Joint particle filters prognostics for PEMFC power prediction at constant current solicitation

Proton Exchange Membrane Fuel Cells (PEMFC) are promising energy converters but still suffer from a too short life duration. Applying Prognostics and Health Management seems to be a great solution to overcome that issue. More precisely, developing prognostics to anticipate and try to avoid failures is a critical challenge. To tackle this problem, a hybrid prognostics is proposed. It aims at predicting the power aging of a PEMFC stack working at constant operating condition and 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 the aging. Consequently, this work propose a new empirical modeling 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 filters framework directly initialized by an automatic parameter estimate process. When sufficient data is available, the prognostics can give very 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.