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A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems

This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integ...

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Main Authors: Kanouni B., Badoud A.E., Mekhilef S., Elsanabary A., Bajaj M., Zaitsev I.
Other Authors: 57345129000
Format: Article
Published: Nature Research 2025
Subjects:
proton
algorithm
article
conductance
controlled study
duration
electric potential
fuel
fuzzy logic
polarization
pressure
simulator
temperature
velocity
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spelling my.uniten.dspace-361762025-03-03T15:41:30Z A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems Kanouni B. Badoud A.E. Mekhilef S. Elsanabary A. Bajaj M. Zaitsev I. 57345129000 36805436300 57928298500 57221120034 57189048184 59198121200 proton algorithm article conductance controlled study duration electric potential fuel fuzzy logic polarization pressure simulator temperature velocity This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC. The maximum point (P-I) of the PEMFC polarization curve is determined, followed by the selection of the reference current. A predictive current control technique employs the reference current to ensure the voltage balance of the output capacitor in the three-level converter. The hardware-in-the-loop system utilizes a real-time and high-speed simulator, specifically the PLECS RT Box 1, to obtain the findings. The computational cost of the overall system is rather low, making it feasible to construct using PLECS RT Box 1. The new MPPT algorithm quickly finds the maximum power point (MPP) and balances the voltage of capacitors in a number of different proton exchange membrane fuel cells. The suggested MPPT technique has been verified to demonstrate rapid tracking of the maximum power point (MPP) location, as well as precise balancing of capacitor voltage and robustness to environmental variations. This approach was tested and found to outperform conventional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IC) in terms of tracking duration, precision, and voltage balancing, achieving a 15% reduction in tracking duration, a 5% deviation from the MPP value for voltage, and superior stability under changing temperature and pressure. ? The Author(s) 2024. Final 2025-03-03T07:41:30Z 2025-03-03T07:41:30Z 2024 Article 10.1038/s41598-024-78030-0 2-s2.0-85209105764 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209105764&doi=10.1038%2fs41598-024-78030-0&partnerID=40&md5=83d71d132c560604692f7d29be3fd57c https://irepository.uniten.edu.my/handle/123456789/36176 14 1 27166 All Open Access; Gold Open Access Nature Research Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic proton
algorithm
article
conductance
controlled study
duration
electric potential
fuel
fuzzy logic
polarization
pressure
simulator
temperature
velocity
spellingShingle proton
algorithm
article
conductance
controlled study
duration
electric potential
fuel
fuzzy logic
polarization
pressure
simulator
temperature
velocity
Kanouni B.
Badoud A.E.
Mekhilef S.
Elsanabary A.
Bajaj M.
Zaitsev I.
A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
description This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC. The maximum point (P-I) of the PEMFC polarization curve is determined, followed by the selection of the reference current. A predictive current control technique employs the reference current to ensure the voltage balance of the output capacitor in the three-level converter. The hardware-in-the-loop system utilizes a real-time and high-speed simulator, specifically the PLECS RT Box 1, to obtain the findings. The computational cost of the overall system is rather low, making it feasible to construct using PLECS RT Box 1. The new MPPT algorithm quickly finds the maximum power point (MPP) and balances the voltage of capacitors in a number of different proton exchange membrane fuel cells. The suggested MPPT technique has been verified to demonstrate rapid tracking of the maximum power point (MPP) location, as well as precise balancing of capacitor voltage and robustness to environmental variations. This approach was tested and found to outperform conventional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IC) in terms of tracking duration, precision, and voltage balancing, achieving a 15% reduction in tracking duration, a 5% deviation from the MPP value for voltage, and superior stability under changing temperature and pressure. ? The Author(s) 2024.
author2 57345129000
author_facet 57345129000
Kanouni B.
Badoud A.E.
Mekhilef S.
Elsanabary A.
Bajaj M.
Zaitsev I.
format Article
author Kanouni B.
Badoud A.E.
Mekhilef S.
Elsanabary A.
Bajaj M.
Zaitsev I.
author_sort Kanouni B.
title A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
title_short A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
title_full A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
title_fullStr A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
title_full_unstemmed A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems
title_sort fuzzy-predictive current control with real-time hardware for pem fuel cell systems
publisher Nature Research
publishDate 2025
_version_ 1825816054088073216
score 13.244413

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