Proton Exchange Membrane Fuel Cell Model for Prognosis

Detti, Abdelkader Haidar; Jemeï, Samir; Steiner, Nadia Yousfi

doi:10.1109/vppc.2018.8605017

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Then, this is multiplied by the randomly generated input weight matrix to calculate the network input. Detti et al [93] likewise only used the voltage signal, which Fast Fourier Transform processes to obtain the Total Harmonic Distortion (THD). The popular KNN is employed to identify flooding and drying.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

Zhou,

Jervis

2023

Chemistry Methods

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Polymer electrolyte fuel cells (PEFCs) are regarded as a substitution for the combustion engine with high energy conversion efficiency and zero CO2 emissions. Stable system operation requires control within a relatively narrow range of operating conditions to achieve the optimal output, leading to faults that can easily cause accelerated degradation when operating conditions deviate from the control targets. Performance recovery of the system can be realized through early fault diagnosis; therefore, accurate and effective diagnostic characterisation is vital for long‐term serving. A review of off‐line and on‐line techniques applied to the fault diagnosis of fuel cells is presented in this work. Off‐line approaches include electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), galvanostatic charge (GSC), visualisation‐based and image‐based techniques; the on‐line methods can be divided into model‐based, data‐driven, signal‐based and hybrid methods. Since each methodology has advantages and drawbacks, its effectiveness is analysed, and limitations are highlighted.

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Section: Hybrid Methodsmentioning

confidence: 99%

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

Zhou,

Jervis

2023

Chemistry Methods

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“…The identification phase is based on the model presented in our previous work [38] combined with experimental polarization curves, then, their time evolution is modeled and predicted using an ARMA model. Thus, predicted polarization curves are reconstructed from the prediction of the parameters and will represent the performance degradation of the fuel cell.…”

Section: The Prognosis Approachmentioning

confidence: 99%

Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC)

Detti¹,

Steiner²,

Bouillaut³

et al. 2022

IJEPE

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Today the world is full of time-dependent phenomena in all fields: physics, chemistry, mechanics and many others. Time acts on the performance of any system whatever its nature is. When a system operates over time, aging becomes a real concern. Regarding fuel cells, several degradation phenomena can occur in a short or long term. Short-term phenomena are generally referred to as reversible degradations, these degradations are of the order of the microsecond and can go up to hours and sometimes to days, such as problems related to water management. The long term degradations are usually called irreversible degradations; it can be defined as aging. This phenomenon is of the order of a day and can increase up to months. In order to mitigate the impact of aging on the fuel cell system performance, good corrective actions must be taken. To do so, the performance prediction during the aging of the fuel cell system must be conducted. In this paper a verified and tested prognosis approach applicable to fuel cells is presented. The novelty in the approach used is linked to its modular structure. In fact, this approach has three phases. The first one aims to identify the internal physical parameters of the stack using an optimization algorithm on experimental data and a static fuel cell model. The second one predicts the temporal evolution of these parameters using a prediction algorithm. Finally, the reconstruction phase consists in the prediction of parameters that are re-injected into the model to reconstruct polarization curves and thus reflect the performance degradation of the fuel cell. The reliability and repeatability of the proposed approach have been successfully validated on two data sets from two different experimental campaigns.

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“…For transport applications, the Department of Energy (DOE) gave the objectives for 2025 and identified a durability target of 8000 hours (for fuel cell cars) with less than 10% loss of performance in order to meet commercial requirement [1]. To find solutions for the durability of fuel cells, there are two main ways: i) study new innovative materials to improve the lifetime of fuel cell systems by considering the catalyst structure [2], the catalyst support [3], hydrophobic doping for catalyst layers [4] and/or ii) develop additional approaches as diagnostics, prognostics and fault tolerant control dedicated to the fuel cell system [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Effect of Load Cycling on the Performance of Fuel Cell Stacks

Pahon

Jemeï

Steiner

et al. 2019

2019 IEEE Vehicle Power and Propulsion Conference (VPPC)

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The durability and the reliability of fuel cells are key points to continue to promote hydrogen energy as an efficient solution to face the global warming and the dependency to fossil fuels. Some long duration tests are needed to evaluate the performance losses of the fuel cell. This paper deals with the study of a proton exchange membrane fuel cell degradation due to different load cycling profiles. Based on a reference cycle, two other profiles are designed, by increasing the frequency and the magnitude of the load cycling. The impact of these two stressor factors, on short fuel cell stacks, is given as results.

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Proton Exchange Membrane Fuel Cell Model for Prognosis

Cited by 5 publications

References 15 publications

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives

Hybrid Ageing Model of a Proton Exchange Membrane Fuel Cell (PEMFC)

Effect of Load Cycling on the Performance of Fuel Cell Stacks

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