Article(id=1321485723417563974, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.5.241, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1658505600000, receivedDateStr=2022-07-23, revisedDate=1667232000000, revisedDateStr=2022-11-01, acceptedDate=1669046400000, acceptedDateStr=2022-11-22, onlineDate=1792996348479, onlineDateStr=2026-10-26, pubDate=1759161600000, pubDateStr=2025-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792996348479, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792996348479, creator=13701087609, updateTime=1792996348479, updator=13701087609, issue=Issue{id=1321485607872872740, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='5', pageStart='1', pageEnd='332', issueExtLink='null', onlineDate='null', pubDate='1759161600000', pubDateStr='2025-09-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792996320932, creator='13701087609', updateTime=1792996886850, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321487981563728115, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321487981563728116, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=241, endPage=250, ext={EN=ArticleExt(id=1321485724961067847, articleId=1321485723417563974, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Estimation of State-of-health of Lithium-ion Batteries Based on Modal Decomposition and Time Series, columnId=1152281491788100462, journalTitle=Journal of Power Supply, columnName=Battery and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Aimed at the problem that the prediction results of state-of-health (SOH) of lithium-ion batteries are inaccurate due to the failure to consider the capacity regeneration phenomenon, a nonlinear relationship method based on multi-time scale set empirical mode decomposition fusion time series mapping input and SOH is proposed. The battery capacity information considering capacity regeneration is decomposed into high-frequency main trend signals and low-frequency margin signals by an ensemble empirical mode decomposition (EEMD) algorithm, and the estimation of lithium-ion battery SOH is completed by the prediction results of the two types of signals. Results show that the proposed method can capture the phenomenon of capacity regeneration to improve the estimation accuracy and achieve the fitting of battery capacity degradation, and it is less affected by different prediction starting points. Both the predicted maximum RMSE (root mean square error) and MAE (mean absolute error) are not more than 1.5%, and the maximum MAPE (mean absolute percentage error) is not more than 2%.

, authors=Yan CHENG, Linhong ZHENG, Jiang LIU, authorsList=Yan CHENG, Linhong ZHENG, Jiang LIU, authorCompany=null, correspAuthors=Linhong ZHENG, 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=1321485725896397642, articleId=1321485723417563974, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于模态分解和时间序列的锂电池健康状态估计, columnId=1149830274575463188, journalTitle=电源学报, columnName=电池与储能, runingTitle=null, highlight=null, articleAbstract=

针对锂离子电池健康状态SOH（state-of-health）估计未考虑容量再生现象导致预测结果不准确的问题，提出1种基于多时间尺度集合经验模态分解融合时间序列映射输入健康状态非线性关系方法。通过集合经验模态分解EEMD（ensemble empirical mode decomposition）算法将考虑容量再生现象的电池容量信息分解为高频主趋势信号及低频余量信号，由2类信号预测结果完成对锂离子电池SOH的估算。结果表明，该方法可以捕获容量再生现象提高估算准确度，实现对电池容量退化的拟合，且受不同预测起点影响小，预测的最大均方根差RMSE（root mean square error）和平均绝对误差MAE（mean absolute error）不超过1.5%，最大平均绝对百分误差MAPE（mean absolute percentage error）不超过2%。

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成燕（1972—　），女，博士，副教授。研究方向：微电网运行与控制。E-mail：chengyan100@163.com。

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成燕（1972—　），女，博士，副教授。研究方向：微电网运行与控制。E-mail：chengyan100@163.com。

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刘江（1980—　），男，博士，讲师。研究方向：功率变换器控制。E-mail：447558917@qq.com。

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刘江（1980—　），男，博士，讲师。研究方向：功率变换器控制。E-mail：447558917@qq.com。

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