Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

Published:2019-10 Issue: Volume:251 Page:113381
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Hong Jichao,Wang Zhenpo,Yao Yongtao

Funder

China NSF

Chinese Scholarship Council

Publisher

Elsevier BV

Subject

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

Reference44 articles.

1. A review of battery electric vehicle technology and readiness levels;Andwari;Renew Sustain Energy Rev,2017

2. Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles;Capasso;Appl Energy,2014

3. Experimental performance assessment of pb, li [nicomn] o 2 and lifepo 4 batteries for road vehicles;Veneri,2012

4. Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles;Yao;J Power Sources,2015

5. Voltage fault diagnosis and prognosis of battery systems based on entropy and z-score for electric vehicles;Wang;Appl Energy,2017

Cited by 234 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transfer-driven prognosis from battery cells to packs: An application with adaptive differential model decomposition;Applied Energy;2025-01

2. Expert deep learning techniques for remaining useful life prediction of diverse energy storage Systems: Recent Advances, execution Features, issues and future outlooks;Expert Systems with Applications;2024-12

3. Hydrogen production techniques and use of hydrogen in internal combustion engine: A comprehensive review;Fuel;2024-12

4. Empowering lithium-ion battery manufacturing with big data: Current status, challenges, and future;Journal of Power Sources;2024-12

5. Unsupervised adversarial and cycle consistent feature extraction network for intelligent fault diagnosis;Applied Soft Computing;2024-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3