Diagnosis of PEM Fuel Cell System Based on Electrochemical Impedance Spectroscopy and Deep Learning Method

In this article, a new fault diagnosis framework for proton exchange membrane fuel cells (PEMFCs) based on electrochemical impedance spectroscopy (EIS) and the deep learning method is proposed. Specifically, this work employs the PEMFC knowledge to drive the training process of the deep learning net...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2024-01, Vol.71 (1), p.657-666
Main Authors: Lv, Jianfeng, Kuang, Jiyuan, Yu, Zhongliang, Sun, Guanghui, Liu, Jianxing, Leon, Jose I.
Format: Article
Language:English
Subjects:
Algorithms
Complex neural network (C-NN)
Data mining
Data models
Deep learning
Electrochemical impedance spectroscopy
Equivalent circuits
Fault diagnosis
Feature extraction
Impedance
Machine learning
Neural networks
Parameter identification
Proton exchange membrane fuel cells
proton exchange membrane fuel cells (PEMFCs)
Spectrum analysis
Teaching methods
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this article, a new fault diagnosis framework for proton exchange membrane fuel cells (PEMFCs) based on electrochemical impedance spectroscopy (EIS) and the deep learning method is proposed. Specifically, this work employs the PEMFC knowledge to drive the training process of the deep learning network, which makes it possible to improve fault diagnosis performance by deep learning algorithms with a limited scale of actual measured EIS data. A pretraining network is developed to predict the equivalent circuit model (ECM) parameters, which could reduce the time consumption of ECM parameter identification. Besides, considering that the ECM parameters are susceptible to significant changes due to nonfault operation, a fine-tuning network is designed to generate robust diagnosis features, which could support the proposed framework working in different environments. Moreover, the complex neural networks are adopted in the proposed framework to extract features from EIS data, which is composed of complex impedances. Finally, a new evaluation metric PScore is proposed to assess the performance of the diagnosis framework from the perspective of practical applications. The experiments are performed to demonstrate the effectiveness of each component in the framework, and the proposed algorithm has significant improvements in fault diagnosis performance and computational efficiency over traditional algorithms.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2023.3241404