PEMFC Residual Life Prediction Using Sparse Autoencoder-Based Deep Neural Network

In the cause of working out the challenge of remaining life prediction (RUL) of proton exchange membrane fuel cell (PEMFC) under dynamic operating conditions, this article proposes a PEMFC RUL forecast technique based on the sparse autoencoder (SAE) and deep neural network (DNN). The method extracts...

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Published in:IEEE transactions on transportation electrification Vol. 5; no. 4; pp. 1279 - 1293
Main Authors: Liu, Jiawei, Li, Qi, Han, Ying, Zhang, Guorui, Meng, Xiang, Yu, Jiaxi, Chen, Weirong
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aging
Artificial neural networks
Deep learning
deep neural network (DNN)
Feature extraction
Fuel cells
Life prediction
Load modeling
Neural networks
Prediction algorithms
Predictive models
proton exchange membrane fuel cell (PEMFC)
Proton exchange membrane fuel cells
residual service life prediction
Root-mean-square errors
sparse autoencoder (SAE)
Support vector machines
Training
ISSN:2332-7782, 2577-4212, 2332-7782
Online Access:Get full text
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Summary:In the cause of working out the challenge of remaining life prediction (RUL) of proton exchange membrane fuel cell (PEMFC) under dynamic operating conditions, this article proposes a PEMFC RUL forecast technique based on the sparse autoencoder (SAE) and deep neural network (DNN). The method extracts the data set from the original experimental data at intervals periods of one hour to realize datum reconstruction. The Gaussian-weighted moving average filter is used to smooth noisy data (voltage and current). The smoothed filtered power output signal of the stack is extracted as an aging indicator. The SAE is used to extract the prediction features automatically, and the DNN is applied to realize the RUL prediction. The proposed method is experimentally verified using 127 369 experimental data. The effectiveness of the novel method is verified by three different training sets and test set configurations. The experimental results reveal that the novel approach has the best prediction effectiveness when the training set length is set to 500 h. At this point, the prediction accuracy can reach 99.68%. The mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) are minimum values, which are 0.2035, 0.1121, and 0.3348, respectively. The superiority and effectiveness of the proposed approach are further validated by comparison with the K-nearest neighbor and support vector regression machine. The proposed approach can be appropriate for the prediction of the RUL of PEMFC under dynamic conditions.
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ISSN:2332-7782
2577-4212
2332-7782
DOI:10.1109/TTE.2019.2946065