processes Article A Fuzzy Model to Manage Water in Polymer Electrolyte Membrane Fuel Cells Gómer Abel Rubio * and Wilton Edixon Agila   Citation: Rubio, G.A.; Agila, W.E. A Fuzzy Model to Manage Water in Polymer Electrolyte Membrane Fuel Cells. Processes 2021, 9, 904. https:// doi.org/10.3390/pr9060904 Academic Editor: Jin-Cherng Shyu Received: 7 April 2021 Accepted: 6 May 2021 Published: 21 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CASE, CIDIS, km 30.5 vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador; wagila@espol.edu.ec * Correspondence: grubio@espol.edu.ec Abstract: In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response, and, consequently, its autonomous operation. By applying load, current, and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits. Keywords: PEM fuel cell; fuzzy; neural network; electrical response; flooding; drying 1. Introduction This document is an extension of the work originally presented at Icrera 2019 [1]. The great problems of today’s society such as economic inequality, climate change, decreasing of the ozone layer, lack of resources, toxic pollution, diminishing biodiversity, human health, coastal settlements; must be confronted by the academy and decidedly by the engineering sciences. The hydrogen-powered fuel cell stack is the most promising energy source of the future [2], as it would help eliminate the world’s serious problems [3,4]. The hydrogen economy—where hydrogen and fuel cells are essential partners—is estimated to grow from USD 122 billion in 2018 to USD 155 billion in 2022 [5,6]. The fuel cell stack converts the chemical energy into electrical power and produces water and heat as subproducts during its operation. The proton exchange membrane fuel cell (PEMFC), it is expected to play a key role in the future energy system since its favorable characteristics such as high-power density, zero pollution, low operating temperature, quick start-up capability, and long lifetime. PEMFC can be used in cell phones, electric vehicles, distributed power systems, submarines, and aerospace applications. The PEMFC for its proper operation simultaneously requires both a high proton conductivity in the membrane and a sufficient supply of reactants; for this reason, water management is one of the most important issues and to manage it properly, it is neces- sary to know how it is transported and distributed through the different components of the PEMFC. Different methodologies have been proposed to study, diagnose, and prevent failures in water management in PEMFC. On the one hand, methodologies based on analytical, physical, or experimental models and on the other hand, methodologies that are based on non-models and that use artificial intelligence, statistics, or signal processing techniques. From the position of model-based methodologies, the problem of water management has been diagnosed considering models that involve multi-scale methods solved by electric circuits, numerical simulations [7–9], use of VOF in CFD, method of Lattice–Boltzmann, Processes 2021, 9, 904. https://doi.org/10.3390/pr9060904 https://www.mdpi.com/journal/processes