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Article(id=1322531702199665505, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531607177707705, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.5.248, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709049600000, receivedDateStr=2024-02-28, revisedDate=1712592000000, revisedDateStr=2024-04-09, acceptedDate=1713283200000, acceptedDateStr=2024-04-17, onlineDate=1793245729255, onlineDateStr=2026-10-29, pubDate=1780070400000, pubDateStr=2026-05-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245729255, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245729255, creator=13701087609, updateTime=1793245729255, updator=13701087609, issue=Issue{id=1322531607177707705, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='5', pageStart='1', pageEnd='348', issueExtLink='null', onlineDate='null', pubDate='1780070400000', pubDateStr='2026-05-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793245706600, creator='13701087609', updateTime=1793245706600, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=248, endPage=255, ext={EN=ArticleExt(id=1322531703785112418, articleId=1322531702199665505, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Estimation of Lithium-ion Battery State of Health Based on Stacking Model, columnId=1322506830719341418, journalTitle=Journal of Power Supply, columnName=AI and Power Electronics, runingTitle=null, highlight=null, articleAbstract=

Accurate estimation of the battery state of health (SOH) can effectively improve the safe operation of system. Aiming at the problem of unstable SOH estimation accuracy under different working conditions, a stacking model-based method for lithium-ion battery SOH estimation is proposed. The method firstly carries out incremental capacity (IC) analysis for the charging data, and extracts the health factor as an effective feature, and then constructs an stacking model by stacking the support vector regression (SVR) and back propagation (BP) neural network model to realize the on-line evaluation of the battery SOH. The experimental results show that the maximum error of the stacking model proposed decreases by 38.1% and 23.7%, and the root mean square error (RMSE) decreases by 47% and 20% compared with the two sub-models. The proposed method can more accurately estimate the SOH under various operating conditions, mitigating the instability factor of the single model estimation.

, authors=Ertao LEI1, Junkun ZHANG1, Jinyu WANG2, Li JIN1, Kai MA1, Ying LI1, authorsList=Ertao LEI, Junkun ZHANG, Jinyu WANG, Li JIN, Kai MA, Ying LI, authorCompany=null, correspAuthors=Junkun ZHANG, 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=1322531704296817509, articleId=1322531702199665505, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于集成模型的锂离子电池SOH估计, columnId=1322506830924862316, journalTitle=电源学报, columnName=人工智能与电力电子, runingTitle=null, highlight=null, articleAbstract=

准确估计电池健康状态SOH（state of health）可以有效提升电池系统运行安全性。针对不同工况下SOH估计精度不稳定的问题，提出一种基于集成模型的锂离子电池SOH估计算法。首先对电池充电阶段开展容量增量IC（incremental capacity）分析，并提取健康因子作为电池SOH的有效特征，接着采用支持向量回归SVR（support vector regression）模型与反向传播BP（back propagation）神经网络模型组合构建集成模型，实现对电池SOH的在线评估。实验结果表明，所提出的集成模型相较于2个子模型最大误差RMSE（root mean square error）分别下降了38.1%和23.7%，均方根误差分别下降了47.8%和20.0%，所提方法能更准确估计不同工况下的电池SOH，削弱了单个模型估计的不稳定性因素。

, authors=雷二涛1, 张浚坤1, 王瑾瑜2, 金莉1, 马凯1, 李盈1, authorsList=雷二涛, 张浚坤, 王瑾瑜, 金莉, 马凯, 李盈, authorCompany=null, correspAuthors=张浚坤, authorNote=

雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

, correspAuthorsNote=
张浚坤（1992—　），男，通信作者，博士研究生，工程师。研究方向：新能源，储能及特种电源。E-mail：。
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雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

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雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

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王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

"}, bioImg=null, bioContent=

王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

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金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

"}, bioImg=null, bioContent=

金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

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马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

"}, bioImg=null, bioContent=

马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

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李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

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李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

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基于集成模型的锂离子电池SOH估计
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雷二涛 1 , 张浚坤 1 , 王瑾瑜 2 , 金莉 1 , 马凯 1 , 李盈 1
电源学报 | 人工智能与电力电子 2026,24(5): 248-255
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电源学报 |人工智能与电力电子 2026 , 24 (5) : 248 -255
基于集成模型的锂离子电池SOH估计
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雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

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雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

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王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

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王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

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金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

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金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

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马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

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马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

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李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

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李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

