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Proton Exchange Membrane Fuel Cells (PEMFC) suffer from a limited lifespan, which impedes their uses at a large scale. From this point of view, prognostics appears to be a promising activity since the estimation of the Remaining Useful Life (RUL) before a failure occurs allows deciding from mitigation actions at the right time when needed. Prognostics is however not a trivial task: 1) underlying degradation mechanisms cannot be easily measured and modeled, 2) health prediction must be performed with a long enough time horizon to allow reaction. The aim of this paper is to face these problems by proposing a prognostics framework that enables avoiding assumptions on the PEMFC behavior, while ensuring good accuracy on RUL estimates. Developments are based on a particle filtering approach that enables including non-observable states (degradation through time) into physical models. RUL estimates are obtained by considering successive probability distributions of degrading states. The method is applied on 2 data sets, where 3 models of the voltage drop are tested to compare predictions. Results are obtained with an accuracy of 90 hours around the real RUL value (for a 1000 hours lifespan), clearly showing the significance of the proposed approach.

Fuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being competitive, it is not ready for large scale industrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area.

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Prognostics applications on PEMFC are developing these last years. Indeed, taking decision to extend the lifetime of a PEMFC stack based on behavior and remaining useful life predictions is seen as a promising solution to tackle the too short life's issue of PEMFCs. However, the development of prognostics shows some lacks in the literature. Indeed, performing prognostics requires health indicators that reflect the state of health of stack, while being able to interpret them in an industrial context. It is also important to propose criteria to set its end of life. Moreover, to trust any prognostics' application, one should be able to evaluate the performance of its algorithms with respect to standards. To help launching a discussion on these subjects among scientific and industrial actors, this paper addresses some of the issues encountered when performing prognostics of a PEMFC stack. After showing the link between prognostics and decision, this paper proposes guidelines to set the limits of a prognostics approach. The definitions of healthy and degraded modes are discussed as well as how to choose the time instant to perform predictions. Then, three criteria based on the power produced by the stack are proposed as indicators of the state of health of the stack. The definition of the end of life of the stack is also discussed before proposing some criteria to assess the performance of any prognostics algorithm on a PEMFC. Some perspectives of works are also discussed before concluding.

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