Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection-Reference-Cited by-同舟云学术

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Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection

Published:2021-09 Issue: Volume:243 Page:114367
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Huo Weiwei,Li Weier,Zhang Zehui,Sun Chao,Zhou Feikun,Gong Guoqing

Funder

National Natural Science Foundation of China

China Scholarship Council

Natural Science Foundation of Guangxi Province

Tianjin Municipal Education Commission

Special Project for Research and Development in Key areas of Guangdong Province

Tianjin Research Innovation Project for Postgraduate Students

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference46 articles.

1. A comprehensive review on hybrid power system for PEMFC-HEV: Issues and strategies;Lü;Energy Convers Manage,2018

2. Real-time optimization energy management strategy for fuel cell hybrid ships considering power sources degradation.;Zhang;IEEEAccess,2020

3. Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship;Chen;Energy,2020

4. Prediction of electrochemical characteristics of practical-size solid oxide fuel cells based on database of unit cell performance;Kishimoto;Appl Energy,2021

5. Experimental and numerical study on catalyst layer of polymer electrolyte membrane fuel cell prepared with diverse drying methods;Talukdar;J Power Sources,2020

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