Online Modeling of a Fuel Cell System for an Energy Management Strategy Design
An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/14/3713 |
| _version_ | 1797562052744577024 |
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| author | Mohsen Kandidayeni Alvaro Macias Loïc Boulon João Pedro F. Trovão |
| author_facet | Mohsen Kandidayeni Alvaro Macias Loïc Boulon João Pedro F. Trovão |
| author_sort | Mohsen Kandidayeni |
| collection | DOAJ |
| description | An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation. |
| first_indexed | 2024-03-10T18:23:08Z |
| format | Article |
| id | doaj.art-ced7011d5a874de2aff31013fbf9efaf |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:23:08Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ced7011d5a874de2aff31013fbf9efaf2023-11-20T07:14:45ZengMDPI AGEnergies1996-10732020-07-011314371310.3390/en13143713Online Modeling of a Fuel Cell System for an Energy Management Strategy DesignMohsen Kandidayeni0Alvaro Macias1Loïc Boulon2João Pedro F. Trovão3Department of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaDepartment of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaDepartment of Electrical & Computer Engineering, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, CanadaDepartment of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaAn energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.https://www.mdpi.com/1996-1073/13/14/3713control strategyhybrid vehicleKalman filtermaximum power point trackermetaheuristic optimizationonline parameters estimation |
| spellingShingle | Mohsen Kandidayeni Alvaro Macias Loïc Boulon João Pedro F. Trovão Online Modeling of a Fuel Cell System for an Energy Management Strategy Design Energies control strategy hybrid vehicle Kalman filter maximum power point tracker metaheuristic optimization online parameters estimation |
| title | Online Modeling of a Fuel Cell System for an Energy Management Strategy Design |
| title_full | Online Modeling of a Fuel Cell System for an Energy Management Strategy Design |
| title_fullStr | Online Modeling of a Fuel Cell System for an Energy Management Strategy Design |
| title_full_unstemmed | Online Modeling of a Fuel Cell System for an Energy Management Strategy Design |
| title_short | Online Modeling of a Fuel Cell System for an Energy Management Strategy Design |
| title_sort | online modeling of a fuel cell system for an energy management strategy design |
| topic | control strategy hybrid vehicle Kalman filter maximum power point tracker metaheuristic optimization online parameters estimation |
| url | https://www.mdpi.com/1996-1073/13/14/3713 |
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