Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914820903665664 |
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| author | Pene, Serigne Daouda Picot, Antoine Gamboa, Fabrice Savy, Nicolas Turpin, Christophe Jaafar, Amine |
| author_facet | Pene, Serigne Daouda Picot, Antoine Gamboa, Fabrice Savy, Nicolas Turpin, Christophe Jaafar, Amine |
| contents | This article presents a methodology that aims to model and to provide predictive capabilities for the lifetime of Proton Exchange Membrane Fuel Cell (PEMFC). The approach integrates parametric identification, dynamic modeling, and Extended Kalman Filtering (EKF). The foundation is laid with the creation of a representative aging database, emphasizing specific operating conditions. Electrochemical behavior is characterized through the identification of critical parameters. The methodology extends to capture the temporal evolution of the identified parameters. We also address challenges posed by the limiting current density through a differential analysis-based modeling technique and the detection of breakpoints. This approach, involving Monte Carlo simulations, is coupled with an EKF for predicting voltage degradation. The Remaining Useful Life (RUL) is also estimated. The results show that our approach accurately predicts future voltage and RUL with very low relative errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter Pene, Serigne Daouda Picot, Antoine Gamboa, Fabrice Savy, Nicolas Turpin, Christophe Jaafar, Amine Applications Numerical Analysis Systems and Control Computation Methodology This article presents a methodology that aims to model and to provide predictive capabilities for the lifetime of Proton Exchange Membrane Fuel Cell (PEMFC). The approach integrates parametric identification, dynamic modeling, and Extended Kalman Filtering (EKF). The foundation is laid with the creation of a representative aging database, emphasizing specific operating conditions. Electrochemical behavior is characterized through the identification of critical parameters. The methodology extends to capture the temporal evolution of the identified parameters. We also address challenges posed by the limiting current density through a differential analysis-based modeling technique and the detection of breakpoints. This approach, involving Monte Carlo simulations, is coupled with an EKF for predicting voltage degradation. The Remaining Useful Life (RUL) is also estimated. The results show that our approach accurately predicts future voltage and RUL with very low relative errors. |
| title | Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter |
| topic | Applications Numerical Analysis Systems and Control Computation Methodology |
| url | https://arxiv.org/abs/2406.01259 |