journal article·2020-08

Recent progress in the use of electrochemical impedance spectroscopy for the measurement, monitoring, diagnosis and optimization of proton exchange membrane fuel cell performance

Zhepeng Tang, Qiu-An Huang, Yan-Jie Wang, Fangzhou Zhang, Weiheng Li, Aijun Li, Lei Zhang, Jiujun Zhang

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Abstract

Abstract The performance of proton exchange membrane fuel cells (PEMFCs), including reaction activity and durability, requires further improvement before large-scale commercialization, particularly in hydrogen-fueled automotive applications. However, PEMFCs are complex and non-linear systems with multi-physics at different scales in which many factors can affect performance, including external operating conditions and internal core components. Because of this, both theoretical modeling and experimental measurements need to be carried out to achieve performance optimization. With respect to this, electrochemical impedance spectroscopy (EIS) has been recognized as a powerful tool in the diagnosis and fundamental understanding of PEMFC performances. Based on this, this review will correlate the numerous publications related to EIS and its application with a particular focus on the effects of external operating conditions and internal core components on PEMFC performance. Furthermore, special attention will be given to the interpretation of the sensitivity of EIS data as applied to PEMFCs and several methods such as model-based, model-free and nonlinear methods are analyzed. And to facilitate the further research and development of EIS applications, this review will analyze current challenges and propose possible solutions towards enhancing the measurement and diagnosis reliability and accuracy of EIS for PEMFC performances.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Fuel Cells and Related Materials · Electrocatalysts for Energy Conversion · Electrochemical Analysis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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