Estimation of localized current anomalies in polymer electrolyte fuel cells from magnetic flux density measurements

Nara, Takaaki; Koike, M.; Ando, Shigeru; Gotoh, Yasuo; Izumi, Masaaki

doi:10.1063/1.4943607

Cited by 13 publications

(18 citation statements)

References 6 publications

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“…In particular, it has been reported that a vertical current flowing perpendicular to the MEA dominantly generates the magnetic flux density. Nara et al reported the validity of 2-D modeling for computing the magnetic flux density outside the fuel cells [6]. In the present paper, we also assume a 2-D current source model: let an MEA be on z = 0, and let the current I flow in the −z direction perpendicular to the MEA.…”

Section: Magnetic Flux Density Produced By a Current Flowing Through mentioning

confidence: 99%

“…Although this method is able to model the current density of the fuel cell, it cannot precisely detect the size or shape of defects. Nara et al calculated the external magnetic field from the current density inside the fuel cell using a two-dimensional (2-D) model and proposed a method for predicting the center positions of defects using a complex analysis based-method [6]. However, they were not able to obtain the spatial extent of the defects.…”

Section: Introductionmentioning

confidence: 99%

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Level Set Method-Based Identification of Locations and Shapes of Fuel Cell Defects

Miyoshi

Nara

Gotoh

et al. 2018

SICE Journal of Control, Measurement, and System Integration

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Nondestructive inspection of the sizes and positions of defect regions in a fuel cell is an indispensable task in order to ensure their practical use. In the present paper, a level set method for estimating the defects in a membrane electrode assembly (MEA) in fuel cells is proposed. Boundaries of the defects in the MEA are represented by a zero contour of a level set function. By updating the function, the shapes and positions of the defect regions can be identified. A cost function for updating the level set function consists of the mean squared error between the magnetic fields computed by a 2-D model and sensor data and the regularization terms for the estimated shapes. The magnetic fields were measured using magnetic impedance sensors. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.

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Section: Magnetic Flux Density Produced By a Current Flowing Through mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Level Set Method-Based Identification of Locations and Shapes of Fuel Cell Defects

Miyoshi

Nara

Gotoh

et al. 2018

SICE Journal of Control, Measurement, and System Integration

Self Cite

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“…However, these non-destructive methods are difficult to implement in PEMFC stacks. Furthermore, previous studies [30][31][32] used numerous sensors and involved considerable calculation time to realize the same level of accuracy as the PCB method. Hence, installation and control in a PEMFC system are challenging.…”

Section: Introductionmentioning

confidence: 99%

“…The current distribution is derived based on the magnetic flux densities embedded within the fuel cell through Maxwell's equations [29]. Moreover, a three‐dimensional finite element method simulation that converts the environmental magnetic flux around the PEMFC cell was developed to estimate the localized current distribution [30]. However, these non‐destructive methods are difficult to implement in PEMFC stacks.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of stable the proton exchange membrane fuel cell control using magnetic sensor probes

et al. 2022

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In proton exchange membrane fuel cells (PEMFCs), the avoidance and detection of failures such as flooding and dry-out are crucial. Non-destructive measurement approaches using magnetic sensors have been developed for this purpose.To reduce the number of sensors required to be installed in a PEMFC system for evaluation, we propose a control method for PEMFCs using the magnetic flux density as an index. The proposed method determines the failure conditions by calculating a simple current mapping with a reduced number of sensors. The results of four-point magnetic flux densities under failure conditions and stable control using two-point measurement indices are presented.

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“…Yamanshi and al. [33] also investigated fuel cell diagnosis by measuring the magnetic field, and an additional study was done in [34]. In these studies, a genetic algorithm is used to find a linear combination of magnetic field signatures associated to elementary faults.…”

Section: Introductionmentioning

confidence: 99%

Fault detection for polymer electrolyte membrane fuel cell stack by external magnetic field

Ifrek

Rosini

Cauffet

et al. 2019

Electrochimica Acta

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Luc Rouveyre, et al.. Fault detection for polymer electrolyte membrane fuel cell stack by external magnetic field. Electrochimica Acta, Elsevier, 2019, 313, pp. Abstract:An original non-invasive approach of fuel cell diagnosis is proposed in order to locate different kinds of faults in PEMFC stacks from magnetic field measurements. The method is based on the solving of an inverse linear problem linking the magnetic field signature outside of the fuel cell to the current density distribution inside.The searched solution is a linear combination of conservative current distribution obtained by a set of electrokinetic problems solved by a finite face element method. As the problem is ill-posed, the solution is stabilized using a truncated singular value decomposition. In this work, 30 sensors are used to perform the 2 magnetic tomography of a PEMFC stack consisting of 100 cells with a large active area of 220 cm 2 . External magnetic measurement makes possible to identify 2D or 3D changes of current density distribution induced either by a cell flooding or membrane drying as well as by material degradation in a PEMFC stack.

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Estimation of localized current anomalies in polymer electrolyte fuel cells from magnetic flux density measurements

Cited by 13 publications

References 6 publications

Level Set Method-Based Identification of Locations and Shapes of Fuel Cell Defects

Level Set Method-Based Identification of Locations and Shapes of Fuel Cell Defects

Experimental investigation of stable the proton exchange membrane fuel cell control using magnetic sensor probes

Fault detection for polymer electrolyte membrane fuel cell stack by external magnetic field

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