Intelligent Extremum Seeking Control of PEM Fuel Cells for Optimal Hydrogen Utilization in Hydrogen Electric Vehicles

Abstract

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles.