2024/06/03 by Serigne Daouda Pene, Pene, Serigne Daouda, Antoine Picot +9
Energy · Engineering · #Applications (stat.AP) #Computation (stat.CO) #Electric and Hybrid Vehicle Technologies #Electrocatalysts for Energy Conversion #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fuel Cells and Related Materials #Methodology (stat.ME) #Numerical Analysis (math.NA) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.01259
openalex publication_date 2024/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.