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Real-Time Estimation of PEMFC Parameters Using a Continuous-Discrete Extended Kalman Filter Derived from a Pseudo Two-Dimensional Model

Author

Listed:
  • Yasser Diab

    (Institut de Recherche en Énergie Électrique de Nantes Atlantique (IREENA, UR 4642), Nantes Université, Centre de Recherche et de Transfert de Technologie (CRTT), 37 Bd de l’Université, B.P. 406, CEDEX, 44602 Saint Nazaire, France)

  • Francois Auger

    (Institut de Recherche en Énergie Électrique de Nantes Atlantique (IREENA, UR 4642), Nantes Université, Centre de Recherche et de Transfert de Technologie (CRTT), 37 Bd de l’Université, B.P. 406, CEDEX, 44602 Saint Nazaire, France)

  • Emmanuel Schaeffer

    (Institut de Recherche en Énergie Électrique de Nantes Atlantique (IREENA, UR 4642), Nantes Université, Centre de Recherche et de Transfert de Technologie (CRTT), 37 Bd de l’Université, B.P. 406, CEDEX, 44602 Saint Nazaire, France)

  • Stéphane Chevalier

    (Arts et Métiers Institute of Technology, I2M UMR CNRS 5295, Bordeaux University, Bâtiment A11, 351 Cours de la Libération, CEDEX, 33405 Talence, France)

  • Adib Allahham

    (Urban Sciences Building, Newcastle University, Newcastle upon Tyne NE4 5TG, UK)

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) are clean energy conversion devices that are widely used in various energy applications. In most applications, the main challenge is accurately estimating the state of health (SoH) of the PEMFCs during dynamic operating conditions. Moreover, their behavior is affected by numerous physical phenomena such as heat and membrane flooding. This paper proposes the design of an observer to estimate the PEMFC parameters. A state-space model is first built from 2D physical equations solved by a finite difference in a discretized space domain. The discretized dynamic model is then used to design an observer based on the continuous-discrete extended Kalman filter. The observer has been validated experimentally and is used to estimate the parameters of a PEMFC under dynamic operating conditions. For several load variations, the results obtained using the proposed observer accurately characterize the dynamic responses of PEMFC in real-time.

Suggested Citation

  • Yasser Diab & Francois Auger & Emmanuel Schaeffer & Stéphane Chevalier & Adib Allahham, 2022. "Real-Time Estimation of PEMFC Parameters Using a Continuous-Discrete Extended Kalman Filter Derived from a Pseudo Two-Dimensional Model," Energies, MDPI, vol. 15(7), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2337-:d:777655
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    References listed on IDEAS

    as
    1. Thomas Mazzoni, 2008. "Computational aspects of continuous–discrete extended Kalman-filtering," Computational Statistics, Springer, vol. 23(4), pages 519-539, October.
    2. Abdin, Z. & Webb, C.J. & Gray, E.MacA., 2016. "PEM fuel cell model and simulation in Matlab–Simulink based on physical parameters," Energy, Elsevier, vol. 116(P1), pages 1131-1144.
    3. Yan Shi & Holger Janßen & Werner Lehnert, 2019. "A Transient Behavior Study of Polymer Electrolyte Fuel Cells with Cyclic Current Profiles," Energies, MDPI, vol. 12(12), pages 1-13, June.
    4. Idoia San Martín & Alfredo Ursúa & Pablo Sanchis, 2014. "Modelling of PEM Fuel Cell Performance: Steady-State and Dynamic Experimental Validation," Energies, MDPI, vol. 7(2), pages 1-31, February.
    5. Tang, Yong & Yuan, Wei & Pan, Minqiang & Li, Zongtao & Chen, Guoqing & Li, Yong, 2010. "Experimental investigation of dynamic performance and transient responses of a kW-class PEM fuel cell stack under various load changes," Applied Energy, Elsevier, vol. 87(4), pages 1410-1417, April.
    6. Yasser Diab & François Auger & Emmanuel Schaeffer & Moutassem Wahbeh, 2017. "Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter," Energies, MDPI, vol. 10(8), pages 1-19, July.
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