Article(id=1321491309689422458, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.6.320, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1667923200000, receivedDateStr=2022-11-09, revisedDate=1675699200000, revisedDateStr=2023-02-07, acceptedDate=1677686400000, acceptedDateStr=2023-03-02, onlineDate=1792997680350, onlineDateStr=2026-10-26, pubDate=1761753600000, pubDateStr=2025-10-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997680350, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997680350, creator=13701087609, updateTime=1792997680350, updator=13701087609, issue=Issue{id=1321491279368794534, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='6', pageStart='1', pageEnd='352', issueExtLink='null', onlineDate='null', pubDate='1761753600000', pubDateStr='2025-10-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997673122, creator='13701087609', updateTime=1792997908848, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492268125631431, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492268125631432, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=320, endPage=328, ext={EN=ArticleExt(id=1321491309882360443, articleId=1321491309689422458, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Life Prediction of Supercapacitor Based on Improved Support Vector Regression, columnId=1152281491788100462, journalTitle=Journal of Power Supply, columnName=Battery and Energy Storage, runingTitle=null, highlight=null, articleAbstract=
The service performance and remaining life of a supercapacitor which serves as a novel energy storage device are part of the factors affecting the operation reliability of a system. A 2.7 V/10 F supercapacitor was taken as the research object, an aging experiment and test platform for energy storage devices was built, and the rules for the performance degradation of the supercapacitor and influencing factors were analyzed by collecting the variations in its characteristics. To solve the contradictory relationship between small samples and prediction accuracy of the supercapacitor lifetime prediction training set, a lifetime prediction method combining the non-dominated sorting genetic algorithm II (NSGA-II) with an elite retention strategy and support vector regression (SVR) was proposed, and the average values of charge-discharge cycles and charge-discharge duration were determined as the characterization of supercapacitor performance degradation. Moreover, a dual-objective model with the minimum sample size and highest prediction accuracy in the training set was established. Compared with the BP (back propagation), SVR and LSTM (long short-term memory) methods, the proposed NSGA-II-SVR method improved the evaluation indicators of root mean square error and mean absolute percentage error by 5 to 10 times in supercapacitor lifetime prediction under the premise of a small sample size of the training set. Finally, a generalization experiment was carried out using the open-source data set of lithium-ion batteries provided by NASA, proving that the NSGA-II-SVR method has strong generalization capability.
, authors=Xiaoming LIU, Yupeng REN, Hai CHEN, Dawei FENG, Jun TIAN, authorsList=Xiaoming LIU, Yupeng REN, Hai CHEN, Dawei FENG, Jun TIAN, authorCompany=null, correspAuthors=Yupeng REN, 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=1321491310343733886, articleId=1321491309689422458, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于改进支持向量回归的超级电容器寿命预测, columnId=1149830274575463188, journalTitle=电源学报, columnName=电池与储能, runingTitle=null, highlight=null, articleAbstract=
超级电容器作为1种新型储能器件，其服役性能与剩余寿命会影响其所在系统的可靠性。以2.7 V/10 F超级电容器为研究对象，搭建了储能器件老化实验与测试平台，通过采集超级电容器的特征量变化，分析其性能退化规律和影响因素。为了解决超级电容器寿命预测训练集小样本和预测精度之间的矛盾，提出了1种精英保留策略的非支配排序遗传算法II NSGA-II（non-dominated sorting genetic algorithm II）与支持向量回归SVR（support vector regression）相结合的寿命预测方法；确定了将充放电循环次数和充放电时长均值作为超级电容性能退化的表征量；建立了训练集样本最小和预测精度最高的双目标模型。相比于神经网络BP（back propagation）、SVR和长短期记忆网络LSTM（long short-term memory）方法，所提NSGA-II-SVR方法在超级电容器寿命预测中，可实现样本训练集数量小的前提下，预测均方根误差和平均绝对百分比误差评价指标均提升5~10倍。最后使用NASA提供的锂离子电池开源数据集进行泛化性实验，证明了NSGA-II-SVR方法具有较强的泛化能力。
, authors=刘晓明, 任宇朋
中国电源学会会员, 陈海, 冯大伟, 田俊, authorsList=刘晓明, 任宇朋
中国电源学会会员, 陈海, 冯大伟, 田俊, authorCompany=null, correspAuthors=任宇朋
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刘晓明（1968— ），女，博士，教授。研究方向：高压电器和智能电器。E-mail：liuxiaoming@hebut.edu.cn。
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陈海（1968— ），男，硕士，工程师。研究方向：高压电器和智能电器。E-mail：chehai@hebut.edu.cn。
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陈海（1968— ），男，硕士，工程师。研究方向：高压电器和智能电器。E-mail：chehai@hebut.edu.cn。
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冯大伟（1992— ），男，博士，讲师。研究方向：高电压与绝缘技术。E-mail：josaturday@hebut.edu.cn。
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田俊（1994— ），男，硕士研究生。研究方向：现代电器设计与应用。E-mail：949204816@qq.com
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