Article(id=1309639589548094013, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20240320, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172010075, onlineDateStr=2026-09-23, pubDate=1770220800000, pubDateStr=2026-02-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172010075, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172010075, creator=13701087609, updateTime=1790172010075, updator=13701087609, issue=Issue{id=1309639589137052221, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='2', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1770220800000', pubDateStr='2026-02-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172009977, creator='13701087609', updateTime=1790233873475, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309899063529669032, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309899063529669033, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=57, endPage=62, ext={EN=ArticleExt(id=1309639589799752255, articleId=1309639589548094013, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on the Lifetime Prediction Method of the Power Module Based on the Mission Profile, columnId=1309639589728449086, journalTitle=Automotive Digest, columnName=Special Topic on Autonomous Driving Technology, runingTitle=null, highlight=null, articleAbstract=

To achieve lifetime prediction of power modules under actual operating conditions, a power module lifetime prediction method based on a temperature model is proposed. Simulation parameters such as voltage, phase current, power factor, and switching frequency are obtained from the vehicle driving cycle data to calculate module losses. First, the Foster thermal network model is then used to calculate the junction temperature, thereby obtaining the module temperature variation sequence. The rainflow counting method is applied to extract the junction temperature fluctuation sequence. Next, the damage degree of the power module under the corresponding driving cycle is evaluated by combining the lifetime model fitted from power cycling tests. Additionally, the damage degrees of the same module under power generation and electric driving conditions are compared to analyze the performance requirements of the module in different application scenarios. The results show that under a 240 000 km driving cycle, the damage degree in electric driving conditions is significantly higher than in power generation conditions, and an increase in switching frequency exacerbates the damage. This method can effectively support rapid reliability assessment of power modules and provide a basis for module selection and optimization in different application scenarios.

, authors=Ying Ma, Jiabin Wang, Zhanshan Zhu, Min Li, Yuexin Zhang, authorsList=Ying Ma, Jiabin Wang, Zhanshan Zhu, Min Li, Yuexin Zhang, 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=1309639590084964929, articleId=1309639589548094013, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于实际工况谱的功率模块寿命预测方法研究, columnId=1309639589879444032, journalTitle=汽车文摘, columnName=自动驾驶技术专题, runingTitle=null, highlight=null, articleAbstract=

为了在实际工况中预测功率模块的寿命，提出了一种基于温度模型的功率模块寿命预测方法。首先根据整车路谱信息获得电压、相电流、功率因数和开关频率等仿真参数，计算模块损耗。其次，结合Foster热网络模型计算其结温，获取模块温度变化序列，并利用雨流计数法提取结温波动序列。然后结合功率循环试验拟合出的寿命模型评估相应路谱的功率模块损伤度。最后，将同一模块发电工况与电动工况损伤度进行对比，分析不同应用场景下对模块的性能要求。结果表明：在24万km行驶路谱下，电动工况损伤度显著高于发电工况，且开关频率升高会加剧损伤。该方法能够有效支持功率模块的可靠性快速评估，为不同应用场景下的模块选型与优化提供依据。

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