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.

翻译：本文提出一种旨在建模并预测质子交换膜燃料电池寿命的方法论。该方法整合了参数辨识、动态建模与扩展卡尔曼滤波技术。研究首先构建了具有代表性的老化数据库，重点关注特定运行工况。通过辨识关键参数来表征电化学行为。该方法进一步扩展到捕捉已辨识参数的时间演化规律。针对极限电流密度带来的挑战，我们采用基于微分分析的建模技术与断点检测方法进行处理。该方案结合蒙特卡洛模拟，并与扩展卡尔曼滤波耦合以实现电压衰减预测。同时估算了剩余使用寿命。结果表明，本方法能准确预测未来电压与剩余使用寿命，且相对误差极低。