A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems

Zheng, Zhixue; Petrone, Raffaele; Péra, Marie‐Cécile; Hissel, Daniel; Becherif, Mohamed; Pianese, Cesare; Steiner, Nadia Yousfi; Sorrentino, Marco

doi:10.1016/j.ijhydene.2013.04.007

Cited by 175 publications

(93 citation statements)

References 74 publications

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“…Although several studies employ a model-based method for fuel cell diagnostics, i.e., developing a fuel cell model, and identifying fuel cell faults from residuals between model outputs and actual measurements [4][5][6][7][8], there are complexities in developing an accurate fuel cell model containing complete sets of failure modes. Data-driven approaches are more widely used for fuel cell diagnostics, that is, extracting the features by applying signal processing techniques to the sensor data, and discriminating fuel cell faults with extracted features [9][10][11][12][13]. Compared to fuel cell diagnostics, fewer studies have been devoted to fuel cell prognostics, and among these studies, training data from a fuel cell system is required to generate the input-output relationship of the fuel cell model for the prediction of future performance [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

2017

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Abstract:In this paper, the sensor selection approach is investigated with the aim of using fewer sensors to provide reliable fuel cell diagnostic and prognostic results. The sensitivity of sensors is firstly calculated with a developed fuel cell model. With sensor sensitivities to different fuel cell failure modes, the available sensors can be ranked. A sensor selection algorithm is used in the analysis, which considers both sensor sensitivity to fuel cell performance and resistance to noise. The performance of the selected sensors in polymer electrolyte membrane (PEM) fuel cell prognostics is also evaluated with an adaptive neuro-fuzzy inference system (ANFIS), and results show that the fuel cell voltage can be predicted with good quality using the selected sensors. Furthermore, a fuel cell test is performed to investigate the effectiveness of selected sensors in fuel cell fault diagnosis. From the results, different fuel cell states can be distinguished with good quality using the selected sensors.

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Section: Introductionmentioning

confidence: 99%

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

2017

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“…Despite the complexities, methods are developed to predict Fuel Cell State of Health (SoH) in terms of performance loss, degradation and, fault detection and isolation (FDI) [45,63,[74][75][76][77][78][79]. Diagnostic methods available are model or non-model based [71,[80][81][82][83][84]. Model-based methods are further classified as white, grey or black box depending on the nature of input and output.…”

Section: Pemfc Life Prediction Methods Under Aeronautic Conditionsmentioning

confidence: 99%

“…On the other hand, non-model based methods are simple, flexible, capable of dealing with nonlinear problems and do not require system structure knowledge. Non-model based methods are further grouped as artificial intelligence, statistical method or signal processing [82]. An emerging area of science called Prognostics and Health Management (PHM) focuses on methods that assess State of Health (SoH), predict Remaining Useful Lifetime (RUL) and decide mission achievement from mitigation actions [80,85].…”

Section: Pemfc Life Prediction Methods Under Aeronautic Conditionsmentioning

confidence: 99%

“…The fault diagnosis methods involve four steps: data pre-processing, feature extraction, feature reduction and fault classification. One major drawback of the non-model based method is generating datasets for targeted fault conditions, which can be time-consuming in cases of multiple faults [82]. Dataset generation for data pre-processing includes acquiring data from different sensors and electrochemical characterization techniques such as Polarization Curve (IV curve), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and Linear Square Voltammetry (LSV).…”

Section: Pemfc Life Prediction Methods Under Aeronautic Conditionsmentioning

confidence: 99%

“…The study defined three subspaces related to nominal, drying and flooding conditions by measuring membrane and polarization resistances. Figure 11 shows that the resistances of the normal cell from Fouquet et [81,82,86]. The reviews showed that there is no single technique capable of providing real-time diagnostic data, cost-effective, non-intrusive and insensitive to electrical network.…”

Section: Electrochemical Impedance Spectroscopymentioning

confidence: 99%

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PEMFC for aeronautic applications: A review on the durability aspects

et al. 2017

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Proton exchange membrane fuel cells (PEMFC) not only offer more efficient electrical energy conversion, relative to on-ground/backup turbines but generate by-products useful in aircraft such as heat for ice prevention, deoxygenated air for fire retardation and drinkable water for use on-board. Consequently, several projects (e.g. DLR-H2 Antares and RAPID2000) have successfully tested PEMFC-powered auxiliary unit (APU) for manned/unmanned aircraft. Despite the progress from flying PEMFC-powered small aircraft with 20 kW power output as high as 1 000 m at 100 km/h to 33 kW at 2 558 m, 176 km/h [1-3], durability and reliability remain key challenges. This review reports on the inadequate understanding of behaviour of PEMFC under aeronautic conditions and the lack of predictive methods conducive for aircraft that provide real-time information on the State of Health of PEMFCs. Highlights: The main research findings are -To minimize performance loss due to high altitude and inclination by adjusting cathode stoichiometric ratio. -To improve quality of oxygen-depleted air by controlling operating temperature and stoichiometric ratio. -Need to devise real time prediction methods conducive for determining PEMFC SoH in aircraft.

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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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A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems

Cited by 175 publications

References 74 publications

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

PEMFC for aeronautic applications: A review on the durability aspects

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

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