Oxygen bubble accumulation on the anodic side of a polymer exchange membrane water electrolyzer (PEMWE) may cause a decrease in performance. To understand the behavior of these bubbles, a deep-learning-based bubble flow recognition tool dedicated to a PEMWE is developed. Combining the transparent side of a single PEMWE cell with a high-resolution high-speed camera allows us to acquire images of the two-phase flow in the channels. From these images, a deep learning vision system using a fine-tuned YOLO V7 model is applied to detect oxygen bubbles. The tool achieved a high mean average precision of 70%, confirmed the main observations in the literature, and provided exciting insights into the characteristics of two-phase flow regimes. In fact, increasing the water flow rate from 0.05 to 0.4 L/min decreases the bubble coverage (by around 32%) and the mean single-bubble area. In addition, increasing the current density from 0.3 to 1.4 A/cm² leads to an increase in bubble coverage (by around 40%) and bubble amount.

This work focuses on a signal-based diagnosis approach dedicated to proton exchange membrane water electrolyzer (PEM WE) anode pump fault. The PEM WE cell measurements are performed with an experimental test bench to highlight the impact of water flow rate in the anode compartment. This approach is non-intrusive, and it can detect anode flow rate variation during the electrolysis and is designed to fulfill online diagnosis requirements. Contrary to electrochemical impedance spectroscopy-based approaches (EIS), this method stands out from existing procedures as a result of its few requirements, excluding any signal with perturbing amplitude. Therefore, the electrolyzer remains continuously available, even while the analysis is performed. The empirical mode decomposition (EMD) is used to decompose the signal variation into a sum of amplitude modulation and frequency modulation (AM-FM) components, called intrinsic mode functions (IMFs). In this work, the PEM WE current signal is decomposed into several IMFs using EMD. Then, the energetic contribution of each IMF is calculated. Experimental results exhibited that the energetic contribution of IMFs can be used as relevant criteria for fault diagnosis in PEM WE systems. This process only requires monitoring of the PEM WE current and has a low computational cost, which is a significant economic and technical advantage.

In this paper, a coupled model of transfer phenomena within Proton Exchange Membrane Fuel Cell (PEMFC) developed from Stefan-Maxwell (in the diffusion and active layers), Butler-Volmer (in the active layer), and water mass transport (in the electrolyte membrane) equations is presented. This modeling allows interpreting experimental results, prediction of PEMFC electrical performances and guiding perspective investigations on optimization of PEMFC. The model helps the research of dominating sensitivity parameters, as well as the estimation of some badly known MEA (Membrane Electrode Assembly) parameters using fuel cell tests.