Article(id=1309639639577752393, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250545, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1756569600000, revisedDateStr=2025-08-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172022003, onlineDateStr=2026-09-23, pubDate=1779552000000, pubDateStr=2026-05-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172022003, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172022003, creator=13701087609, updateTime=1790172022003, updator=13701087609, issue=Issue{id=1309639624411152909, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='5', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1779552000000', pubDateStr='2026-05-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172018387, creator='13701087609', updateTime=1790232830701, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894689805922601, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894689805922602, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=48, endPage=55, ext={EN=ArticleExt(id=1309639641368720202, articleId=1309639639577752393, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Estimation of Lithium-Ion Battery State of Health in Electric Vehicles Using an Ensemble Model Based on Semi-Supervised Learning, columnId=1309639624981578254, journalTitle=Automobile Technology, columnName=Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles, runingTitle=null, highlight=null, articleAbstract=

To address the issue that existing supervised learning methods for estimating the battery State of Health (SOH) rely on scarce labeled data while neglecting the abundance of easily obtainable unlabeled data, a joint SOH estimation method based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) within a Semi-Supervised Learning (SSL) framework is proposed. First, three interpretable health indicators are extracted from the charging voltage to accurately describe battery degradation. Then, based on SSL, pseudo-labels for unlabeled data are predicted to augment the training samples, thereby enhancing the generalization capability of the model. Analysis and experiments demonstrate that the proposed SSL-based method significantly improves SOH estimation performance, with an average Root Mean Square Error (RMSE) below 1.2%.

, authors=Yaping Qin¹, Liuyang Ning², authorsList=Yaping Qin, Liuyang Ning, authorCompany=null, correspAuthors=null, 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=1309639641725236043, articleId=1309639639577752393, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于半监督学习的集成模型估计电动汽车锂电池健康状态, columnId=1309639625430364898, journalTitle=汽车技术, columnName=智能汽车全栈环境感知技术专题, runingTitle=null, highlight=null, articleAbstract=

为解决现有的监督学习方法估计电池健康状态（SOH）时依赖于稀少的标记数据，而忽略了大量易获得的未标记数据这一问题，提出了基于长短期记忆（LSTM）网络和门控循环单元（GRU）的半监督学习（SSL）的联合SOH估计方法。首先，从充电电压中提取了3种具有可解释性的健康因子，用于准确描述电池退化；然后，基于SSL对未标记数据的伪标签进行预测，旨在增强训练样本，从而提升模型泛化能力。分析和试验表明，提出的基于SSL的方法可以显著提高SOH估计性能，平均均方根误差不超过1.2%。

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