Aging modeling and lifetime prediction of a proton exchange membrane fuel cell using an extended Kalman filter

TL;DR

This paper introduces a novel methodology combining parametric identification, dynamic modeling, and Extended Kalman Filtering to accurately predict the aging and remaining useful life of Proton Exchange Membrane Fuel Cells under specific operating conditions.

Contribution

The study develops an integrated approach using EKF and Monte Carlo simulations for precise lifetime prediction of PEMFCs, addressing challenges like limiting current density and parameter evolution.

Findings

Accurately predicts voltage degradation and RUL with low relative errors.

Effectively models aging behavior under various operating conditions.

Addresses challenges in parameter identification and breakpoint detection.

Abstract

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.