PEM fuel cell prognostics using particle filter with model parameter adaptation
J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.
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Conference Paper
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Author
Kimotho, James Kuria ;
Meyer, Tobias;
Sextro, WalterLibreCat
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Abstract
Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.
Keywords
ageing;
particle filtering (numerical methods);
proton exchange membrane fuel cells;
remaining life assessment;
PEM fuel cell prognostics;
PHM;
RUL predictions;
accelerated degradation;
adaptive particle filter algorithm;
dynamic loading;
model parameter adaptation;
prognostics and health management;
proton exchange membrane fuel cells;
remaining useful life estimation;
self-healing effect;
Adaptation models;
Data models;
Degradation;
Estimation;
Fuel cells;
Mathematical model;
Prognostics and health management
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Proceedings Title
Prognostics and Health Management (PHM), 2014 IEEE Conference on
Page
1-6
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Cite this
Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: Prognostics and Health Management (PHM), 2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406
Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In Prognostics and Health Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406}, booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on}, author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014}, pages={1–6} }
Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in Prognostics and Health Management (PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
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