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
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 consumpti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/4/890 |
| _version_ | 1797298357900673024 |
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| author | Yurdagül Benteşen Yakut |
| author_facet | Yurdagül Benteşen Yakut |
| author_sort | Yurdagül Benteşen Yakut |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:33:45Z |
| format | Article |
| id | doaj.art-93a396bc59144a0f9d5a10abbd1b67e0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:33:45Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-93a396bc59144a0f9d5a10abbd1b67e02024-02-23T15:15:21ZengMDPI AGEnergies1996-10732024-02-0117489010.3390/en17040890Optimization 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 CellsYurdagül Benteşen Yakut0Electrical & Electronics Engineering Department, Engineering Faculty, Dicle University, 21280 Diyarbakır, TürkiyeIn 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.https://www.mdpi.com/1996-1073/17/4/890PEM fuel cellimproving performancePI/FOPI controlleroptimizationPSOGA |
| spellingShingle | Yurdagül Benteşen Yakut 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 Energies PEM fuel cell improving performance PI/FOPI controller optimization PSO GA |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | PEM fuel cell improving performance PI/FOPI controller optimization PSO GA |
| url | https://www.mdpi.com/1996-1073/17/4/890 |
| work_keys_str_mv | AT yurdagulbentesenyakut optimizationofproportionalintegralpiandfractionalorderproportionalintegralfopiparametersusingparticleswarmoptimizationgeneticalgorithmpsogainadcdcconverterforimprovingtheperformanceofprotonexchangemembranefuelcells |