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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Bibliographic Details
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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