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雷二涛1 , 张浚坤1 , 王瑾瑜2 , 金莉1 , 马凯1 , 李盈1
作者信息
  • 1.广东电网有限责任公司电力科学研究院，广州　510080
  • 2.北京交通大学电气工程学院，北京　100044
通讯作者:
张浚坤（1992—　），男，通信作者，博士研究生，工程师。研究方向：新能源，储能及特种电源。E-mail：。
作者简介:

雷二涛（1987—　），男，博士研究生，高级工程师。研究方向：电能质量，新能源及储能。E-mail：。

王瑾瑜（1996—　），女，博士。研究方向：锂离子电池寿命预测。E-mail：。

金莉（1989—　），女，博士研究生，高级工程师。研究方向：电力电子在电力系统中的应用，新能源及储能技术。E-mail：。

马凯（1985—　），男，硕士研究生，正高级工程师。研究方向：柔性直流，电化学储能技术。E-mail：。

李盈（1994—　），女，硕士研究生，工程师。研究方向：电力电子与储能技术。E-mail：。

Estimation of Lithium-ion Battery State of Health Based on Stacking Model
Ertao LEI1 , Junkun ZHANG1 , Jinyu WANG2 , Li JIN1 , Kai MA1 , Ying LI1
Affiliations
  • 1.Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
  • 2.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
出版时间: 2026-05-30 doi: 10.13234/j.issn.2095-2805.2026.5.248
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准确估计电池健康状态SOH（state of health）可以有效提升电池系统运行安全性。针对不同工况下SOH估计精度不稳定的问题，提出一种基于集成模型的锂离子电池SOH估计算法。首先对电池充电阶段开展容量增量IC（incremental capacity）分析，并提取健康因子作为电池SOH的有效特征，接着采用支持向量回归SVR（support vector regression）模型与反向传播BP（back propagation）神经网络模型组合构建集成模型，实现对电池SOH的在线评估。实验结果表明，所提出的集成模型相较于2个子模型最大误差RMSE（root mean square error）分别下降了38.1%和23.7%，均方根误差分别下降了47.8%和20.0%，所提方法能更准确估计不同工况下的电池SOH，削弱了单个模型估计的不稳定性因素。

锂离子电池  /  容量增量分析  /  健康状态估计  /  集成模型

Accurate estimation of the battery state of health (SOH) can effectively improve the safe operation of system. Aiming at the problem of unstable SOH estimation accuracy under different working conditions, a stacking model-based method for lithium-ion battery SOH estimation is proposed. The method firstly carries out incremental capacity (IC) analysis for the charging data, and extracts the health factor as an effective feature, and then constructs an stacking model by stacking the support vector regression (SVR) and back propagation (BP) neural network model to realize the on-line evaluation of the battery SOH. The experimental results show that the maximum error of the stacking model proposed decreases by 38.1% and 23.7%, and the root mean square error (RMSE) decreases by 47% and 20% compared with the two sub-models. The proposed method can more accurately estimate the SOH under various operating conditions, mitigating the instability factor of the single model estimation.

Lithium-ion battery  /  incremental capacity (IC) analysis  /  estimation of state of health (SOH)  /  stacking model
雷二涛, 张浚坤, 王瑾瑜, 金莉, 马凯, 李盈. 基于集成模型的锂离子电池SOH估计. 电源学报, 2026 , 24 (5) : 248 -255 . DOI: 10.13234/j.issn.2095-2805.2026.5.248
Ertao LEI, Junkun ZHANG, Jinyu WANG, Li JIN, Kai MA, Ying LI. Estimation of Lithium-ion Battery State of Health Based on Stacking Model[J]. Journal of Power Supply, 2026 , 24 (5) : 248 -255 . DOI: 10.13234/j.issn.2095-2805.2026.5.248

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文章信息
doi: 10.13234/j.issn.2095-2805.2026.5.248
  • 接收时间：2024-02-28
  • 首发时间：2026-10-29
  • 出版时间：2026-05-30
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  • 收稿日期：2024-02-28
  • 修回日期：2024-04-09
  • 录用日期：2024-04-17
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作者信息
    1.广东电网有限责任公司电力科学研究院，广州　510080
    2.北京交通大学电气工程学院，北京　100044

通讯作者:

张浚坤（1992—　），男，通信作者，博士研究生，工程师。研究方向：新能源，储能及特种电源。E-mail：。
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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.5.248
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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