Fractional PI<sup>λ</sup>D<sup>µ</sup> controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm

Routh, Avijit; Ghosh, Sankhadeep; Rahaman, Mehabub; Ghosh, Avijit

doi:10.1080/00194506.2022.2133641

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The output voltage of a Proton Exchange Membrane Fuel Cell (PEMFC) is significantly influenced by many operating variables, including partial pressure of H 2 , partial pressure of O 2 , stack temperature, mass flow rate of hydrogen and oxygen, humidity, and load fluctuation [21]. The polarization curve is generally used to illustrate the static characteristics that are associated with a fuel cell.…”

Section: Mathematical Expression Of Fuel Cellmentioning

confidence: 99%

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Ghosh,

Routh,

Hembrem

et al. 2024

Eng. Res. Express

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Proton Exchange Membrane Fuel Cells (PEMFCs) provide a reliable, pollution-free, sustainable, and stable power generating alternative to non-renewable resources, and they do not self-discharge. Proton exchange membrane fuel cells (PEMFCs) necessitate correct parameter estimates for effective investigation, modelling, and development of efficient fuel cells, highlighting the importance of exact modelling for successful use in many industries. The objective of this research is to estimate the parameters of PEMFC using a modified algorithm derived from the Ant Colony Optimization (ACO) meta-heuristic algorithm. In order to provide justification for the algorithm, it is initially benchmarked against 10 functions. The study compares the outcomes of PEMFC parameter estimation through the Dynamic Ant Colony Optimisation (DACO) algorithm with several other meta-heuristic algorithms such as Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), Differential Evolution (DE) algorithm, Artificial Bee Colony (ABC), and a hybrid algorithm known as Grey Wolf Optimisation - Cuckoo Search (GWOCS). The suggested algorithm's performance evaluation is based on minimising the Square Error (SSE). The proposed optimization algorithm exhibits better performance compared to alternative meta-heuristic algorithms due to its minimal SSE value. Statistical error analysis and non-parametric tests were used to evaluate the performance and efficacy of the modified algorithm based on the Ballard Mark V datasheet. The convergence curves of DACO demonstrate a faster pace of convergence compared to the other algorithms being compared.

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Section: Mathematical Expression Of Fuel Cellmentioning

confidence: 99%

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Ghosh,

Routh,

Hembrem

et al. 2024

Eng. Res. Express

Self Cite

View full text Add to dashboard Cite

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Multi objective optimization using artificial neural network to maximize the power output of PEMFCs

Ghosh,

Routh,

Rahaman

et al. 2024

Indian Chemical Engineer

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Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

Benteşen Yakut

2024

Energies

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In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance.

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Fractional PI^λD^µ controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm

Cited by 3 publications

References 25 publications

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Multi objective optimization using artificial neural network to maximize the power output of PEMFCs

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

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Fractional PIλDµ controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm

Cited by 3 publications

References 25 publications

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Multi objective optimization using artificial neural network to maximize the power output of PEMFCs

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

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Fractional PI^λD^µ controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm