Fuel Cell Remaining Useful Life Prediction and Uncertainty Quantification Under an Automotive Profile
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Fuel Cell Remaining Useful Life Prediction and Uncertainty Quantification Under an Automotive Profile |
| Type de publication | Conference Paper |
| Year of Publication | 2016 |
| Auteurs | Bressel M, Hilairet M, Hissel D, Bouamama BQuid |
| Conference Name | PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY |
| Publisher | IEEE Ind Elect Soc; Inst Elect & Elect Engineers |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5090-3474-1 |
| Mots-clés | Extended Kalman filter, PEM fuel cell, Remaining useful life, uncertainty quantification |
| Résumé | Being a very efficient and clean energy converter, a proton exchange membrane fuel cell may be utilized to power an electrical vehicle efficiently. Nevertheless, degradation mechanisms affect the lifespan of this electrochemical converter. Consequently, the estimation of the State of Health and Remaining Useful Life have been the subject of numerous researches in the past years. However, most of the methods available considering fuel cell prognostic do not allow the uncertainty quantification of the estimation that can be implemented online considering the calculation cost. As a novelty, the present article depicts a prognostic algorithm based on an Extended Kalman Filter. This observer estimates the State of Health, the speed of the degradation and also provides the estimation uncertainty. Then, an Inverse First Order Reliability Method computes the Remaining Useful Life with a 90% confidence interval based on the estimation of the observer. This method is applied on a 175 hours data set subsequent of an experiment on an 8-cells fuel cell stack that was subjected to an automotive power profile. |