A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems
Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to thi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/5541124 |
| _version_ | 1826992813499219968 |
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| author | Ezzeddine Touti Mouloud Aoudia C. H. Hussaian Basha Ibrahim Mohammed Alrougy |
| author_facet | Ezzeddine Touti Mouloud Aoudia C. H. Hussaian Basha Ibrahim Mohammed Alrougy |
| author_sort | Ezzeddine Touti |
| collection | DOAJ |
| description | Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller. |
| first_indexed | 2024-04-24T08:24:12Z |
| format | Article |
| id | doaj.art-e6f5b7f2bfe641d8866c4ac870175263 |
| institution | Directory Open Access Journal |
| issn | 2050-7038 |
| language | English |
| last_indexed | 2025-02-18T08:55:24Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj.art-e6f5b7f2bfe641d8866c4ac8701752632024-11-02T23:56:11ZengHindawi-WileyInternational Transactions on Electrical Energy Systems2050-70382024-01-01202410.1155/2024/5541124A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV SystemsEzzeddine Touti0Mouloud Aoudia1C. H. Hussaian Basha2Ibrahim Mohammed Alrougy3Department of Electrical EngineeringDepartment of Industrial EngineeringElectric Vehicle R&D LabKing Abdulaziz City for Science and Technology (KACST)Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.http://dx.doi.org/10.1155/2024/5541124 |
| spellingShingle | Ezzeddine Touti Mouloud Aoudia C. H. Hussaian Basha Ibrahim Mohammed Alrougy A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems International Transactions on Electrical Energy Systems |
| title | A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems |
| title_full | A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems |
| title_fullStr | A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems |
| title_full_unstemmed | A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems |
| title_short | A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems |
| title_sort | novel design and analysis adaptive hybrid anfis mppt controller for pemfc fed ev systems |
| url | http://dx.doi.org/10.1155/2024/5541124 |
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