Article(id=1321485717897850932, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.5.270, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666022400000, receivedDateStr=2022-10-18, revisedDate=1670169600000, revisedDateStr=2022-12-05, acceptedDate=1672156800000, acceptedDateStr=2022-12-28, onlineDate=1792996347163, onlineDateStr=2026-10-26, pubDate=1759161600000, pubDateStr=2025-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792996347163, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792996347163, creator=13701087609, updateTime=1792996347163, updator=13701087609, issue=Issue{id=1321485607872872740, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='5', pageStart='1', pageEnd='332', issueExtLink='null', onlineDate='null', pubDate='1759161600000', pubDateStr='2025-09-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792996320932, creator='13701087609', updateTime=1792996886850, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321487981563728115, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321487981563728116, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=270, endPage=277, ext={EN=ArticleExt(id=1321485718271143989, articleId=1321485717897850932, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=SOH Estimation of Lithium-ion Battery Based on Residual Connection and Conv-LSTM, columnId=1152281491788100462, journalTitle=Journal of Power Supply, columnName=Battery and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Lithium-ion batteries are usually taken as energy storage components to realize the storage and utilization of electrical energy, and an accurate estimation of their state-of-health (SOH) can provide a theoretical basis for their actual applications. However, SOH is difficult to measure directly. To estimate the SOH of lithium-ion batteries accurately and conveniently, a model for estimating the SOH of lithium-ion batteries is proposed based on residual connection and convolutional long short-term memory (Conv-LSTM) networks from the perspective of data driven. This method is based on residual connection, Conv-LSTM and convolutional neural networks, and it uses the voltage, current and capacity during the charging process of a lithium-ion battery to realize the SOH estimation in its service cycle. The experimental results on multiple battery test datasets show that compared with those of the existing models with better estimation effects, the estimation result of the SOH estimation model based on residual connection and Conv-LSTM was more accurate, and the maximum percentage error and mean absolute percentage error of SOH estimation on the test dataset were less than 3.3% and 1.7%, respectively.

, authors=Bentao ZHOU^{1, 2}, Jili TAO², Qingsong WANG², authorsList=Bentao ZHOU, Jili TAO, Qingsong WANG, authorCompany=null, correspAuthors=Jili TAO, 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=1321485720800309304, articleId=1321485717897850932, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于残差连接和Conv-LSTM的锂离子电池SOH估算, columnId=1149830274575463188, journalTitle=电源学报, columnName=电池与储能, runingTitle=null, highlight=null, articleAbstract=

锂离子电池常被用作储能元件以实现电能的存储和使用，精确地估算其健康状态SOH（state-of-health）可以为电池的实际使用提供理论依据。然而，SOH无法被直接测量。为了准确且便捷地估算锂离子电池SOH，从数据驱动法的角度提出1种基于残差连接和卷积长短时记忆Conv-LSTM（convolutional long short-term memory）网络的锂离子电池SOH估算模型。该方法基于残差连接、Conv-LSTM和卷积神经网络，利用锂离子电池充电过程中的电压、电流和容量，实现锂离子电池使用周期内的SOH估算。在多个电池测试数据集上的实验结果表明，与多种估算效果较好的现有模型相比，基于残差连接和Conv-LSTM的SOH估算模型具有更精准的估算结果，测试集SOH估算的最大相对误差MAX PE（maximum percentage error）和平均绝对百分比误差MAPE（mean absolute percentage error）分别小于3.3%和1.7%。

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^2.浙大宁波理工学院信息科学与工程学院，宁波　315000, bio={"content":"

周奔滔（1997—　），男，硕士研究生。研究方向：电池健康管理系统。E-mail：710991620@qq.com。

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周奔滔（1997—　），男，硕士研究生。研究方向：电池健康管理系统。E-mail：710991620@qq.com。

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王清松（2002—　），男，本科。研究方向：电池健康管理系统。E-mail：23812063920@qq.com。

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王清松（2002—　），男，本科。研究方向：电池健康管理系统。E-mail：23812063920@qq.com。

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