Trend‐focused dynamic degradation prediction based on echo state networks in automotive fuel cells

Meiling Yue (1) , Xin Zhang (2) , Teng Teng (3) , Jianwen Meng (4)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 ICAR - Interactions, Corpus, Apprentissages, Représentations
3 Chongqing University [Chongqing]
4 ESTACA'Lab - ESTACA'Lab

Abstract Fuel cell technology is a promising alternative to traditional internal combustion engines in various applications, especially in transportation applications. However, the high cost and limited lifetime of fuel cells have hindered their widespread commercialization. Accurately predicting fuel cell lifetime is crucial for reducing the cost of ownership, ensuring safety, and promoting the adoption of this technology. The objective of the present work is to develop a tool that is able to estimate the lifespan of a proton exchange membrane fuel cell and to predict its behavior to anticipate failures. Therefore, this paper contributes to proposing a multi‐input time‐series prediction network based on an echo state network, which takes the future current into consideration. A degradation trend extraction method is proposed in this paper and the remaining useful life of the fuel cell is predicted. Results have shown that the proposed methods in both short‐term and long‐term prediction have achieved satisfying prediction accuracy.

Mots clés: automotive degradation trend extraction echo state network performance prediction proton exchange membrane fuel cell; automotive; degradation trend extraction; echo state network; performance prediction; proton exchange membrane fuel cell

Domaines: Sciences de l'ingénieur [physics]

Meiling Yue, Xin Zhang, Teng Teng, Jianwen Meng. Trend‐focused dynamic degradation prediction based on echo state networks in automotive fuel cells. Energy Science & Engineering, 2024, ⟨10.1002/ese3.1669⟩. ⟨hal-04540309⟩