Article(id=1321491387435044877, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.7.313, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1691510400000, receivedDateStr=2023-08-09, revisedDate=1693238400000, revisedDateStr=2023-08-29, acceptedDate=1694016000000, acceptedDateStr=2023-09-07, onlineDate=1792997698887, onlineDateStr=2026-10-26, pubDate=1764432000000, pubDateStr=2025-11-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997698887, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997698887, creator=13701087609, updateTime=1792997698887, updator=13701087609, issue=Issue{id=1321491360620851250, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='7', pageStart='1', pageEnd='348', issueExtLink='null', onlineDate='null', pubDate='1764432000000', pubDateStr='2025-11-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997692494, creator='13701087609', updateTime=1792997936183, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492382776931282, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492382776931283, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=313, endPage=320, ext={EN=ArticleExt(id=1321491387728646158, articleId=1321491387435044877, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Construction of Thermoelectric Coupling Model for Lithiumion Batteries Based on BP Neural Network and DP Equivalent Circuit, columnId=1152281491788100462, journalTitle=Journal of Power Supply, columnName=Battery and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Owing to the advantages of lithium-ion batteries such as high energy density, long service life and low internal resistance, they have been widely applied to the fields of electric vehicles and energy storage. During their utilization process, their temperature will increase due to the internal chemical reactions. However, the traditional equivalent circuit model of batteries based on the voltage and current characteristics does not consider the thermal process, which cannot satisfy the precision requirement for battery models in practical applications. To solve this problem, a construction method for high-precision thermoelectric coupling model with measurable parameters is proposed. First, a thermal model of batteries based on back propagation neural networks is established using the Bayesian regularization method, and the battery surface temperature under different charge and discharge conditions is obtained. Then, the obtained characteristics of battery temperature are further input into a dual-polarization equivalent circuit model, so as to construct a thermoelectric coupling model of lithium-ion batteries. Compared with the traditional equivalent circuit model method which does not take into account the thermal effect, the established model can more accurately predict the changes in the temperature, capacitance and internal resistance of lithium-ion batteries with time, with an average reduction of 44.395% in output voltage error.

, authors=Huanlun DU, Zhefan LI, Qionglin SHI, Kai JIANG, authorsList=Huanlun DU, Zhefan LI, Qionglin SHI, Kai JIANG, authorCompany=null, correspAuthors=Kai JIANG, 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=1321491388278099985, articleId=1321491387435044877, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于BP神经网络与DP等效电路的锂离子电池热电耦合模型构建, columnId=1149830274575463188, journalTitle=电源学报, columnName=电池与储能, runingTitle=null, highlight=null, articleAbstract=

锂离子电池因具有高能量密度、长寿命和低内阻等优点而被广泛应用于电动汽车以及储能领域。电池在使用过程中会因为内部电化学反应而温度升高，目前传统的基于电压、电流特性的电池等效电路模型缺乏对该热过程的描述，难以满足实际应用中对电池模型的精度要求。可以通过提出1种新的参数易于测量的高精度热电耦合模型构建方法解决上述问题。首先利用贝叶斯正则化方法建立基于反向传播神经网络的电池热模型，获取在不同充放电工况下的电池表面温度，再将获取的电池温度特性输入到双极化等效电路模型中，从而构建锂离子电池热电耦合模型。对比传统不考虑热效应的等效电路模型方法，所构建的热电耦合模型可以更准确地预测锂离子电池的温度、电容和内阻随时间的变化，输出电压误差平均减小了44.395%。

, authors=杜焕伦, 李哲帆, 石琼林, 蒋凯, authorsList=杜焕伦, 李哲帆, 石琼林, 蒋凯, authorCompany=null, correspAuthors=蒋凯, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=kjWYO1CR92Gf5Q9JiP+z0A==, magXml=ii2R+j8BsQrQHGQIPdpbWw==, pdfUrl=null, pdf=ahTU/6oJMqotk3vUTxBYBA==, pdfFileSize=1712882, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=VgnASgVRTA0Vu746Ju1GhA==, mapNumber=null, fund=null)}, authors=[Author(id=1321491390421389336, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491387435044877, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=duhuanlun1@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321491390635298842, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491387435044877, authorId=1321491390421389336, language=EN, stringName=Huanlun DU, firstName=Huanlun, middleName=null, lastName=DU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491390698213403, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491387435044877, authorId=1321491390421389336, language=CN, stringName=杜焕伦, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=华中科技大学电气与电子工程学院，武汉　430074, bio={"content":"

杜焕伦（2001—　），男，本科。研究方向：电力系统与电池建模。E-mail：duhuanlun1@163.com。

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杜焕伦（2001—　），男，本科。研究方向：电力系统与电池建模。E-mail：duhuanlun1@163.com。

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李哲帆（2002—　），男，本科。研究方向：电池建模。E-mail：u202012564@hust.edu.cn。

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李哲帆（2002—　），男，本科。研究方向：电池建模。E-mail：u202012564@hust.edu.cn。

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石琼林（1994—　），男，博士研究生。研究方向：电池机理-数据建模与老化研究。E-mail：shiql@hust.edu.cn。

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石琼林（1994—　），男，博士研究生。研究方向：电池机理-数据建模与老化研究。E-mail：shiql@hust.edu.cn。

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