells (PEMFCs) with power converters requires effective control strategies to optimize energy conversion and ensure system stability. This paper presents an advanced control approach for a boost converter coupled with a PEMFC using Artificial Neural Networks (ANNs). The proposed ANN-based controller is designed to enhance the dynamic performance, improve voltage regulation, and adapt to the nonlinear behavior of the fuel cell. The performance of the developed control law is validated through simulation results using Matlab / Simulink. The results demonstrate that the ANN controller significantly improves the system’s response time, reduces voltage ripple, and enhances overall efficiency, making it a promising solution for fuel cell energy applications.