Article(id=1322531766674502403, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531709363536798, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.6.177, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716307200000, receivedDateStr=2024-05-22, revisedDate=1718812800000, revisedDateStr=2024-06-20, acceptedDate=1721750400000, acceptedDateStr=2024-07-24, onlineDate=1793245744627, onlineDateStr=2026-10-29, pubDate=1782748800000, pubDateStr=2026-06-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245744627, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245744627, creator=13701087609, updateTime=1793245744627, updator=13701087609, issue=Issue{id=1322531709363536798, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='6', pageStart='1', pageEnd='368', issueExtLink='null', onlineDate='null', pubDate='1782748800000', pubDateStr='2026-06-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793245730962, creator='13701087609', updateTime=1793245730962, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=177, endPage=188, ext={EN=ArticleExt(id=1322531766880023300, articleId=1322531766674502403, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=SOH Prediction for Lithium-ion Batteries Based on Feature Analysis and V-PSA-LSTM, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Aimed at the phenomenon of capacity recovery and the problem of accurate prediction during the cyclic usage of lithium-ion batteries, an approach integrating feature analysis with an innovative variational pyramid split attention long short-term memory (V-PSA-LSTM) network model is proposed. This method performs the feature analysis of lithium-ion batteries, and Pearson correlation coefficients are employed to screen out the health features that are closely associated with the battery’s state of health (SOH), thereby revealing the key characteristics such as dynamic capacity restoration. Then, a novel V-PSA-LSTM network model is put forward, in which a periodic variation activation mechanism is incorporated into the pyramid split attention (PSA) algorithm to optimize the long short-term memory (LSTM) network for the first time. This design aims at enhancing the LSTM model’s capability to precisely capture the evolution patterns of SOH in lithium-ion batteries, particularly the fluctuating changes in capacity during the cyclic process. It is verified that the V-PSA-LSTM model significantly reduces prediction errors during the SOH prediction for lithium-ion batteries, e.g., the root mean square error (RMSE) and mean absolute error (MAE) are 0.002 2 and 0.001 8, respectively, indicating the exceptional performance in solving the problems of capacity recovery and accurate prediction.

, authors=Zhijun GAO, Rui DAI, Yi NING, Xifeng GUO, authorsList=Zhijun GAO, Rui DAI, Yi NING, Xifeng GUO, authorCompany=null, correspAuthors=Rui DAI, 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=1322531767425282823, articleId=1322531766674502403, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=结合特征分析和V-PSA-LSTM的锂离子电池SOH预估, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

针对锂离子电池循环使用中的容量回升现象及其精准预估问题，提出了一种整合特征分析与创新变分金字塔切分注意力长短期记忆V-PSA-LSTM（variational pyramid split attention long short-term memory）网络模型的方法。该方法对锂离子电池进行特征分析，通过Pearson相关系数筛选出与电池健康状态SOH（state of health）密切相关的健康特征，以揭示电池容量动态恢复等关键特性。提出的创新V-PSA-LSTM网络模型方案，将周期变异激活机制融入金字塔切分注意力PSA（pyramid split attention）算法中优化长短期记忆LSTM（long short-term memory）网络，旨在强化LSTM模型对锂离子电池SOH的演化规律，特别是循环过程中容量起伏变化的精准捕获能力。经验证，V-PSA-LSTM模型在预估锂离子电池SOH时，显著减小了预估误差，均方根误差RMSE（root mean square error）和平均绝对误差MAE（mean absolute error）分别为0.002 2和0.001 8，展示了在解决电池容量回升问题和其精准预估问题上的优越性能。

, authors=高治军, 戴瑞, 宁一, 郭喜峰, authorsList=高治军, 戴瑞, 宁一, 郭喜峰, authorCompany=null, correspAuthors=戴瑞, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=xiUqGwS+PckovQY63053ow==, magXml=MNMe6eLB9Y3Wmg9Un9UyNQ==, pdfUrl=null, pdf=coFtCR1k8oPRzBvsH2YIGA==, pdfFileSize=8093844, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=oVC4BsZqACwn4pNh3ojRBw==, mapNumber=null, fund=null)}, authors=[Author(id=1322531767718884108, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322531766674502403, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=gzj@sjzu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322531767802770190, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322531766674502403, authorId=1322531767718884108, language=EN, stringName=Zhijun GAO, firstName=Zhijun, middleName=null, lastName=GAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322531767861490447, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322531766674502403, authorId=1322531767718884108, language=CN, stringName=高治军, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=沈阳建筑大学电气与控制工程学院，沈阳　110168, bio={"content":"

高治军（1978—　），男，博士，教授。研究方向：智能信息感知与故障诊断，智慧建筑技术与应用。E-mail：gzj@sjzu.edu.cn。

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高治军（1978—　），男，博士，教授。研究方向：智能信息感知与故障诊断，智慧建筑技术与应用。E-mail：gzj@sjzu.edu.cn。

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宁一（1988—　），女，博士，讲师。研究方向：新能源技术，人工智能在综合能源系统中的应用。E-mail：vipningyi@sjzu.edu.cn。

"}, bioImg=null, bioContent=

宁一（1988—　），女，博士，讲师。研究方向：新能源技术，人工智能在综合能源系统中的应用。E-mail：vipningyi@sjzu.edu.cn。

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郭喜峰（1981—　），男，博士后，教授。研究方向：建筑能源管控，清洁能源高效利用，区域能源优化。E-mail：guoxifeng@sjzu.edu.cn。

"}, bioImg=null, bioContent=

郭喜峰（1981—　），男，博士后，教授。研究方向：建筑能源管控，清洁能源高效利用，区域能源优化。E-mail：guoxifeng@sjzu.edu.cn。

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