Forecasting battery capacity and power degradation with multi-task learning-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Forecasting battery capacity and power degradation with multi-task learning

Published:2022-12 Issue: Volume:53 Page:453-466
ISSN:2405-8297
Container-title:Energy Storage Materials
language:en
Short-container-title:Energy Storage Materials

Author:

Li Weihan^ORCID,Zhang Haotian,van Vlijmen Bruis,Dechent Philipp,Sauer Dirk Uwe

Funder

RWTH Aachen University

Stanford University

Bundesministerium für Bildung und Forschung

Bundesministerium für Wirtschaft und Energie

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,General Materials Science,Renewable Energy, Sustainability and the Environment

Reference54 articles.

1. Cycling degradation of an automotive LiFePO4 lithium-ion battery;Zhang;J. Power Sources,2011

2. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks;Li;J. Power Sources,2021

3. Nonlinear health evaluation for lithium-ion battery within full-lifespan;You;J. Energy Chem.,2022

4. Theory of SEI formation in rechargeable batteries: capacity fade, accelerated aging and lifetime prediction;Pinson;J. Electrochem. Soc.,2013

5. Lithium plating in a commercial lithium-ion battery – a low-temperature aging study;Petzl;J. Power Sources,2015

Cited by 42 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. Predict the lifetime of lithium-ion batteries using early cycles: A review;Applied Energy;2024-12

2. Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions;IEEE Transactions on Industrial Electronics;2024-11

3. Comparative analysis of data-driven electric vehicle battery health models across different operating conditions;Energy;2024-11

4. Co-estimation of state of charge and capacity for battery packs in real electric vehicles with few representative cells and physics-informed machine learning;Energy;2024-10

5. Experimentally validated screening strategy for alloys as anode in Mg-air battery with multi-target machine learning predictions;Chemical Engineering Journal;2024-09

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