Article(id=1322531803659874337, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531709363536798, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.6.198, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716134400000, receivedDateStr=2024-05-20, revisedDate=1719417600000, revisedDateStr=2024-06-27, acceptedDate=1722700800000, acceptedDateStr=2024-08-04, onlineDate=1793245753444, onlineDateStr=2026-10-29, pubDate=1782748800000, pubDateStr=2026-06-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245753444, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245753444, creator=13701087609, updateTime=1793245753444, 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=198, endPage=209, ext={EN=ArticleExt(id=1322531803861200930, articleId=1322531803659874337, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Method for Estimating State-of-health of Lithium Batteries Based on Random Charging Segments, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

The state of health (SOH) estimation method for lithium batteries is crucial for battery management systems used in electric vehicles. At present, most of the SOH estimation methods often require the charging and discharging data during a long time, resulting in a poor real-time performance. To address this issue, an SOH estimation method based on random charging segments for feature extraction analysis is proposed. First, the complete constant current (CC) charging process is divided into several fixed-length data segments according to voltage intervals. Then, health indicators (HIs) are extracted for different voltage intervals. Finally, based on the characteristics of HIs, a back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm is trained, which can extract features from any CC charging segment to complete SOH estimation. NASA and Oxford datasets verify that this method has high flexibility and accuracy.

, authors=Pengxu GUO¹, Li ZHAO^{1, 2}, Fengshuo ZHANG¹, authorsList=Pengxu GUO, Li ZHAO, Fengshuo ZHANG, authorCompany=null, correspAuthors=Li ZHAO, 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=1322531804423237669, articleId=1322531803659874337, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于随机充电片段的锂离子电池健康状态估计方法, 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）估计方法对电动汽车电池管理系统至关重要。当前，大多数SOH估计方法往往需要较长时间的充放电数据，实时性较差。针对该问题，提出一种基于随机充电片段进行特征提取分析的SOH估计方法。该方法首先将完整的恒流CC（constant current）充电过程按电压区间分割成若干固定长度的数据段，然后针对不同电压区间分别提取健康指标HIs（health indicators），最后基于HIs特征训练经粒子群优化PSO（particle swarm optimization）算法优化后的反向传播神经网络BPNN（back propagation neural network）。该方法可基于任意CC充电片段进行特征提取，进而完成SOH估计。经NASA数据集和Oxford数据集验证，表明该方法具有较高的灵活性和精度。

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郭鹏旭（1997—　），男，硕士研究生。研究方向：新能源汽车电池管理系统。E-mail：2022020124@bistu.edu.cn。

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郭鹏旭（1997—　），男，硕士研究生。研究方向：新能源汽车电池管理系统。E-mail：2022020124@bistu.edu.cn。

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张丰硕（1998—　），男，硕士研究生。研究方向：新能源汽车故障诊断方法。E-mail：2022020130@bistu.edu.cn。

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张丰硕（1998—　），男，硕士研究生。研究方向：新能源汽车故障诊断方法。E-mail：2022020130@bistu.edu.cn。

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