Design of Optimum SIMO-PID Neural Voltage-Tracking Controller for Non-Linear Fuel Cell System Based on a Comparative Study Of Various Intelligent Swarm Optimization Algorithms

Dhahad, Hayder A.; Dagher, Khulood E.; Al-Araj, Ahmed S.

doi:10.1088/1757-899x/1094/1/012027

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…It is possible to calculate each term in Eqs. (4-6) using the physical fuel cell characteristics mentioned in Table 1 [15,[19][20][21]. The ideal output voltage is represented by 𝐸 𝑁 , which is the cell's electrochemical thermodynamic potential and may be calculated as follows [15,19]:…”

Section: Pemfc Nonlinear Modelmentioning

confidence: 99%

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Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

2023

IJIES

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This paper presents a new development of a predictive voltage neural controller to control the stack terminal output voltage of a nonlinear proton exchange membrane fuel cell (PEMFC) system based on a neural network technique and a back-propagation learning algorithm. The main objective of this paper is to precisely and quickly identify the best control action of the hydrogen partial pressure to enhance the nonlinear performance of the fuel cell output voltage under a variable load current. This optimal control action prevents damage to the fuel cell membrane, thereby prolonging the fuel cell's lifetime. The proposed predictive voltage controller consists of three sub-controllers. The first one is the numerical feed-forward controller (NFFC), which is used to decide the steady-state hydrogen partial pressure (PH2) control action depending on the desired voltage. The second sub-controller is a feedback neural controller that uses a multi-layer perceptron (MLP) and a back-propagation learning algorithm to generate the hydrogen partial pressure feedback control action to track the desired output voltage of the fuel cell during transient conditions. The third sub-controller is the predictive control law equation, which is based on the modified Elman recurrent neural network (MERNN) as an identifier for the PEMFC model and the multi-objective performance index. From the simulation results, the proposed controller, which is composed of the three sub-controllers, has the capability to generate a precisely and quickly timed response to the hydrogen partial pressure control action in order to minimize the tracking voltage error and eliminate oscillation in the output voltage of the fuel cell. Finally, the suggested predictive voltage control strategy's numerical simulation results are then verified by comparison with those of other types of controllers in terms of the minimum number of steps ahead prediction (reducing from 10 to 1 step ahead prediction) and enhancement of the tracking voltage error by 81.8% when comparing with a predictive neural controller and improvement of the tracking voltage error by 87.5% when comparing with an inverse neural controller. Moreover, the oscillation effect in the output voltage is completely eliminated, resulting in a response without any overshoot.

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Section: Pemfc Nonlinear Modelmentioning

confidence: 99%

“…Where 𝐿 represents the polymer membrane's thickness (cm), A is the live cell area (cm 2 ), and ρ 𝑚 is the membrane's specific resistance, which can be calculated using Eq. ( 12) [21]:…”

Section: Pemfc Nonlinear Modelmentioning

confidence: 99%

Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

2023

IJIES

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“…Where 𝑉 𝑐𝑒𝑙𝑙 is a fuel cell's output voltage, 𝑉 𝑜ℎ𝑚 is the ohmic voltage drop brought on by the resistance to protons and electrons passing through the solid electrolyte, 𝑉 𝑎𝑐𝑡 is the voltage drop brought on by anode and cathode activation, and 𝑉 𝑐𝑜𝑛 is the voltage drop brought on by a decrease in the concentration of the reactants' gases or the passage of mass of oxygen and hydrogen. 𝐸 𝑁 is also known as the cell's thermodynamic potential or the reversible voltage and may be calculated as follows [21]:…”

Section: Pem Fuel Cell Nonlinear Modelmentioning

confidence: 99%

Design of a Real-Time Monitoring and Controlling System for the PEM Fuel Cell Model Based on LabVIEW with IoT Technology

2023

IJIES

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In this research, a predictive voltage neural controller and remote monitoring for the nonlinear proton exchange membrane fuel cell (PEMFC) system are implemented in real-time. This paper's major purpose is to precisely and rapidly determine the appropriate hydrogen partial pressure (PH2) control action to improve the fuel cell's nonlinear performance under varying load currents and to enable remote monitoring of the nonlinear PEMFC system based on the internet of things (IoT). The laboratory, virtual instrument engineering workbench (LabVIEW) package is used to demonstrate the real-time performance of the proposed predictive voltage controller applied to the 150-watt PROTIUM PEMFC, which will be used to generate the appropriate amount of hydrogen partial pressure control action that will enter the fuel cell for stabilizing the desired output voltage. The message queuing telemetry transport (MQTT) protocol and a Raspberry Pi 4 acting as a local server are the building blocks upon which the monitoring component of the proposed system is implemented in order to monitor the desired output voltage, the fuel cell output voltage, and PH2. The Raspberry Pi collects the necessary fuel cell data and sends it to the node-red dashboard for monitoring. According to the simulation and the experimental results obtained using the proposed predictive controller on PROTIUM PEMFC, the proposed controller can generate an accurate, prompt, and timely reaction to the hydrogen partial pressure control action to reduce the tracking voltage error and to get rid of the fuel cell output voltage oscillation. The proposed experimental work was compared to the simulation findings to confirm its effectiveness in terms of effectively tracking the desired output voltage, providing a fast response, and achieving the optimal partial pressure of hydrogen. However, in the simulation findings, a voltage error of 0.01 volts was observed without any oscillation. On the other hand, the experimental results indicate a slightly higher voltage error of approximately 0.1 volts, accompanied by oscillations of around ± 0.1 volts.

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Voltage Control of PEM Fuel Cell in a DC Microgrid Using Optimal Artificial Rabbits Algorithm-Based Fractional Order PID Controller

Riad,

Hasanien,

Ullah

et al. 2024

IEEE Access

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Design of Optimum SIMO-PID Neural Voltage-Tracking Controller for Non-Linear Fuel Cell System Based on a Comparative Study Of Various Intelligent Swarm Optimization Algorithms

Cited by 4 publications

References 23 publications

Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

Design of a Real-Time Monitoring and Controlling System for the PEM Fuel Cell Model Based on LabVIEW with IoT Technology

Voltage Control of PEM Fuel Cell in a DC Microgrid Using Optimal Artificial Rabbits Algorithm-Based Fractional Order PID Controller

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