43 Journal of New Materials for Electrochemical Systems Vol.24, No.1, January, 2021, pp. 43-48 http://new-mat.org http://iieta.org/journals/jnmes Novel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell Abdelghani Harrag Mechatronics Laboratory, Optics and Precision Mechanics Institute, Ferhat Abbas University Setif 1, Cite Maabouda (ex. Travaux), 19000 Setif Algeria Corresponding Author Email: a.b.harrag@gmail.com ABSTRACT This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT. Keywords: PEM Fuel Cell, MPPT, Single Sensor, Neural Network, NN Received: November-10-2020, Accepted: January-31-2021, https://doi.org/10.14447/jnmes.v24i1.a08 NOMENCLATURE V Voltage, V i Current, A T Temperature, K P Pressure, bar R Resistance, Ω A Active area, cm 2 Greek symbols ξi (i = 1 to 4) Parametric coefficients Subscripts FC Fuel Cell Nernst Nernst voltage act Activation losses ohmic Ohmic losses conc Concentration losses H2 Hydrogen O2 Oxygen C Contact M Membrane 1. INTRODUCTION In the last decades, the demand for clean, green and sustainable energy sources has become a strong requirement and driving force in the continuity of economic development and therefore in the enhancement of human living conditions. Consequently, fuel cells and hydrogen energy in general have been acknowledged as one of keystones of clean energy technologies due to their high energy density, high efficiency, and low/zero emissions. Lately, diverse energy sectors like transportation, stationary and portable power, and micro- power have experiencing an explosive growth of applications using fuel cells requiring by the way a basic science and technology knowledge as well as advanced fuel cell design and analysis techniques [1]. A fuel cell is an electrochemical device converting continuously the chemical energy content of the fuel into electrical energy, water, and heat as long via reverse electrochemical reactions. Among various types of fuel cells, the high temperature solid oxide fuel cell (SOFC) and the low temperature proton exchange membrane fuel cell (PEMFC) have been identified as the expected fuel cell categories that will dominate the market in the near future. The PEMFC uses a solid membrane that transports protons. It can operate from about 0°C to 80°C with the output power ranging from a few watts to several hundred kilowatts[2]. One of most relevant issue in fuel cell usage is it's stability related to its non regulated output power due especially to the heavily influence of changes in electric current, temperature, membrane water content, stoichiometry, partial gas pressures, gas speed and reactants humidity level on its voltage. As a result, the fuel cell maximum power extraction is crucial for its economical and optimum usage. Conversely, due to the varying load current requirements and the varying operating conditions, the extraction of the maximum available power varies dynamically during the fuel cell operation making it as a challenging task[3]. The last decade has observed a vast implementation of fuel cell maximum power point tracking (MPPT) controllers [4-5], among them: perturb and observe[6], incremental conductance[7], sliding mode approach[8], fractional order filter strategy[9], hysteresis method[10], extremum seeking control[11], fuzzy logic controller[12-13], particle swarm optimization controller[14], water cycle algorithm[15], unified tracker algorithm [16], eagle strategy method [17], neural network approach[18-19], etc. This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the