[1]陈建新,候建明,王 鑫,等.基于局部信息融合及支持向量回归集成的锂电池健康状态预测[J].南京理工大学学报(自然科学版),2018,42(01):48.[doi:10.14177/j.cnki.32-1397n.2018.42.01.007]
 Chen Jianxin,Hou Jianming,Wang Xin,et al.Prediction for state of health of lithium-ion batteriesby local informationfusion with ensemble support vector regression[J].Journal of Nanjing University of Science and Technology,2018,42(01):48.[doi:10.14177/j.cnki.32-1397n.2018.42.01.007]
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基于局部信息融合及支持向量回归集成的 锂电池健康状态预测()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年01期
页码:
48
栏目:
出版日期:
2018-02-28

文章信息/Info

Title:
Prediction for state of health of lithium-ion batteries by local information fusion with ensemble support vector regression
文章编号:
1005-9830(2018)01-0048-08
作者:
陈建新1候建明1王 鑫1邵海涛1宋广磊1薛 宇2
1.国网新疆电力公司 信息通信公司,新疆 乌鲁木齐 830000; 2.南瑞集团有限公司 国网电力科学研究院有限公司,江苏 南京 210003
Author(s):
Chen Jianxin1Hou Jianming1Wang Xin1Shao Haitao1Song Guanglei1Xue Yu2
1.State Grid Xinjiang Information & Telecommunication Company,Urumqi 830000,China; 2.State Grid Electric Power Research Institute,NARI Group Corporation,Nanjing 210003,China
关键词:
锂电池 健康状态 支持向量回归 集成学习 信息融合
Keywords:
lithium-ion batteries state of health support vector regression ensemble learning information fusion
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.01.007
摘要:
为了提高锂电池健康状态(State of health,SOH)的预测准确率,该文将支持向量回归(Support vector regression,SVR)算法与集成学习理论相结合,提出一种基于局部信息融合的支持向量回归集成(Local information fusion with ensemble support vector regression,LIF-ESVR)算法。该算法的核心思想是利用数据的局部信息融合替代原有全局信息,并将信息层融合问题转化为决策层融合问题。首先将原始的训练集划分为若干个子训练集,每个子训练集都包含了原始训练集中的部分重要信息; 然后,在每个子训练集上训练一个对应的SVR模型; 最后,利用集成学习算法将已训练好的多个SVR模型进行融合。在美国国家航空航天局蓄电池数据上的实验结果表明,所提方法的性能优于现有的锂电池SOH预测方法,具有广泛的应用价值。
Abstract:
To improve the prediction accuracy of state of health(SOH)for lithium-ion batteries,this paper developes a local information fusion with ensemble support vector regression(LIF-ESVR)method,which is implemented by combining support vector regression(SVR)algorithm with ensemble learning theory. The basic idea of LIF-ESVR is to use local information fusion to replace the global information and switch the information fusion problem to the decision fusion problem. Firstly the original training dataset is divided into multiple subsets,each of which contains the important local information; then,for each subset,the corresponding SVR is trained on it; finally,the ensemble learning technology is adopted to incorporate multiple trained SVRs. The experimental results on batteries datasets of the National Aeronautics and Space Administration(NASA)of USA have demonstrated that the LIF-ESVR outperforms the existing methods for predicting lithium-ion batteries SOH and can be used practically and extensively.

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相似文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-07修回日期:2018-01-05 作者简介:陈建新(1971-),男,工程师,主要研究方向:信息系统及信息网络安全,E-mail:67612237@qq.com; 通讯作者:侯建明(1988-),男,工程师,主要研究方向:电力通信运行,E-mail:1265385443@qq.com。 引文格式:陈建新,候建明,王鑫,等. 基于局部信息融合及支持向量回归集成的锂电池健康状态预测[J]. 南京理工大学学报,2018,42(1):48-55. 投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-02-28