Impedance modeling for polymer electrolyte membrane fuel cells by combining the transient two-phase fuel cell and equivalent electric circuit models

Lee, Jiseung; Salihi, Hassan; Lee, Jaeseung; Ju, Hyunchul

doi:10.1016/j.energy.2021.122294

Cited by 13 publications

(3 citation statements)

References 52 publications

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“…In terms of the proton transfer loss, it is widely accepted that the membrane water content λ is the dominant factor and its equation can be expressed by [30]:…”

Section: Input Parameter Selectionmentioning

confidence: 99%

“…where R p,PEM and R p,CCL represent the proton transfer loss in PEM and cathode catalyst layer (CCL), respectively; L PEM and L CCL are the thickness of the PEM and CCL, respectively; ε is the ionomer volume fraction; and σ mem is the proton conductivity, which is the function of the membrane water content. As for the charge transfer loss R ct , its expression can be approximatively derived from the Butler-Volmer equation for oxygen reduction reaction as follows [30]:…”

Section: Input Parameter Selectionmentioning

confidence: 99%

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Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

Xie

Yuan

et al. 2023

Energies

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Electrochemical impedance is a powerful technique for elucidating the multi-scale polarization process of the proton exchange membrane (PEM) fuel cell from a frequency domain perspective. It is advantageous to acquire frequency impedance depicting dynamic losses from signals measured by the vehicular sensor without resorting to costly impedance measurement devices. Based on this, the impedance data can be leveraged to assess the fuel cell’s internal state and optimize system control. In this paper, a residual network (ResNet) with strong feature extraction capabilities is applied, for the first time, to estimate characteristic frequency impedance based on eight measurable signals of the vehicle fuel cell system. Specifically, the 2500 Hz high-frequency impedance (HFR) representing proton transfer loss and 10 Hz low-frequency impedance (LFR) representing charge transfer loss are selected. Based on the established dataset, the mean absolute percentage errors (MAPEs) of HFR and LFR of ResNet are 0.802% and 1.386%, respectively, representing a superior performance to other commonly used regression and deep learning models. Furthermore, the proposed framework is validated under different noise levels, and the findings demonstrate that ResNet can attain HFR and LFR estimation with MAPEs of 0.911% and 1.610%, respectively, even in 40 dB of noise interference. Finally, the impact of varying operating conditions on impedance estimation is examined.

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“…In terms of the proton transfer loss, it is widely accepted that the membrane water content λ is the dominant factor and its equation can be expressed by [30]:…”

Section: Input Parameter Selectionmentioning

confidence: 99%

Section: Input Parameter Selectionmentioning

confidence: 99%

Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

Xie

Yuan

et al. 2023

Energies

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“…18,19 Gerteisen 20 developed a one-dimensional model of CCL and investigated the effects of heterogeneous distribution of catalyst layer parameters such as protonic conductivity and volumetric double layer capacity on the impedance spectrum of CCL. Lee et al 21 numerically studied the EIS data of flooding and dehydration degradations using an EC model coupled with a one-dimensional physics-based model. Their coupled model was capable of predicting cell performance under various operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Model‐based diagnosis of proton‐exchange membrane fuel cell cathode catalyst layer microstructure degradation

Heidari¹,

Esmaili

Ranjbar³

2022

Intl J of Energy Research

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Summary It is of vital importance to utilize diagnostic tools that are capable of quickly identifying faulty conditions during cell operation. Electrochemical impedance spectroscopy (EIS) is a popular technique for monitoring proton‐exchange membrane fuel cell (PEMFC) performance and physics‐based models are a sophisticated tool for predicting cell behavior and interpreting the data achieved by EIS. In this study, a transient, one‐dimensional, two‐phase model for PEMFC membrane electrode assembly (MEA) was developed. By coding in MATLAB, the system of equations was solved and EIS spectra were obtained over the frequency range of 0.1 Hz to 100 kHz. The effects of Pt loss and ionomer distribution on EIS spectrum were investigated. The results indicate that the low‐frequency intercept of the impedance spectrum with the real axis, also known as the low‐frequency resistance (LFR), is an ideal indicator of Pt degradation and if not mitigated in the early stages, Pt degradation can significantly deteriorate cell electrochemical performance (up to 27% increase in LFR for the cases studied). The impact of heterogeneous ionomer distribution in cathode catalyst layer (CCL) on the high‐frequency portion of the impedance spectrum, also known as the “straight line” in the literature, is investigated. The results show that the length and the slope of the straight line are great indicators of ionomer degradation as the slope of the straight line deviated up to 16° from the ideal 45° (in case of a uniform distribution of ionomer in CCL) and the length of the straight line decreased about 44% for the cases studied. Highlights A transient, one‐dimensional, two‐phase model of proton‐exchange membrane fuel cell suitable for diagnostic applications is developed. Effects of cathode catalyst layer microstructure degradation on cell electrochemical impedance spectrum are investigated. The low‐frequency resistance is an ideal indicator of Pt degradation. The shape of the high‐frequency portion of the impedance spectrum could be utilized for ionomer degradation monitoring.

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Enhancing PEMFC performance through orifice-shaped cathode flow field designs: A multiscale, multiphase simulation study on oxygen supply and water removal

Lim,

Kim,

Park

et al. 2023

Chemical Engineering Journal

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Impedance modeling for polymer electrolyte membrane fuel cells by combining the transient two-phase fuel cell and equivalent electric circuit models

Cited by 13 publications

References 52 publications

Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

Model‐based diagnosis of proton‐exchange membrane fuel cell cathode catalyst layer microstructure degradation

Enhancing PEMFC performance through orifice-shaped cathode flow field designs: A multiscale, multiphase simulation study on oxygen supply and water removal

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