Article(id=1321491568146632895, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491493899055939, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.8.293, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1705507200000, receivedDateStr=2024-01-18, revisedDate=1708876800000, revisedDateStr=2024-02-26, acceptedDate=1711468800000, acceptedDateStr=2024-03-27, onlineDate=1792997741971, onlineDateStr=2026-10-26, pubDate=1767024000000, pubDateStr=2025-12-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997741971, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997741971, creator=13701087609, updateTime=1792997741971, updator=13701087609, issue=Issue{id=1321491493899055939, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='8', pageStart='1', pageEnd='341', issueExtLink='null', onlineDate='null', pubDate='1767024000000', pubDateStr='2025-12-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997724270, creator='13701087609', updateTime=1792997964624, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492502109074426, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491493899055939, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492502109074427, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491493899055939, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=293, endPage=301, ext={EN=ArticleExt(id=1321491569832743104, articleId=1321491568146632895, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Prediction of SOH and RUL for Lithium-ion Batteries Using EIS Data Augmentation Based on VAE Model, columnId=1152281491788100462, journalTitle=Journal of Power Supply, columnName=Battery and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

The state-of-health (SOH) and remaining useful life (RUL) of batteries are important indicators for measuring their performance and conditions, and an accurate prediction of these indicators has high practical value. The electrochemical impedance spectroscopy (EIS) test of batteries can reveal the electrochemical reactions and processes inside them, providing important information for evaluating their SOH. EIS can be used to predict SOH and RUL, but it has high testing costs and limited data volume. The use of variational autoencoder (VAE) for data augmentation can expand the volume of EIS data, which helps improve the model’s generalization capability and reduce the risk of overfitting. Due to the fact that the EIS data is usually high-dimensional and may contain redundant information, principal component analysis (PCA) can be used to reduce the dimensionality of data while retaining the key information. Convolutional neural network (CNN) can recognize the spatial features in the EIS data, while bidirectional long short-term memory (BiLSTM) network can capture features within the time series data. The Attention mechanism can enhance the model’s understanding of data contextual relationships. The research results indicate that the model combining PCA, CNN, BiLSTM network and Attention mechanism can more effectively predict the SOH and RUL of batteries with the addition of augmented data. The proposed EIS data augmentation method can also be applied to other models to improve the predictive performance.

, authors=Wei CHANG, Zhichao HU, Duozhao PAN, Jiwen SHI, authorsList=Wei CHANG, Zhichao HU, Duozhao PAN, Jiwen SHI, authorCompany=null, correspAuthors=Wei CHANG, 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=1321491570663215298, articleId=1321491568146632895, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于VAE模型的EIS数据增强的锂电池SOH和RUL预测, columnId=1149830274575463188, journalTitle=电源学报, columnName=电池与储能, runingTitle=null, highlight=null, articleAbstract=

电池的健康状态SOH（state-of-health）和剩余使用寿命RUL（remaining useful life）是衡量电池性能和状态的重要指标，对它们的准确预测具有很高的实用价值。电池的电化学阻抗谱EIS（electrochemical impedance spectroscopy）测试能够揭示电池内部的电化学反应和过程，为评估电池的健康状况提供重要信息。EIS可用于预测SOH和RUL，但测试成本高、数据量少。采用变分自编码器VAE（variational autoencoder）进行数据增强，可以扩充EIS数据的数量，有助于提升模型的泛化能力并减少过拟合的风险。EIS数据通常是高维的且可能包含冗余信息，主成分分析PCA（principal component analysis）可以用来降低数据的维度，同时保留关键信息。卷积神经网络CNN（convolutional neural network）能够识别EIS数据中的空间特征，而双向长短期记忆BiLSTM（bidirectional long short-term memory）网络能够捕捉时间序列数据中的特征。注意力（Attention）机制则可以加强模型对数据上下文关系的理解。研究结果表明，结合PCA、CNN、BiLSTM和Attention的模型，在加入增强数据后，能够更有效地预测电池的SOH和RUL。所提EIS数据增强方法也可以被应用于其他模型，以提高预测性能。

, authors=常伟, 胡志超, 潘多昭, 师继文, authorsList=常伟, 胡志超, 潘多昭, 师继文, authorCompany=null, correspAuthors=常伟, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=YnaSQjWsNeGPscdGw4BfKg==, magXml=k69J8NnXR2+UTIfH5y/c4A==, pdfUrl=null, pdf=0gY2+CSeuQIlKELlQm0NyA==, pdfFileSize=1992772, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=MKB96Q0e963QNcvi81AYnQ==, mapNumber=null, fund=null)}, authors=[Author(id=1321491572726812871, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=weichang008@hotmail.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1321491573133660361, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, authorId=1321491572726812871, language=EN, stringName=Wei CHANG, firstName=Wei, middleName=null, lastName=CHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Nantong LeChuang New Energy Co., Ltd., Nantong 200233, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491573209157835, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, authorId=1321491572726812871, language=CN, stringName=常伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=南通乐创新能源有限公司，南通　200233, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1321491571959255235, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, xref=null, ext=[AuthorCompanyExt(id=1321491571967643844, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, companyId=1321491571959255235, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Nantong LeChuang New Energy Co., Ltd., Nantong 200233, China), AuthorCompanyExt(id=1321491571984421061, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, companyId=1321491571959255235, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=南通乐创新能源有限公司，南通　200233)])]), Author(id=1321491573955743949, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=18221123045@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321491574039630031, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, authorId=1321491573955743949, language=EN, stringName=Zhichao HU, firstName=Zhichao, middleName=null, lastName=HU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Nantong LeChuang New Energy Co., Ltd., Nantong 200233, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491574400340176, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491568146632895, authorId=1321491573955743949, language=CN, stringName=胡志超, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=南通乐创新能源有限公司，南通　200233, bio={"content":"

胡志超（1984—　），男，硕士，工程师。研究方向：电池热失控，EIS、SOH估计。E-mail：18221123045@163.com。

"}, bioImg=null, bioContent=

胡志超（1984—　），男，硕士，工程师。研究方向：电池热失控，EIS、SOH估计。E-mail：18221123045@163.com。

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潘多昭（1991—　），男，硕士，工程师。研究方向：储能与能源工程，AI算法，智慧能源。E-mail：pandz_sjtu@163.com。

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潘多昭（1991—　），男，硕士，工程师。研究方向：储能与能源工程，AI算法，智慧能源。E-mail：pandz_sjtu@163.com。

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师继文（1990—　），男，硕士，工程师。研究方向：储能与能源工程，新能源材料。E-mail：sjw_1007@163.com。

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师继文（1990—　），男，硕士，工程师。研究方向：储能与能源工程，新能源材料。E-mail：sjw_1007@163.com。

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