This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

A simple and novel membraneless paper-based microfluidic fuel cell was presented in this study. The occurrence of laminar flow was employed to ensure no mixing of the fuel and oxidant fluids along the bath of reaction. The acidic wastewater was used as a fuel. It was an air-breathing cell, so air and tab water were used as oxidants. Both the fuel and tab water flowed continuously under gravity. Whatman filter paper was used for preparation of the fuel cell channel and two carbon fibre electrodes were used and firmed on the edges of the cell. The performance of the cell was examined over three consecutive days. The results indicated that the present cell has the potential to generate electric power, but an extensive study is required to harv

   Zinc-air fuel cells (ZAFCs) are a promising energy source that could compete with lithium-ion batteries and perhaps proton-exchange membrane fuel cells (PEMFCs) for next-generation electrified transportation and energy storage applications. In the present work, a flow-type ZAFC with mechanical rechargeable was adopted, combined with an auxiliary cell (electrolyzer) for zinc renewal and electrolyte recharge to the main cell. In this work a practical study was performed to calculate the cell capacity (Ah), as well as study the electrolysis cell efficiency by current efficiency, and study the effective parameters that have an influence on cell performance such as space velocity and current density. The best parameters were selected to

      Low cost Co-Precipitation  method was used for Preparation of novel nickel oxide (NiO) nano particle thin films with Simple, with two different PH values 6, 12 and its effect on structural and optical properties as an active optical filter. Experimental results of structural properties X-ray diffraction (XRD) showed that both Nickel oxide nanoparticles with (PH=6 and 12) have polycrystalline structure smaller average particle size about 8.5 nm for PH=6 in comparison with PH=12. Morphological studies using Scanning electron microscopy (SEM) and  atomic force microscope (AFM)  show uniform nano rod distribution for PH=6 with smaller average diameter, average roughness as compared with NiO with

      In this research, carbon nanotubes (CNTs) is prepared  through the Hummers method with a slight change in some of the work steps, thus, a new method has been created for preparing carbon nanotubes which is similar to the original Hummers method that is used to prepare graphene oxide. Then, the suspension carbon nanotubes is transferred to a simple electrode position platform consisting of two electrodes and the cell body for the coating and reduction of the carbon nanotubes on ITO glass which represents the cathode electrode while platinum represents the anode electrode. The deposited layer of carbon nanotubes is examined through the scanning electron microscope technique (SEM), and the images throughout the research show the

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

The CIGS/CdS p-n junction thin films were fabricated and deposited at room temperature with rate of deposition 5, and 6 nm secG1 , on ITO glass substrates with 1mm thickness by thermal evaporation technique at high vacuum pressure 2×10G5 mbar, with area of 1 cm2 and Aluminum electrode as back contact. The thickness of absorber layer (CIGS) was 1 µm while the thickness of the window layer CdS film was 300 nm. The X-ray Diffraction results have shown that all thin films were polycrystalline with orientation of 112 and 211 for CIGS thin films and 111 for CdS films. The direct energy gaps for CIGS and CdS thin films were 1.85 and 2.4 eV, respectively. Atomic Force Microscopy measurement proves that both films CIGS and CdS films have nanostru