Particle Filter Based Prognostics of PEM Fuel Cell Under Constant Load

Mayank Shekhar JHA, Mathieu Bressel, Belkacem Ould-Bouamama, Genevieve Dauphin-Tanguy, Mickael Hilairet, Daniel Hissel

Abstract


This paper develops an efficient solution towards the prognostics of industrial PEMFC. It involves employment of an efficient multi-energetic model suited for diagnostics and prognostics, developed in Bond Graph framework. The Electrical-Electrochemical (EE) part constitutes the main focus for the problem of prognostics, wherein deviation of the global resistance and limiting current inspires a statistical linear degradation model (DM), under constant current load conditions. The benefits of Particle Filters (PF) is integrated with the BG model derived Analytical Redundancy Relations (ARRs), for prognostics of the electrical-electrochemical (EE) part. The prognostic problem is formulated as the joint state-parameter estimation problem in Particle Filter framework. Using PF algorithms, in probabilistic terms,  estimation of State of Health (SOH) is obtained along with the estimation of the associated parameter that influences the rate of degradation. A simplified variance adaptation scheme is employed to ameliorate the accuracy of remaining useful life (RUL) predictions.  Influence of variance adaptation on SOH estimation as well as RUL prediction is assessed.  It is shown that a proportional type of variance control leads to better accuracy in RUL predictions accompanied with precise confidence bounds. As the degradation data is obtained from a real industrial PEMFC, the economic viability of this approach for prognostics of PEMFC is significantly high.

Keywords


Hydrogen and Fuel Cell; Prognostics; Bond Graph; Particle Filters;Proton Exchange Membrane Fuel Cell; Remaining Useful Life;renewable energy;clean energy

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v6i2.3694.g6830

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