Article(id=1309884748655325422, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1309884740061192672, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20260134, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1784649600000, revisedDateStr=2026-07-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1790230460561, onlineDateStr=2026-09-24, pubDate=1789401600000, pubDateStr=2026-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790230460561, onlineIssueDateStr=2026-09-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790230460561, creator=13701087609, updateTime=1790230460561, updator=13701087609, issue=Issue{id=1309884740061192672, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='9', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1789401600000', pubDateStr='2026-09-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790230458512, creator='13701087609', updateTime=1790230907791, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309886624545211382, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1309884740061192672, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309886624545211383, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1309884740061192672, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=36, endPage=42, ext={EN=ArticleExt(id=1309884749469020399, articleId=1309884748655325422, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=An Early Warning Method for Electric Vehicle Charging Faults Based on Charging Process Data, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the limited generalizability of electric-vehicle fault warning caused by large differences among vehicle models, restricted access to on-board private data and insufficient extraction of temporal features, the paper proposes a hybrid deep-learning fault-warning model based on grid-side charging-process data. First, model-independent voltage, current and other time-series data readily collected at charging piles are processed by the Transformer self-attention mechanism to capture global temporal dependencies and long-range correlations among key components such as batteries and motors. Scecond, a Bidirectional Long Short-Term Memory (BiLSTM) network models local temporal features in both directions to enhance the contextual representation of fault signatures. Finally, a Support Vector Machine (SVM) classifies the fused features to warn of fault levels during charging. Visualization of attention weights confirms that the model focuses effectively on fault-sensitive features such as voltage anomalies and abrupt temperature-gradient changes. The results show that the method enables unobtrusive "inspection while charging" fault warning using the charging process.

, authors=Xutong Zheng, Guo Zheng, authorsList=Xutong Zheng, Guo Zheng, 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=1309884750240772336, articleId=1309884748655325422, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于充电过程数据的电动汽车充电故障早期预警方法, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对新能源电动汽车车型差异大、车载数据隐私受限导致故障预警普适性不足，以及时序数据特征挖掘不充分的问题，提出一种基于电网侧充电过程数据的混合深度学习故障预警模型。首先，利用充电桩侧易于采集且不依赖特定车型的电压、电流等通用时序数据，通过Transformer的自注意力机制提取全局时序依赖关系，捕捉电池、电机等关键部件的长程关联特征；其次，利用双向长短期记忆（BiLSTM）网络对局部时序特征进行双向建模，增强故障征兆的上下文表征能力；最后，通过支持向量机（SVM）对融合特征进行模式分类，实现早期故障的精准预警。通过注意力权重可视化分析验证了模型对电压异常、温度梯度突变等故障敏感特征的有效聚焦，结果表明，该方法依托充电过程实现了“即充即检”的无感化故障诊断。

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