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题名: The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network
作者: Tian, Qifeng1, 2, 4;  Zhang, Yao3;  Wu, Yuanxin1, 2;  Tan, Zhicheng3
通讯作者: 田琦峰 ;  张耀
关键词: Cycle life ;  Artificial neural network ;  Mg-based hydrogen storage alloys
刊名: INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
发表日期: 2009-02-01
DOI: 10.1016/j.ijhydene.2008.11.077
卷: 34, 期:4, 页:1931-1936
收录类别: SCI
文章类型: Article
部门归属: 15
项目归属: 1504
产权排名: 2;2
WOS标题词: Science & Technology ;  Physical Sciences ;  Technology
类目[WOS]: Chemistry, Physical ;  Electrochemistry ;  Energy & Fuels
研究领域[WOS]: Chemistry ;  Electrochemistry ;  Energy & Fuels
英文摘要: Mg-based hydrogen storage alloys are a type of promising cathode material of Nickel-Metal Hydride (Ni-MH) batteries. But inferior cycle life is their major shortcoming. Many methods, such as element substitution, have been attempted to enhance its life. However, these methods usually require time-consuming charge-discharge cycle experiments to obtain a result. In this work, we suggested a cycle life prediction method of Mg-based hydrogen storage alloys based on artificial neural network, which can be used to predict its cycle life rapidly with high precision. As a result, the network can accurately estimate the normalized discharge capacities vs. cycles (after the fifth cycle) for Mg(0.8)Ti(0.1)M(0.1)Ni (M = Ti, Al, Cr, etc.) and Mg(0.9-x)Ti(0.1)Pd(x)Ni (x = 0.04-0.1) alloys in the training and test process, respectively. The applicability of the model was further validated by estimating the cycle life of Mg(0.9)Al(0.08)Ce(0.02)Ni alloys and Nd(5)Mg(41)-Ni composites. The predicted results agreed well with experimental values, which verified the applicability of the network model in the estimation of discharge cycle life of Mg-based hydrogen storage alloys. Crown Copyright (c) 2008 Published by Elsevier Ltd on behalf of international Association for Hydrogen Energy. All rights reserved.
关键词[WOS]: METAL HYDRIDE BATTERIES ;  LEAD-ACID-BATTERIES ;  NI-MH BATTERIES ;  ELECTRIC VEHICLES ;  ELECTROCHEMICAL PROPERTIES ;  CAPACITY INDICATOR ;  TI ;  SUBSTITUTION ;  TERNARY ;  NICKEL
语种: 英语
原文出处: 查看原文
WOS记录号: WOS:000264355300036
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/101833
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Wuhan Inst Technol, Key Lab Green Chem Proc, Minist Educ, Wuhan 430073, Peoples R China
2.Wuhan Inst Technol, Hubei Key Lab Novel Reactor & Green Chem Technol, Wuhan 430073, Peoples R China
3.Chinese Acad Sci, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
4.Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS B3H 3J5, Canada

Recommended Citation:
Tian, Qifeng,Zhang, Yao,Wu, Yuanxin,et al. The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network[J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,2009,34(4):1931-1936.
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