Article(id=1309640008458400461, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20260065, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1777392000000, revisedDateStr=2026-04-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172109951, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172109951, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172109951, creator=13701087609, updateTime=1790172109951, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=54, ext={EN=ArticleExt(id=1309640008621978318, articleId=1309640008458400461, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=SOH Prediction of Real Vehicles Power Battery Based on WOA-VMD and TCN-Transformer, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the insufficient accuracy of State Of Health (SOH) prediction for power batteries under real-vehicle operating conditions, a prediction method integrating Temporal Convolutional Network (TCN) and Transformer is proposed. Using real-vehicle operation monitoring data as the research object, after removing abnormal values and selecting charging condition data, the current battery capacity is calculated via the ampere-hour integration method. The Whale Optimization Algorithm (WOA) is adopted to optimize the parameters of Variational Mode Decomposition (VMD), realizing the decomposition and denoising of the capacity sequence. Features are extracted by combining parameters such as current and temperature, and 6 strongly correlated features are selected to construct a dataset, which is input into the model to complete SOH prediction. Results show that the model significantly reduces prediction errors in the datasets of 4 real-vehicle batteries, with the coefficient of determination [R²>0.8], providing technical support for the full-life-cycle health management of power batteries.

, authors=Nanlin Lei¹, Bo Rao^{2, 3}, Ying Jiang¹, Jie Tian^{2, 3}, Jinqiao Du^{2, 3}, authorsList=Nanlin Lei, Bo Rao, Ying Jiang, Jie Tian, Jinqiao Du, authorCompany=null, correspAuthors=Ying Jiang, 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=1309640008819110607, articleId=1309640008458400461, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于WOA-VMD与TCN-Transformer的实车动力电池SOH预测, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对实车工况下动力电池健康状态（SOH）预测精度不足的问题，提出一种融合时序卷积网络（TCN）与Trans‑former的预测方法。以实车运行监测数据为研究对象，经异常值剔除与充电工况筛选后，使用安时积分法计算电池当前容量；采用鲸鱼优化算法（WOA）优化变分模态分解（VMD）参数，实现容量序列的分解与降噪；结合电流、温度等参数提取特征，筛选出6项强相关特征并构建数据集，完成SOH预测。试验结果显示：所提出的方法在4辆实车电池数据集中预测误差显著降低，决定系数［R²>0.8］，可为动力电池全生命周期健康管理提供技术支撑。

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