PEM fuel cell prognostics using particle filter with model parameter adaptation (bibtex)
by , ,
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.
Reference:
Kimotho, J. K.; Meyer, T.; Sextro, W.: PEM fuel cell prognostics using particle filter with model parameter adaptation. Prognostics and Health Management (PHM), 2014 IEEE Conference on, 2014.
Bibtex Entry:
@INPROCEEDINGS{Kimotho2014a,
  howpublished = {Conference Proceedings},
  author = {Kimotho, James Kuria AND Meyer, Tobias AND Sextro, Walter},
  title = {PEM fuel cell prognostics using particle filter with model parameter
	adaptation},
  booktitle = {Prognostics and Health Management (PHM), 2014 IEEE Conference on},
  year = {2014},
  pages = {1-6},
  month = {June},
  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.},
  doi = {10.1109/ICPHM.2014.7036406},
  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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