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Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems
(Department of Industrial Engineering, University of Salerno)
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
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2017 (English)In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 357, 26-40 p.Article in journal (Refereed) Published
Abstract [en]

The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 357, 26-40 p.
Keyword [en]
Polymer Electrolyte Membrane Fuel Cell, Model-based diagnosis, Fault detection, Fault isolation, Residual generation, Algorithm design
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137770DOI: 10.1016/j.jpowsour.2017.04.089ISI: 000403457000004Scopus ID: 2-s2.0-85018962975OAI: oai:DiVA.org:liu-137770DiVA: diva2:1101175
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2017-07-05Bibliographically approved

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The full text will be freely available from 2019-05-03 15:29
Available from 2019-05-03 15:29

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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