Study on non-contacting diagnostic method of PEFC performance using magnetic sensors (Approach using sparse modeling)

Abstract

This study aims to locate defects in polymer electrolyte fuel cells (PEFCs) caused by electrolyte breakage, electrical contact failure, contamination of electrical insulators, or the like in an easy and instantaneous manner by a noninvasive method. Specifically, the density of the magnetic flux generated around the PEFC during power generation is measured, and then value of the electric current in the electrodes of the PEFC is estimated from the magnetic flux density through inverse problem analysis using sparse modeling. Since the estimated current values are affected by the variables used in the inverse problem analysis, the procedure for determining the variables was first discussed using a simulated fuel cell that imitated the current flow inside the fuel cell. Then, the authors tried to detect a defect of 10 mm x 10 mm inside the fuel cell containing one layer of MEA (Membrane Electrode Assembly) with electrode area of 50 mm x 50 mm according to the above determination procedure. Each estimated current at the three characteristic defect locations was 0.00 A, and the estimated current values at other locations were higher than 0.08 A. From this current distribution, the location of the defect was able to be clearly identified. Particularly, it became possible to detect defects at the central part of the electrode, which was impossible with the Tikhonov regularization method.

Keywords

• fuel cell
• defect detection
• electric current distribution
• magnetic field
• inverse problem analysis
• sparse modeling