Etienne Dijoux
University of La Réunion
6 Papers
15 Citations
Etienne Dijoux is an academic researcher from University of La Réunion. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault (power engineering). The author has an hindex of 2, co-authored 3 publications. Previous affiliations of Etienne Dijoux include Centre national de la recherche scientifique.
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Papers
A review of fault tolerant control strategies applied to proton exchange membrane fuel cell systems
Etienne Dijoux,Etienne Dijoux,Nadia Yousfi Steiner,Michel Benne,Marie-Cécile Péra,Brigitte Grondin Perez +5 more
TL;DR: In this paper, the state-of-the-art Fault Tolerant Control (FTC) is applied to the proton exchange membrane fuel cell (PEMFC).
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Active Fault Tolerant Control Strategy Applied to PEMFC Systems
Etienne Dijoux,Michel Benne,Nadia Yousfi Steiner,Brigitte Grondin Perez,Marie-Cécile Péra +4 more
- 11 Dec 2017
TL;DR: This paper proposes an active fault tolerant control strategy to mitigate the most recurrent faults in PEMFC systems.
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Fault Structural Analysis Applied to Proton Exchange Membrane Fuel Cell Water Management Issues
TL;DR: In this paper, the authors propose a fuel cell fault structural analysis approach that leads to the proposition of a structural graph, which is then used to highlight the interactions between the control variables and the functionalities of a fuelcell, and therefore to emphasize how changing a parameter to mitigate a fault can influence the fuel cell state and eventually cause another fault.
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Experimental Validation of an Active Fault Tolerant Control Strategy Applied to a Proton Exchange Membrane Fuel Cell
TL;DR: In this article , the Active Fault Tolerant Control (AFTC) strategy is proposed for proton exchange membrane fuel cells (PEMFCs), which is composed of three functions.
A Novel Generic Diagnosis Algorithm in the Time Domain Representation
TL;DR: In this article , the authors proposed a fault diagnosis approach based on a unique variable measurement in the time domain and managed to extract the system behavior evolution, which can discriminate the two faulty operation modes of the fan from a normal condition and also manage to identify the current system state of health.
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