Article(id=1322531638416888364, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531529172046652, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.4.158, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1704124800000, receivedDateStr=2024-01-02, revisedDate=1709654400000, revisedDateStr=2024-03-06, acceptedDate=1709913600000, acceptedDateStr=2024-03-09, onlineDate=1793245714048, onlineDateStr=2026-10-29, pubDate=1777478400000, pubDateStr=2026-04-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245714048, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245714048, creator=13701087609, updateTime=1793245714048, updator=13701087609, issue=Issue{id=1322531529172046652, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='4', pageStart='1', pageEnd='352', issueExtLink='null', onlineDate='null', pubDate='1777478400000', pubDateStr='2026-04-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793245688002, creator='13701087609', updateTime=1793245688002, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=158, endPage=168, ext={EN=ArticleExt(id=1322531638639186477, articleId=1322531638416888364, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Improved NGO Optimized MVMD-BiLSTM for Remaining Useful Life Prediction of Lithium Batteries, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

To solve the problem of low model prediction accuracy caused by noise interference and capacity augmentation in the remaining useful life (RUL) prediction of lithium batteries, an RUL prediction method combining the parameter-optimized multivariate variational modal decomposition (MVMD) and bi-directional long and short-term memory (BiLSTM) network is proposed. Multiple health factors (HFs) are extracted from the charging and discharging processes of lithium batteries as model inputs, and the northern goshawk optimization (NGO) algorithm is introduced by combining the sine cosine algorithm and the whale spiral predation mechanism, which is used to optimize the MVMD parameters. Then, the optimized MVMD is used to decompose the HF into a number of smooth components. Finally, the optimized multilayer BiLSTM is used for prediction, and the stacked summation is the final RUL result. The results of a comparative analysis of the proposed method and other methods based on NASA and CALCE data sets show that this method has higher prediction accuracy and generalized adaptability.

, authors=Xifeng GUO, Yuhai HUANG, Dan SHAN, Baolong YUAN, Yi NING, authorsList=Xifeng GUO, Yuhai HUANG, Dan SHAN, Baolong YUAN, Yi NING, authorCompany=null, correspAuthors=Dan SHAN, 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=1322531641390649904, articleId=1322531638416888364, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=改进NGO优化MVMD-BiLSTM的锂电池剩余使用寿命预测, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

为解决锂电池剩余使用寿命RUL（remaining useful life）预测过程中存在的噪声干扰、容量增生导致模型预测精度低的问题，提出1种将参数优化后的多元变分模态分解MVMD（multivariate variational modal decomposition）和双向长短期记忆BiLSTM（bi-directional long and short-term memory）网络相结合的RUL预测方法。从锂电池充放电过程中提取多个健康因子HF（health factor）作为模型输入，将正余弦优化算法和鲸鱼螺旋捕食机制结合引进北方苍鹰优化算法，利用其对MVMD参数寻优。然后利用寻优后的MVMD分解HF为若干个平稳分量，最后利用寻优后的多层BiLSTM进行预测，叠加求和得出最终RUL结果。应用NASA和CALCE数据集与其他方法进行对比分析，结果表明所提方法具有更高的预测精度和泛化适应性。

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郭喜峰（1981—　），男，博士，教授。研究方向：能源管理，新能源发电。E-mail：guoxifeng@stu.sjzu.edu.cn。

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郭喜峰（1981—　），男，博士，教授。研究方向：能源管理，新能源发电。E-mail：guoxifeng@stu.sjzu.edu.cn。

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黄裕海（2000—　），男，硕士。研究方向：锂电池储能。E-mail：Huangyuhai@stu.sjzu.edu.cn。

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黄裕海（2000—　），男，硕士。研究方向：锂电池储能。E-mail：Huangyuhai@stu.sjzu.edu.cn。

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原宝龙（1970—　），男，硕士研究生，教授级高级工程师。研究方向：电池储能技术。E-mail：yuanbaolong@stu.sjzu.edu.cn。

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原宝龙（1970—　），男，硕士研究生，教授级高级工程师。研究方向：电池储能技术。E-mail：yuanbaolong@stu.sjzu.edu.cn。

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宁一（1988—　），女，博士，讲师。研究方向：能源负荷管理。E-mail：vipningyi@stu.sjzu.edu.cn。

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宁一（1988—　），女，博士，讲师。研究方向：能源负荷管理。E-mail：vipningyi@stu.sjzu.edu.cn。

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