Fuel-Cell Thermal Management Strategies for Enhanced Performance: Review of Fuel-Cell Thermal Management in Proton-Exchange Membrane Fuel Cells (PEMFCs) and Solid-Oxide Fuel Cells (SOFCs)

Effective thermal management is crucial for optimizing the performance, efficiency, and durability of fuel-cell technologies, including proton-exchange membrane fuel cells (PEMFCs) and solid-oxide fuel cells (SOFCs). The operation of fuel cells involves complex heat generation mechanisms, primarily driven by electrochemical reactions, which can lead to significant energy loss as heat. This review examines the specific heat generation sources and challenges associated with different fuel-cell types, highlighting the critical importance of effective thermal management strategies. Key techniques for thermal regulation, including active and passive cooling systems, are examined in detail. Active cooling methods like liquid cooling and air cooling are effective in dissipating excess heat, while passive methods leverage advanced materials and optimized designs to enhance natural heat dissipation. Furthermore, innovative heat recovery systems are explored, demonstrating their potential to enhance overall energy efficiency by capturing and repurposing waste heat. The integration of machine learning techniques has arisen as a promising avenue for advancing temperature control in fuel cells. Reinforcement learning, deep learning algorithms, and support vector machines, along with artificial neural networks, are discussed in the context of their application in managing temperature dynamics and optimizing thermal performance. The review also emphasizes the significance of real-time monitoring, as well as adaptive control strategies to respond effectively to the dynamic operating conditions of fuel cells. Understanding and applying these thermal management strategies is essential for the successful commercialization of fuel cells across various sectors, ranging from automotive to stationary power generation. With the growing demand for clean energy solutions, progress in thermal management techniques will be crucial in improving the dependability and practicality of fuel-cell systems.

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Publication Details

Journal
Hydrogen
Published
2025-09-04
DOI
https://doi.org/10.3390/hydrogen6030065
Citations
8
Primary Topic
Fuel Cells and Related Materials
Type
article
Field-Weighted Citation Impact
5.84
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article

Fuel-Cell Thermal Management Strategies for Enhanced Performance: Review of Fuel-Cell Thermal Management in Proton-Exchange Membrane Fuel Cells (PEMFCs) and Solid-Oxide Fuel Cells (SOFCs)

Ibham Veza
8 citations
Hydrogen
Fuel Cells and Related Materials
5.84
article

Fuel-Cell Thermal Management Strategies for Enhanced Performance: Review of Fuel-Cell Thermal Management in Proton-Exchange Membrane Fuel Cells (PEMFCs) and Solid-Oxide Fuel Cells (SOFCs)

Ibham Veza
article en
8 citations

Abstract

Effective thermal management is crucial for optimizing the performance, efficiency, and durability of fuel-cell technologies, including proton-exchange membrane fuel cells (PEMFCs) and solid-oxide fuel cells (SOFCs). The operation of fuel cells involves complex heat generation mechanisms, primarily driven by electrochemical reactions, which can lead to significant energy loss as heat. This review examines the specific heat generation sources and challenges associated with different fuel-cell types, highlighting the critical importance of effective thermal management strategies. Key techniques for thermal regulation, including active and passive cooling systems, are examined in detail. Active cooling methods like liquid cooling and air cooling are effective in dissipating excess heat, while passive methods leverage advanced materials and optimized designs to enhance natural heat dissipation. Furthermore, innovative heat recovery systems are explored, demonstrating their potential to enhance overall energy efficiency by capturing and repurposing waste heat. The integration of machine learning techniques has arisen as a promising avenue for advancing temperature control in fuel cells. Reinforcement learning, deep learning algorithms, and support vector machines, along with artificial neural networks, are discussed in the context of their application in managing temperature dynamics and optimizing thermal performance. The review also emphasizes the significance of real-time monitoring, as well as adaptive control strategies to respond effectively to the dynamic operating conditions of fuel cells. Understanding and applying these thermal management strategies is essential for the successful commercialization of fuel cells across various sectors, ranging from automotive to stationary power generation. With the growing demand for clean energy solutions, progress in thermal management techniques will be crucial in improving the dependability and practicality of fuel-cell systems.

HydrogenVol. 6(3)
Universitas Bung Karno (ID), National Nuclear Energy Agency of Indonesia (ID)
Openalex Percentile: Top 4%
Fuel Cells and Related Materials
5.84
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