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Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter

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Titel: Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter
Autoren: Pene, Serigne Daouda, Picot, Antoine, Gamboa, Fabrice, Savy, Nicolas, Turpin, Christophe, Jaafar, Amine
Weitere Verfasser: PENE, Serigne Daouda
Quelle: Mathematics and Computers in Simulation. 234:151-168
Publication Status: Preprint
Verlagsinformationen: Elsevier BV, 2025.
Publikationsjahr: 2025
Schlagwörter: FOS: Computer and information sciences, remaining useful life, extended Kalman filter, time change detection, Systems and Control (eess.SY), Statistics - Applications, Electrical Engineering and Systems Science - Systems and Control, Statistics - Computation, 7. Clean energy, Methodology (stat.ME), [STAT.AP] Statistics [stat]/Applications [stat.AP], FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), Mathematics - Numerical Analysis, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Monte Carlo simulation, Statistics - Methodology, Computation (stat.CO), aging, Numerical Analysis (math.NA), PEM fuel cell, [INFO.INFO-NA] Computer Science [cs]/Numerical Analysis [cs.NA], [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, lifetime prediction, hybrid approach, Hydrogen
Beschreibung: 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.
Publikationsart: Article
Dateibeschreibung: application/pdf
Sprache: English
ISSN: 0378-4754
DOI: 10.1016/j.matcom.2025.02.022
DOI: 10.48550/arxiv.2406.01259
Zugangs-URL: http://arxiv.org/abs/2406.01259
https://hal.science/hal-04583935v2
https://doi.org/10.1016/j.matcom.2025.02.022
https://hal.science/hal-04583935v2/document
Rights: CC BY
arXiv Non-Exclusive Distribution
Dokumentencode: edsair.doi.dedup.....5cb63d937246b9c47e338d5c2928ea1e
Datenbank: OpenAIRE
Beschreibung
Abstract: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.
ISSN:03784754
DOI:10.1016/j.matcom.2025.02.022