A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model

Abstract

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based online auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.

An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique

“…4H + + 4 e − + 4O + → 2H 2 O + heat cathode (2) Under normal operation conditions, a single cell produces from 0.5 to 0.9 volts since a higher power is needed, and a number of cells are connected serially and as needed called a stack, and a stack arrangement can be up to hundreds of kilowatts. Generally, the polarization curve is used to express the performance of the whole cell and shows highly nonlinear properties between voltages and current (Dagher, 2018;Al-Araji, et al, 2019). The output voltage of a single cell can be defined as follows:…”

“…Vcon represents the voltage drop resulting from the concentration or mass transportation of the reacting gases in volt (Mammar and Chaker, 2009). Table 1 shows the physical parameters of PEMFC taken from (Dagher, 2018;Al-Araji, et al, 2019).…”

“…Recently, the world goes towards alternative sources of energy as a result of the pollution on the planet and climate change, as well as the diversity of sources that are sought by major energy companies. Therefore, many researchers are focusing on fuel cells because of their advantages as they operate as a battery or internal combustion engines, but with high efficiency up to two or three times (Al-Amir, et al, 2015;Al-Araji, et al, 2019). Although discovered in the 18 th century, it began to highlight on fuel cells in the 1960s when NASA used it in spacecraft launched into space (Spiegel, 2011).…”

“…This research will focus on PEMFC because of its great advantages. Also, the most important is that it can work in room temperature with low pressure and fast response quiet, does not have moving parts, and can be applied in portable applications, the domestic power or power stations (Dagher, 2018;Daud, et al, 2017 ;Al-Araji, et al, 2019). The fuel pressure, temperature, humidity, and applied current are factors affecting the fuel cell and making the fuel cell system generation nonlinear dynamical behavior (Rezazadeh, et al, 2011).…”

“…So that, many other researchers have worked to use nonlinear methods and techniques to solve the variation parameters problems in the fuel cell such as sliding mode controller (Ali, et al 2017) and adaptive back-stepping controller (Zuniga-Ventura, et al, 2018). While other researchers have resorted to using artificial intelligence algorithms to understand the phenomena involved in the generation process of fuel cells such as fuzzy logic control (Mammar and Chaker, 2009;Schumacher, et al, 2004), the fuzzy-PID controller (Beirami, et al, 2015), neural networks (Al-Amir, et al, 2015;El-Sharkh, et al, 2004;Mammar and Chaker, 2012) and nonlinear PID controller was proposed for fuel cell output voltage system (Al-Araji, et al, 2019). Besides, many types of intelligent evolutionary algorithms have been used to build the modeling of PEMFC and controlling it.…”