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Voltammetric Methods for Hydrogen Crossover Diagnosis in a PEMFC Stack.

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Title: Voltammetric Methods for Hydrogen Crossover Diagnosis in a PEMFC Stack.
Authors: Génevé, T.1 geneve@laplace.univ-tlse.fr, Turpin, C.1,2, Régnier, J.1, Rallières, O.1, Verdu, O.3, Rakotondrainibe, A.3, Lombard, K.3
Source: Fuel Cells. Apr2017, Vol. 17 Issue 2, p210-216. 7p.
Subjects: Proton exchange membrane fuel cells, Hydrogen, Voltammetry, Electric potential, Electric currents
Abstract: Fuel cell diagnosis is a key point for a large scale development of this technology. Gas crossover through the membrane is one of the most crucial faults that have to be detected. Several methods of crossover estimation exist, but this work focuses on voltammetry. The main purpose is to provide a method of stack diagnosis that can be deployed in an industrial context for in situ measurements. The method has to detect accurately which cell is damaged, and how high the gas crossover is. Two voltammetric methods are compared on different stacks. First, cyclic voltammetry is applied to every cell of the stack (cell CV), cell after cell. Then potential is applied to the entire stack with an equivalent sweep rate (stack cyclic voltammetry - stack CV). Results show that with stack CV, one cannot strictly access the crossover current of a cell, as cell voltages are indeed not homogeneous along the stack and the current is common for all cells. However, an empirical law that links cell voltages during a stack CV and their crossover currents can be built. It can be used as an abacus to diagnose the crossover of each individual cell with a single stack CV. [ABSTRACT FROM AUTHOR]
Copyright of Fuel Cells is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Voltammetric Methods for Hydrogen Crossover Diagnosis in a PEMFC Stack.
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  Data: <searchLink fieldCode="JN" term="%22Fuel+Cells%22">Fuel Cells</searchLink>. Apr2017, Vol. 17 Issue 2, p210-216. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Proton+exchange+membrane+fuel+cells%22">Proton exchange membrane fuel cells</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrogen%22">Hydrogen</searchLink><br /><searchLink fieldCode="DE" term="%22Voltammetry%22">Voltammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+potential%22">Electric potential</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+currents%22">Electric currents</searchLink>
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  Data: Fuel cell diagnosis is a key point for a large scale development of this technology. Gas crossover through the membrane is one of the most crucial faults that have to be detected. Several methods of crossover estimation exist, but this work focuses on voltammetry. The main purpose is to provide a method of stack diagnosis that can be deployed in an industrial context for in situ measurements. The method has to detect accurately which cell is damaged, and how high the gas crossover is. Two voltammetric methods are compared on different stacks. First, cyclic voltammetry is applied to every cell of the stack (cell CV), cell after cell. Then potential is applied to the entire stack with an equivalent sweep rate (stack cyclic voltammetry - stack CV). Results show that with stack CV, one cannot strictly access the crossover current of a cell, as cell voltages are indeed not homogeneous along the stack and the current is common for all cells. However, an empirical law that links cell voltages during a stack CV and their crossover currents can be built. It can be used as an abacus to diagnose the crossover of each individual cell with a single stack CV. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Fuel Cells is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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