The self-discharge phenomenon results in a decrease of the open-circuit voltage (OCV), which occurs when an electrochemical device is disconnected from the power source. Although the self-discharge phenomenon has widely been investigated for energy storage devices such as batteries and supercapacitors, no previous works have been reported in the literature about this phenomenon for electrolyzers. For this reason, this work is mainly focused on investigating the self-discharge voltage that occurs in a proton exchange membrane (PEM) electrolyzer. To investigate this voltage drop for modeling purposes, experiments have been performed on a commercial PEM electrolyzer to analyze the decrease in the OCV. One model was developed based on different tests carried out on a commercial-400 W PEM electrolyzer for the self-discharge voltage. The proposed model has been compared with the experimental data to assess its effectiveness in modeling the self-discharge phenomenon. Thus, by taking into account this voltage drop in the modeling, simulations with a higher degree of reliability were obtained when predicting the behavior of PEM electrolyzers.
The fault detection method has been used usually to give a diagnosis of the performance and efficiency in the proton exchange membrane fuel cell (PEMFC) systems. To be able to use this method a lot of sensors are implemented in the PEMFC to measure different parameters like pressure, temperature, voltage, and electrical current. However, despite the high reliability of the sensors, they can fail or give erroneous measurements. To address this problem, an efficient solution to replace the sensors must be found. For this reason, in this work, the immersion and invariance method is proposed to develop an oxygen pressure estimator based on the voltage, electrical current density, and temperature measurements. The estimator stability region is calculated by applying Lyapunov’s Theorem and constraints to achieve stability are established for the oxygen pressure, electrical current density, and temperature. Under these estimator requirements, oxygen pressure measurements of high reliability are obtained to fault diagnosis without the need to use an oxygen sensor.
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