Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model
Abstract Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a...
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| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78001-5 |
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| author | Nermin M. Salem Mohamed A. M. Shaheen Hany M. Hasanien |
| author_facet | Nermin M. Salem Mohamed A. M. Shaheen Hany M. Hasanien |
| author_sort | Nermin M. Salem |
| collection | DOAJ |
| description | Abstract Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions. |
| format | Article |
| id | doaj-art-4f2bc6258b8c4f3f87cfa5cf8d86db51 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4f2bc6258b8c4f3f87cfa5cf8d86db512024-11-17T12:18:55ZengNature PortfolioScientific Reports2045-23222024-11-0114111910.1038/s41598-024-78001-5Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell modelNermin M. Salem0Mohamed A. M. Shaheen1Hany M. Hasanien2Faculty of Engineering and Technology, Future University in EgyptFaculty of Engineering and Technology, Future University in EgyptElectrical Power and Machines Department, Faculty of Engineering, Ain Shams UniversityAbstract Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions.https://doi.org/10.1038/s41598-024-78001-5Clean energyPEMFCReinforcement learning |
| spellingShingle | Nermin M. Salem Mohamed A. M. Shaheen Hany M. Hasanien Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model Scientific Reports Clean energy PEMFC Reinforcement learning |
| title | Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| title_full | Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| title_fullStr | Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| title_full_unstemmed | Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| title_short | Novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| title_sort | novel reinforcement learning technique based parameter estimation for proton exchange membrane fuel cell model |
| topic | Clean energy PEMFC Reinforcement learning |
| url | https://doi.org/10.1038/s41598-024-78001-5 |
| work_keys_str_mv | AT nerminmsalem novelreinforcementlearningtechniquebasedparameterestimationforprotonexchangemembranefuelcellmodel AT mohamedamshaheen novelreinforcementlearningtechniquebasedparameterestimationforprotonexchangemembranefuelcellmodel AT hanymhasanien novelreinforcementlearningtechniquebasedparameterestimationforprotonexchangemembranefuelcellmodel |