  This article presents a methodology that aims to model and to provide
predictive capabilities for the lifetime of Proton Exchange Membrane Fuel Cell
(PEMFC). The approach integrates parametric identification, dynamic modeling,
and Extended Kalman Filtering (EKF). The foundation is laid with the creation
of a representative aging database, emphasizing specific operating conditions.
Electrochemical behavior is characterized through the identification of
critical parameters. The methodology extends to capture the temporal evolution
of the identified parameters. We also address challenges posed by the limiting
current density through a differential analysis-based modeling technique and
the detection of breakpoints. This approach, involving Monte Carlo simulations,
is coupled with an EKF for predicting voltage degradation. The Remaining Useful
Life (RUL) is also estimated. The results show that our approach accurately
predicts future voltage and RUL with very low relative errors.
