A Survey of Diagnostic of Fuel Cell Stack Systems

Aïtouche, Abdel; Olteanu, Severus Constantin; Bouamama, Belkacem Ould

doi:10.3182/20120829-3-mx-2028.00261

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…A Fuel Cell Stack system (FCS) refers to a power generation system with fuel cell as the core, fuel supply and circulation system, oxidizer supply system, water/heat management system, control system, etc., and able to continuously output electronic power [1]. The main research interests for FCS include using lightweight materials, optimizing design, and improving the specific power of the fuel cell system, improving the fast cold start capability and dynamic response performance of the FCS system, researching fuel processors with load following capabilities, optimizing supercapacitors and hydrogen storage for system design to improve system efficiency and peak shaving capabilities, recover braking energy, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The main research interests for FCS include using lightweight materials, optimizing design, and improving the specific power of the fuel cell system, improving the fast cold start capability and dynamic response performance of the FCS system, researching fuel processors with load following capabilities, optimizing supercapacitors and hydrogen storage for system design to improve system efficiency and peak shaving capabilities, recover braking energy, etc. [1].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, for determining the best alternative among several others, TOPSIS proves to be a good MCDM method [39]. Apart from that, TOPSIS also has other advantages: (1) The structure of TOPSIS is reasonable;…”

Section: Introductionmentioning

confidence: 99%

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Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Jin

2021

Symmetry

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Multi-Criteria Decision Making (MCDM) methods have rapidly developed and have been applied to many areas for decision making in engineering. Apart from that, the process to select fault-diagnosis sensor for Fuel Cell Stack system in various options is a multi-criteria decision-making (MCDM) issue. However, in light of the choosing of fault diagnosis sensors, there is no MCDM analysis, and Fuel Cell Stack companies also urgently need a solution. Therefore, in this paper, we will use MCDM methods to analysis the fault-diagnosis sensor selection problem for the first time. The main contribution of this paper is to proposed a fault-diagnosis sensor selection methodology, which combines the rank reversal resisted AHP and TOPSIS and supports Fuel Cell Stack companies to select the optimal fault-diagnosis sensors. Apart from that, through the analysis, among all sensor alternatives, the acquisition of the optimal solution can be regarded as solving the symmetric or asymmetric problem of the optimal solution, which just maps to the TOPSIS method. Therefore, after apply the proposed fault-diagnosis sensor selection methodology, the Fuel Cell Stack system fault-diagnosis process will be more efficient, economical, and safe.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Jin

2021

Symmetry

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“…The need for diagnostics [2] and control [14] makes the construction of state observers a requirement. In this regard, the model based approach brings precision and a higher degree of generality but it comes at the price of difficulty in equations modeling and the risk of over complicating the system.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear observer for P.E.M. Fuel Cells using a Takagi-Sugeno approach with unmeasurable premise variables

Olteanu¹,

Gouni²,

Aïtouche³

et al. 2013

3rd International Conference on Systems and Control

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In this paper, a nonlinear observer based on a Takagi Sugeno approach is developed and applied to a PEMFCS (Proton Exchange Membrane Fuel Cell Stack) in the case of unmeasurable premise variables. The FC model focuses on the auxiliary elements associated to the stack. The challenge is to construct a TS fuzzy observer such that it can asymptotically estimate the states of the considered nonlinear systems despite the unmeasurable premise variables. The results are validated through the use of the professional software AMESim using a co-simulation with Matlab-Simulink.

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“…According to Aitouche et al (2012), fault diagnosis methods can be classified on two types: model-based and knowledge-based approaches. In model-based approaches a priori knowledge about the model of the process is assumed.…”

Section: Introductionmentioning

confidence: 99%

PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

Escobet¹,

Nebot

Mugica

2014

Engineering Applications of Artificial Intelligence

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a b s t r a c tIn this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20 dB.

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A Survey of Diagnostic of Fuel Cell Stack Systems

Cited by 7 publications

References 32 publications

Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Nonlinear observer for P.E.M. Fuel Cells using a Takagi-Sugeno approach with unmeasurable premise variables

PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

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