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The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network
Tian, Qifeng1,2,4; Zhang, Yao3; Wu, Yuanxin1,2; Tan, Zhicheng3; Tian QF(田琦峰); Zhang Y(张耀)
KeywordCycle Life Artificial Neural Network Mg-based Hydrogen Storage Alloys
Source PublicationINTERNATIONAL JOURNAL OF HYDROGEN ENERGY
2009-02-01
DOI10.1016/j.ijhydene.2008.11.077
Volume34Issue:4Pages:1931-1936
Indexed BySCI
SubtypeArticle
Department15
Funding Project1504
Contribution Rank2;2
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
WOS SubjectChemistry, Physical ; Electrochemistry ; Energy & Fuels
WOS Research AreaChemistry ; Electrochemistry ; Energy & Fuels
WOS KeywordMETAL HYDRIDE BATTERIES ; LEAD-ACID-BATTERIES ; NI-MH BATTERIES ; ELECTRIC VEHICLES ; ELECTROCHEMICAL PROPERTIES ; CAPACITY INDICATOR ; TI ; SUBSTITUTION ; TERNARY ; NICKEL
AbstractMg-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.
Language英语
URL查看原文
WOS IDWOS:000264355300036
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/101833
Collection中国科学院大连化学物理研究所
Corresponding AuthorTian QF(田琦峰); Zhang Y(张耀)
Affiliation1.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
GB/T 7714
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.
APA Tian, Qifeng,Zhang, Yao,Wu, Yuanxin,Tan, Zhicheng,田琦峰,&张耀.(2009).The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network.INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,34(4),1931-1936.
MLA Tian, Qifeng,et al."The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network".INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 34.4(2009):1931-1936.
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