Development of an Oxygen Pressure Estimator Using the Immersion and Invariance Method for a Particular PEMFC System

Abstract

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.

Special Issue on “Multi-Period Optimization of Sustainable Energy Systems”

“…The authors then carried out short-term wind power prediction using a back-propagation neural network optimized using the genetic algorithm. Hernández-Gómez et al [8] proposed the immersion and invariance method to develop an oxygen pressure estimator based on the voltage, electrical current density, and temperature measurements in proton exchange membrane fuel cell (PEMFC) systems, so as to replace oxygen sensors for fault diagnosis.…”