Magnetic Field Analysis for State of Health Monitoring of PEM Electrolyzer Stacks
Authors: Rasuli Sistan , Buehler Kjell , Witte-Bodnar Kerstin , Naumann Volker , Schoenfelder Stephan , Ilse Klemens 
Published: December 2024

The expected increase in the number of installed PEM electrolyzers during their industrialization in the upcoming years highlights the need for suitable non-destructive diagnostic methods for state of health monitoring and anomaly detection to ensure reliability and durability while reducing lifetime costs. The inner electrical current density distribution significantly impacts the efficiency and lifetime of a PEM electrolyzer stack. During operation, various degradation phenomena and inhomogeneities, including inhomogeneous water and cooling management, starvation effects, contact pressure, and delamination, influence its electric conductivity and current density distribution. These, in turn, affect the generated vectorial magnetic field distribution inside and outside the stack.
This work examines the use of non-destructive magnetic field analysis (MFA) to identify changes in the outer magnetic field distribution and, thus, local current density distributions within PEM electrolyzer stacks. The magnetic field distribution is analyzed in all three spatial directions to trace electrical currents and identify electrical defects during different operating conditions. Accordingly, signatures in magnetic field images allow for the distinction between normal and abnormal system behavior. For initial investigations, creating a parameterized stack model using the finite element method (FEM) enabled the analysis of different stack geometries and their outer magnetic fields. The following examinations evaluated the effect of electric inhomogeneities on the magnetic field signatures, showing unique patterns depending on defect location and current density distribution. Both results were used to determine resolution limits, which enable the development of suitable sensor hardware and measurement concepts for industrial operating conditions. These concepts were initially tested and validated using a test stack designed to emulate the electrical behavior of an operational PEM electrolyzer stack without electrochemical reactions. Figure 1 shows one stylized measurement plane, along with the Bx, By, and Bz magnetic field component scans of the test stack in a non-defective state. Following these measurements, artificial defects are induced. After repeated experiments with an electrochemical operating stack, the work presents, compares, and discusses the resulting magnetic fingerprints of both stacks. Additionally, an overview of expected resolvable current-related defects is provided.
Magnetic field simulations and measurements conducted on a PEM electrolyzer test stack show systematic differences depending on defect location and current density distribution, resulting in characteristic magnetic field images. In addition, comparative experiments with an operating stack are presented and discussed, determining the potential and limitations of the method. Furthermore, enhancing Magnetic Field Analysis for industrial applications using machine learning methods will be assessed. These initial results suggest that MFA is potentially useful as a non-destructive diagnostic state of health monitoring method for industrial PEM electrolyzer stacks.

Figure 1