Article(id=1322560499074581355, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322560475263513295, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.9.155, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1735574400000, receivedDateStr=2024-12-31, revisedDate=1739635200000, revisedDateStr=2025-02-16, acceptedDate=1740844800000, acceptedDateStr=2025-03-02, onlineDate=1793252594965, onlineDateStr=2026-10-29, pubDate=1790697600000, pubDateStr=2026-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252594965, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252594965, creator=13701087609, updateTime=1793252594965, 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=155, endPage=163, ext={EN=ArticleExt(id=1322560499280102252, articleId=1322560499074581355, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Lithium-ion Battery SOC Estimation Based on AEKF Optimized by Dynamic Generalized Correntropy, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Aiming at the shortcomings of low estimation accuracy and poor robustness of extended Kalman filter (EKF) state of charge (SOC) under non-Gaussian noise, an adaptive extended Kalman filter algorithm based on dynamic generalized maximum correntropy (DGMCC-AEKF) optimization is proposed. The algorithm improves the robustness by replacing the minimum mean square error criterion with the generalized maximum correntropy criterion, and proposes a strategy of dynamically adjusting the kernel width parameters based on the exponential form combined with the iterative method, which improves the accuracy and stability. Under dynamic stress test (DST) and federal urban driving schedule (FUDS) conditions, the mean absolute error and root mean squared error of the new algorithm are lower than those of the existing adapative extended Kalman filter (AEKF), adapative iterated extended Kalman filler (AIEKF) and maximum correntropy criterion extended Kalman filter (MCC-EKF) algorithms. Compared with other adaptive kernel width methods, it has better performance under four noise schemes.

, authors=Yuting SONG, Jun ZHOU, authorsList=Yuting SONG, Jun ZHOU, authorCompany=null, correspAuthors=Jun ZHOU, 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=1322560499728892783, articleId=1322560499074581355, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于动态广义相关熵优化AEKF的锂电池SOC估计, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

针对非高斯噪声下传统扩展卡尔曼滤波EKF（extended Kalman filter）荷电状态SOC（state of charge）估计精度低和鲁棒性差的不足，提出基于动态广义最大相关熵准则优化的自适应扩展卡尔曼滤波DGMCC-AEKF（dynamic generalized maximum correntropy criterion adaptive EKF）算法。该算法通过将广义最大相关熵准则替代卡尔曼滤波中的最小均方误差准则，提高了算法在非高斯噪声干扰下的鲁棒性，并提出一种基于指数形式结合迭代方法的动态调整核宽参数的策略，提高了算法的估计精度和稳定性。实验在动态压力测试DST（dynamic stress test）和联邦城市驾驶工况FUDS（federal urban driving schedule）下进行，在非高斯噪声干扰下新算法的平均绝对误差和均方根误差对比现有自适应扩展卡尔曼滤波AEKF（adaptive EKF）、自适应迭代扩展卡尔曼滤波AIEKF（adaptive iterated EKF）、最大相关熵准则扩展卡尔曼滤波MCC-EKF（maximum correntropy criterion EKF）算法均降低，与其他自适应核宽方法在4种非高斯噪声方案下相比具有更好的估计性能。

, authors=宋禹霆中国电源学会学生会员, 周俊, authorsList=宋禹霆中国电源学会学生会员, 周俊, authorCompany=null, correspAuthors=周俊, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=l40y4rQBXWLKTs3Cy7TkVA==, magXml=OMsH8UA0xYZdQ7SU8MZP3w==, pdfUrl=null, pdf=TUX1z8HyHmBfwtpLJDC7HQ==, pdfFileSize=7789355, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=qyx08r8P6YifP8hG8o1H2w==, mapNumber=null, fund=null)}, authors=[Author(id=1322560500026688372, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560499074581355, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1344981910@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322560500173489014, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560499074581355, authorId=1322560500026688372, language=EN, stringName=Yuting SONG, firstName=Yuting, middleName=null, lastName=SONG, prefix=null, suffix=Student Member, CPSS, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322560500244792183, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322560499074581355, authorId=1322560500026688372, language=CN, stringName=宋禹霆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=null, address=昆明理工大学机电工程学院，昆明　650500, bio={"content":"

宋禹霆（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：动力电池状态估计。E-mail：1344981910@qq.com。

"}, bioImg=null, bioContent=

宋禹霆（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：动力电池状态估计。E-mail：1344981910@qq.com。

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