南京理工大学学报（自然科学版）2018，Vol.42Issue(1)：48-55,8.DOI:10.14177/j.cnki.32-1397n.2018.42.01.007
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
Prediction for state of health of lithium-ion batteries by local information fusion with ensemble support vector regression
陈建新 1候建明 1王鑫 1邵海涛 1宋广磊 1薛宇2
作者信息
- 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
- 折叠
摘要
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 orig-inal training dataset is divided into multiple subsets,each of which contains the important local infor-mation;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.关键词
锂电池/健康状态/支持向量回归/集成学习/信息融合Key words
lithium-ion batteries/state of health/support vector regression/ensemble learning/information fusion分类
信息技术与安全科学引用本文复制引用
陈建新,候建明,王鑫,邵海涛,宋广磊,薛宇..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(1):48-55,8.
