Marine Jouin

Degradations analysis and aging modeling for health assessment and prognostics of PEMFC

Applying prognostics to Proton Exchange Membrane Fuel Cell (PEMFC) stacks is a good solution to help taking actions extending their lifetime. However, it requires a great understanding of the degradation mechanisms and failures occurring within the stack. This task is not simple when applied to a PEMFC due to the different levels (stack - cells - components), the different scales and the multiple causes that lead to degradation. To overcome this problem, this work proposes a methodology dedicated to the setting of a framework and a modeling of the aging for prognostics. This methodology is based on a deep literature review and degradation analyses of PEMFC stacks. This analysis allows defining a proper vocabulary dedicated to PEMFC׳s prognostics and health management and a clear limited framework to perform prognostics. Then the degradations review is used to select critical components within the stack, and to define their critical failure mechanisms thanks the proposal of new fault trees. The impact of these critical components and mechanisms on the power loss during aging is included to the model for prognostics. This model is finally validated on four datasets with different mission profiles both for health assessment and prognostics.

Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation

Proton Exchange Membrane Fuel Cells (PEMFC) are promising energy converters, but still suffer from a short life duration. Applying Prognostics and Health Management seems to be a great solution to overcome that issue. But developing prognostics to anticipate and try to avoid failures is a critical challenge. To tackle this problem, a hybrid prognostics approach is proposed. It aims at predicting the power aging of a PEMFC stack working at a constant operating condition and a constant current solicitation. The main difficulties to overcome are the lack of adapted modeling of the aging for prognostics, and the occurrence of disturbances creating recovery phenomena through aging. Consequently, this work proposes a new empirical model for power aging that takes into account these recoveries based on different features extracted from the data. These models are used in a joint particle filter framework directly initialized by an automatic parameter estimate process. When sufficient data are available, the prognostics can give accurate behavior predictions compared to experimentation. Remaining useful life estimates can be given with an error smaller than 5% for a horizon of 500 hours on a life duration of 1750 hours, which is clearly long enough for decision making.

Prognostics of Proton Exchange Membrane Fuel Cell stack in a particle filtering framework including characterization disturbances and voltage recovery

In the perspective of decreasing polluting emissions and developing alternative energies, fuel cells, and more precisely Proton Exchange Membrane Fuel Cells (PEMFC), represent a promising solution. Even if this technology is close to being competitive, it still suffers from too short life duration. As a consequence, prognostic seems to be a great solution to anticipate PEMFC stacks degradation. However, a PEMFC implies multiphysics and multiscale phenomena making the construction of an aging model only based on physics very complex. One solution consists in using a hybrid approach for prognostics combining the use of models and available data. Among these hybrid approaches, particle filtering methods seem to be really appropriate as they offer the possibility to compute models with time varying parameters and to update them all along the prognostics process. But to be efficient, not only should the prognostics system take into account the aging of the stack but also external events influencing this aging. Indeed, some acquisition techniques introduce disturbances in the fuel cell behavior and a voltage recovery can be observed at the end of the characterization process. This paper proposes to tackle this problem. First, PEMFC fuel cells and their complexities are introduced. Then, the impact of characterization of the fuel cell behavior is described. Empirical models are built and introduced in both learning and prediction phases of the prognostics model by combining three particle filters. The new prognostic framework is used to perform remaining useful life estimates and the whole proposition is illustrated with a long term experiment data set of a PEMFC in constant load solicitation and stable operating conditions. Estimates can be given with an error less than 5% for life durations of more than 1000 hours. Finally, the results are compared to a previous work to show that introducing a disturbance modeling can dramatically reduce the uncertainty coming with the predictions.