Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC-Reference-Cited by-同舟云学术

Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC

Published:2023-04 Issue: Volume:336 Page:120821
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Tao Zihan,Zhang Chu^ORCID,Xiong Jinlin,Hu Haowen,Ji Jie^ORCID,Peng Tian,Nazir Muhammad Shahzad

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference35 articles.

1. Degradation model of proton exchange membrane fuel cell based on a novel hybrid method;Chen;Appl Energy,2019

2. Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm;Chen;Appl Energy,2022

3. Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells;Wang;J Power Sources,2020

4. Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models;Liu;Appl Energy,2022

5. Remaining useful life estimation for proton exchange membrane fuel cells using a hybrid method;Liu;Appl Energy,2019

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