Abstract
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 fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package along with the schematic design experimental work using Spartan-3E xc3s500e-4fg320 board with the Xilinx development tool Integrated Software Environment (ISE) version 14.7 and using Verilog hardware description language for design testing are illustrated the performance enhancement of the proposed an adaptive intelligent FPGA-PID-NN controller in terms of error voltage reduction and generating optimal value of the hydrogen partial pressure action (PH2) without oscillation in the output and no saturation state when these results are compared with other controllers.
Highlights
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
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)
There are many ways in which fuel cells are classified, the most important of which is by electrolyte and the most important types are Alkaline Fuel Cell (AFC), Phosphoric Acid Fuel Cell (PAFC), Molten Carbonate Fuel Cell (MCFC), Solid Oxide Fuel Cell (SOFC) and Proton Exchange Membrane Fuel Cell (PEMFC) (Barbir, 2012; Mekhilef, et al, 2012)
Summary
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. To obtain the greatest energy generation and more efficiency, many researchers resorted to using different types of linear controllers for tracking output voltage of fuel cells (Daud, et al, 2017). 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). The motivation for this research is to obtain the best value of the hydrogen partial pressure action (PH2) for enhancement of the dynamical performance of a real-time nonlinear fuel cell output voltage during a variable load current applied. The contribution of this work is to design and implement a digital neural network like PID control law to achieve the following properties:
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