Article(id=1322560503004640235, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322560475263513295, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.9.121, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725984000000, receivedDateStr=2024-09-11, revisedDate=1739030400000, revisedDateStr=2025-02-09, acceptedDate=1740240000000, acceptedDateStr=2025-02-23, onlineDate=1793252595902, onlineDateStr=2026-10-29, pubDate=1790697600000, pubDateStr=2026-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252595902, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252595902, creator=13701087609, updateTime=1793252595902, updator=13701087609, issue=Issue{id=1322560475263513295, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='9', pageStart='1', pageEnd='370', issueExtLink='null', onlineDate='null', pubDate='1790697600000', pubDateStr='2026-09-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252589288, creator='13701087609', updateTime=1793252589288, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=121, endPage=131, ext={EN=ArticleExt(id=1322560503214355436, articleId=1322560503004640235, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Online Estimation of SOH for Lithium-ion Batteries Based on Incremental Capacity Curves and QGA-BP, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Accurate estimation of the state of health (SOH) of lithium-ion batteries can effectively ensure the safe operation of battery systems. To address the difficulties in selecting health indicators (HIs) and the problem that computational methods are prone to falling into local optimal solutions in the process of estimating the SOH of lithium-ion batteries, a lithium-ion battery SOH online estimation model based on incremental capcity (IC) of quantum genetic algorithm optimized back-propagation (QGA-BP) neural network is proposed. This algorithm first extracts key indicators from the IC curves during battery aging, subsequently conducts a correlation analysis between the extracted HIs and the SOH, then uses the extracted indicators as input to train the QGA-BP neural network to achieve online estimation of the SOH of lithium-ion batteries, and finally verified by the experimental data measured from the experimental platform and the NASA dataset. The results show that the maximum values of the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the proposed algorithm are 0.013 12, 0.012 94 and 0.017 86, respectively, compared with the traditional back-propagation (BP) neural network and other algorithm, with good convergence and robustness, and can achieve the accurate online estimation of SOH of lithium-ion batteries.

, authors=Yongmao ZHU¹, Yin Lܲ, Jianwei SHAO¹, Mengyuan WANG¹, Huiying ZHANG¹, Junhua WANG¹, authorsList=Yongmao ZHU, Yin LÜ, Jianwei SHAO, Mengyuan WANG, Huiying ZHANG, Junhua WANG, authorCompany=null, correspAuthors=Jianwei SHAO, 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=1322560503902221295, articleId=1322560503004640235, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于容量增量曲线的QGA-BP锂离子电池SOH在线估计, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

锂离子电池健康状态SOH（state of health）的准确估计可以有效确保电池系统安全运行。为了解决锂离子电池SOH估计过程中健康指标HIs（health indicators）选取困难与计算方法易陷入局部最优的问题，提出一种基于容量增量IC（incremental capcity）曲线的量子遗传算法优化反向传播QGA-BP（quantum genetic algorithm optimized back-propagation）神经网络的锂离子电池SOH在线估计模型。该算法首先从电池性能老化过程中的IC曲线中提取关键指标；接着对提取的HIs与SOH进行相关性分析；然后将提取的指标作为输入训练QGA-BP，实现锂离子电池SOH在线估计；最后对自主实验平台测得的实验数据和NASA数据集进行检验。结果表明，相比于传统反向传播BP（back-propagation）神经网络等算法，所提算法的平均绝对误差MAE（mean absolute error）、平均绝对百分比误差MAPE（mean absolute percentage error）和均方根误差RMSE（root mean square error）的最大值分别为0.013 12、0.012 94和0.017 86，具有良好的收敛性和鲁棒性，能够实现锂离子电池SOH在线准确估计。

, authors=朱永茂中国电源学会学生会员¹, 吕印², 邵建伟中国电源学会高级会员¹, 王梦园中国电源学会学生会员¹, 张慧颖中国电源学会学生会员¹, 王军华¹, authorsList=朱永茂中国电源学会学生会员, 吕印, 邵建伟中国电源学会高级会员, 王梦园中国电源学会学生会员, 张慧颖中国电源学会学生会员, 王军华, authorCompany=null, correspAuthors=邵建伟中国电源学会高级会员, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=1YEiCOiIWq2k1U29d8WtpQ==, magXml=sb/tTlPA7OC7QaBAmIsn5w==, pdfUrl=null, pdf=c1LhMvgu1ZimCmW4mNSOlQ==, pdfFileSize=5249119, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Lc7b94rvtMwBWNvVZFDRqQ==, mapNumber=null, fund=null)}, authors=[Author(id=1322560504359400439, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zhuyongmao@whu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322560504447480825, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, authorId=1322560504359400439, language=EN, stringName=Yongmao ZHU, firstName=Yongmao, middleName=null, lastName=ZHU, prefix=null, suffix=Student Member, CPSS, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322560504535561210, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, authorId=1322560504359400439, language=CN, stringName=朱永茂中国电源学会学生会员, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.武汉大学电气与自动化学院，武汉　430072, bio={"content":"

朱永茂（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：储能电池状态估计及预测。E-mail：zhuyongmao@whu.edu.cn。

"}, bioImg=null, bioContent=

朱永茂（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：储能电池状态估计及预测。E-mail：zhuyongmao@whu.edu.cn。

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吕印（1998—　），男，硕士，工程师。研究方向：储能电池状态评估。E-mail：1172885414@qq.com。

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吕印（1998—　），男，硕士，工程师。研究方向：储能电池状态评估。E-mail：1172885414@qq.com。

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王梦园（2002—　），女，中国电源学会学生会员，硕士研究生。研究方向：储能电池状态评估。E-mail：3343601523@qq.com。

"}, bioImg=null, bioContent=

王梦园（2002—　），女，中国电源学会学生会员，硕士研究生。研究方向：储能电池状态评估。E-mail：3343601523@qq.com。

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张慧颖（2000—　），女，中国电源学会学生会员，硕士研究生。研究方向：储能调度技术。E-mail：huiyingzhang@whu.edu.cn。

"}, bioImg=null, bioContent=

张慧颖（2000—　），女，中国电源学会学生会员，硕士研究生。研究方向：储能调度技术。E-mail：huiyingzhang@whu.edu.cn。

, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1322560504149685232, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, xref=1., ext=[AuthorCompanyExt(id=1322560504158073841, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, companyId=1322560504149685232, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China), AuthorCompanyExt(id=1322560504166462450, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, companyId=1322560504149685232, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^1.武汉大学电气与自动化学院，武汉　430072)])]), Author(id=1322560507203137551, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, orderNo=5, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=junhuawang@whu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322560507274440721, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, authorId=1322560507203137551, language=EN, stringName=Junhua WANG, firstName=Junhua, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322560507358326802, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560503004640235, authorId=1322560507203137551, language=CN, stringName=王军华, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.武汉大学电气与自动化学院，武汉　430072, bio={"content":"

王军华（1981—　），男，博士，教授。研究方向：新能源与高效储能，电力终端智能感知，无线电能传输。E-mail：junhuawang@whu.edu.cn。

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王军华（1981—　），男，博士，教授。研究方向：新能源与高效储能，电力终端智能感知，无线电能传输。E-mail：junhuawang@whu.edu.cn。

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