Automated Analysis of EIS curves for PEM Fuel Cells using Dynamic Time Warping

Fuel cell (FC) is a promising solution in order to tackle global warming problems. Though, efforts are needed for the development of reliable tools to monitor the FC state of health and to extract useful information in order to detect possible malfunctioning. The present paper propose an original method based on the Dynamic Time Warping (DTW) technique in order to process and analyze electrochemical impedance spectroscopy data. The proposed method extracts information on the similarities between 2 EIS curves. This method is evaluated on data from start-up and shut-down experimental campaign on a high temperature PEM-FC stack. Several hundreds of EIS curves are processed over 5 different conditions. The proposed method reaches 92% of correct unsupervised classifications. From the different classes identified, the ohmic resistance is extracted in order to study the impact of 2 different start-up and shut-down protocols on the FC performance.