A Novel Hybrid MPPT Controller Based on Bond Graph and Fuzzy Logic in Proton Exchange Membrane Fuel Cell System: Experimental Validation

Badoud, Abd Essalam; Mekhilef, Saad; Bouamama, Belkacem Ould

doi:10.1007/s13369-021-06096-3

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Research in the international community does not stop using methods and algorithms inspired by nature to solve real problems of industry or others, such as the use of artificial intelligence: Fuzzy logic (Badoud et al, 2022) for the design of a bond graph model-based controller, artificial neural networks (Qin et al, 2014) for the control of magnetic levitation, and the use of meta-heuristic optimization algorithms, such as the grey wolf optimizer (Faris et al, 2018) (Hatta et al, 2019), the PSO (Laldingliana et al, 2022) for the optimization of a fractional order PID controller of an electromagnetic suspension, which is also subject to the application of other meta-heuristic methods, Manta Ray Foraging optimization (Ekinci et al, 2022), and in comparison between several optimization methods (Firefly, Grasshopper and ABC) for the concept of a PID regulator in (Gupta et al, 2023).…”

Section: Firefly Optimisation Control For Control Parametersmentioning

confidence: 99%

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Dif

Benmahammed

2023

J. Eng. Exact Sci.

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This paper focuses on the modelling and control of an electromagnetic suspension using the bond graph tool. This technique combines graphical and relational aspects to establish connections between bond graph elements, particularly in deducing junction relationships and employing bicausal inversion for trajectory tracking controller design. To determine the controller parameters, we utilized a meta-heuristic method called Firefly for optimization, thus avoiding the need for trial and error. The simulation was conducted using 20-Sim software and a Matlab/Simulink script, which yielded improved results. This paper presents an initial contribution that demonstrates the integration of meta-heuristic optimization and the bond graph tool for parameter selection in 20-sim simulation software.

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Section: Firefly Optimisation Control For Control Parametersmentioning

confidence: 99%

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Dif

Benmahammed

2023

J. Eng. Exact Sci.

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“…As traditional energy supplies are depleted, it is critical to develop alternative energy sources that can be stored and transported such as hydrocarbons. In this sense, hydrogen is a significant contender, despite the fact that it is merely an energy transporter and not a main resource [3]. Energy storage is a fundamental challenge when integrating renewable energy sources into an electrical grid, due to their intermittent nature and the need for a steady power supply.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning

et al. 2023

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This paper presents a systematic and comprehensive mathematical model for alkaline water electrolyzer cells, which can be used for simulation and analysis. The model accounts for factors such as gas evolution reactions, dissolution of gases in the electrolyte, bubble formation, and charge transport. It is based on a numerical two-phase model using the Euler-Euler approach, which has been validated against experimental data for various current densities. The study compares the impact of varying potassium hydroxide (KOH) concentration, separator porosity, and electrolyte flow rates on two-phase flow and bubble coverage. Therefore, the electrolyte in the cell consists of a solution of potassium hydroxide in water. The formation of gas bubbles at the electrodes decreases the electrolyte’s ionic conductivity. Additionally, the presence of these bubbles on the electrode surfaces reduces the available surface area for electrochemical reactions, leading to an increase in the overpotential at a given current density. Furthermore, this paper demonstrates how a neural network and ensembled tree model can predict hydrogen production rates in an alkaline water electrolysis process. The trained neural network accurately predicted the hydrogen production rates, indicating the potential of using neural networks for optimization and control of alkaline water electrolysis processes. The model has an average R-squared value of 0.98, indicating a good fit to the data. A new method of describing bubble transfer, “bubble diffusion,” is introduced to improve performance and reduce costs. The model is solved using COMSOL Multi physics 6.0. The machine learning models in this study were built, trained, and tested using MATLAB software R2020a.

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“…Future energy sources will likely use fuel cells, because the usage of fossil fuels for power has had so many negative effects, and interest in fuel cells has grown over the past ten years. [2][3][4] Some of these include catastrophic pollution, widespread resource extraction around the globe, and political dominance over nations with abundant natural resources. It is essential to create an innovative power source that is fuel efficient, emits no pollution, and has an endless supply of fuel.…”

mentioning

confidence: 99%

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

Injarapu Edukondala Sanyasi,

Sambana,

Chilukoti

et al. 2023

Energy Tech

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The proton exchange membrane fuel cell (PEMFC), one of the many fuel cell varieties on the market, is distinguished by its low temperatures of operation, high reliability, and extended lifespan. The fuel cell system's maximum output can only be achieved at one specific operating point under varying operating circumstances. Hence, it is essential to get the most power possible from the PEMFC for improved functioning and optimal exploitation of grid systems. Thus, the design of a maximum power point tracking (MPPT) controller for a PEMFC power system using an amplified salp swarm optimization (AS2O) algorithm is focused on. Here, an interleaved Luo converter is also used to produce a regulated output voltage with less switching stress and frequency. Moreover, the proposed work intends to effectively satisfy the energy demand of grid systems by obtaining the maximum electrical energy from the PEMFCs with less harmonics and low system complexity. The simulation and performance results of the proposed AS2O‐MPPT controlling model are validated and compared using several parameters using MATLAB/Simulink tool. Using the proposed controlling technique, the total harmonics distortion is reduced up to 1.16%, with the settling time of 0.0062 s, rise time of 0.0010 s, and peak overshoot of 0.255 W.

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A Novel Hybrid MPPT Controller Based on Bond Graph and Fuzzy Logic in Proton Exchange Membrane Fuel Cell System: Experimental Validation

Cited by 8 publications

References 40 publications

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization

Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

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