Article(id=1321491711675715785, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.1.242, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1685289600000, receivedDateStr=2023-05-29, revisedDate=1693152000000, revisedDateStr=2023-08-28, acceptedDate=1695312000000, acceptedDateStr=2023-09-22, onlineDate=1792997776191, onlineDateStr=2026-10-26, pubDate=1769702400000, pubDateStr=2026-01-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997776191, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997776191, creator=13701087609, updateTime=1792997776191, updator=13701087609, issue=Issue{id=1321491612593664576, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='1', pageStart='1', pageEnd='346', issueExtLink='null', onlineDate='null', pubDate='1769702400000', pubDateStr='2026-01-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997752568, creator='13701087609', updateTime=1792998003208, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492663950487647, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492663950487648, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=242, endPage=251, ext={EN=ArticleExt(id=1321491713273745610, articleId=1321491711675715785, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=State-of-health Estimation Method for Lithium-ion Battery Considering Capacity Regeneration, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

During the normal utilization of a lithium-ion battery, its capacity will be regenerated in a short term due to factors such as internal chemical reactions and external environment. Aimed at the problems including the inaccurate estimation of state-of-health (SOH) which results from the ignorance of capacity regeneration, an optimal variational mode decomposition (OVMD) strategy based on the number of optimal layers is proposed. The low-frequency stable capacity sequence reflects the overall aging trend, while the high-frequency non-smooth capacity sequence reflects the local capacity regeneration. With the consideration of high- and low-frequency components and by introducing the weighted mean of vectors (INFO) algorithm to improve the support vector regression (SVR), a short-term SOH estimation model for lithium-ion battery is established. With the selection of NASA battery aging data sets, contrast schemes for short-term lithium-ion battery SOH estimation experiment are designed based on BP (back propagation), SVR, INFO-SVR and OVMD-INFO-SVR. Results indicate that in the case of reducing the capacity training sets, the OVMD-INFO-SVR model based on high- and low-frequency components has higher accuracy when it is used in short-term SOH estimation of lithium-ion batteries.

, authors=Jingying ZHAO¹, Linran HUANG¹, Shuailiang YAO², authorsList=Jingying ZHAO, Linran HUANG, Shuailiang YAO, authorCompany=null, correspAuthors=Linran HUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321491713760284877, articleId=1321491711675715785, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=考虑容量再生的锂离子电池健康状态估计方法, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

在锂离子电池正常使用过程中，受内部化学反应和外部环境等因素影响，锂离子电池容量会在短期内出现再生现象。针对未考虑锂离子电池容量再生现象导致电池健康状态SOH（state-of-health）估计不精确等问题，提出基于层数的最优变分模态分解策略OVMD（optimal variational mode decomposition），聚类容量反映整体老化趋势的低频平稳序列和反映局部容量再生的高频非平稳序列。考虑容量高频和低频分量，引入加权向量平均INFO（weighted mean of vectors）方法改进支持向量回归SVR（support vector regression），建立锂离子电池短期SOH估计模型。选取NASA电池老化数据集，设计基于反向传播BP（back propagation）神经网络、SVR、INFO-SVR、OVMD-INFO-SVR的短期锂离子电池SOH估计实验对比方案。结果表明，在减少容量训练集的情况下，基于高频和低频分量的OVMD-INFO-SVR模型在锂离子电池短期SOH的估计上具有更高的准确性。

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赵靖英（1974—　），女，博士，教授。研究方向：电器可靠性评估。E-mail：zhao_team@163.com。

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赵靖英（1974—　），女，博士，教授。研究方向：电器可靠性评估。E-mail：zhao_team@163.com。

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姚帅亮（1994—　），男，硕士，工程师。研究方向：储能控制技术研究。E-mail：1556445299@qq.com。

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姚帅亮（1994—　），男，硕士，工程师。研究方向：储能控制技术研究。E-mail：1556445299@qq.com。

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