Article(id=1309639865973695210, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20250060, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172075980, onlineDateStr=2026-09-23, pubDate=1788537600000, pubDateStr=2026-09-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172075980, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172075980, creator=13701087609, updateTime=1790172075980, updator=13701087609, issue=Issue{id=1309639861922005993, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1788537600000', pubDateStr='2026-09-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172075014, creator='13701087609', updateTime=1790233557151, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309897736787427641, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309897736787427642, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639861922005993, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=43, endPage=49, ext={EN=ArticleExt(id=1309639867726914283, articleId=1309639865973695210, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on SOC Estimation and Control Strategy Optimization Methods for Power Batteries of New Energy Vehicles, columnId=1309639862555345898, journalTitle=Automotive Digest, columnName=Special Topic on Automotive Chip Technology, runingTitle=null, highlight=null, articleAbstract=

Aiming at the low accuracy of State of Charge (SOC) estimation and poor adaptability of control strategies under dynamic working conditions for power batteries of new energy vehicles, a multi-model fusion SOC estimation framework integrating Kalman Filtering (KF), Particle Filtering (PF) and Long Short-Term Memory (LSTM) neural network is constructed. Dynamic charging and discharging management strategies based on genetic algorithm and particle swarm optimization are designed. A power battery test platform covering multiple temperatures and charge-discharge rates is built, and simulations and physical tests are carried out under standard UDDS and WLTC working conditions. The test results show that the root mean square error of SOC estimation of the proposed method is reduced to 0.013 5, the battery charge-discharge efficiency is increased by 9.3%, and the maximum discharge current is reduced by 23.7%. The proposed method can slow down battery capacity attenuation and reduce thermal runaway risk, which provides theoretical support and engineering reference for the optimization of battery management systems.

, authors=Huating Pang¹, Jiangning Zhu², Gang Li¹, Fuhui Chen¹, authorsList=Huating Pang, Jiangning Zhu, Gang Li, Fuhui Chen, authorCompany=null, correspAuthors=null, 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=1309639867965989612, articleId=1309639865973695210, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=新能源汽车动力电池SOC估算与控制策略优化方法研究, columnId=1309639862702146540, journalTitle=汽车文摘, columnName=汽车芯片技术专题, runingTitle=null, highlight=null, articleAbstract=

针对新能源汽车动力电池荷电状态（SOC）估算精度不足、动态工况下控制策略适应性差的问题，融合卡尔曼滤波（KF)、粒子滤波（PF）与长短期记忆（LSTM）神经网络构建多模型融合SOC估算架构，设计基于遗传算法、粒子群算法的动态充放电管理策略。搭建多温度、多倍率动力电池测试平台，结合UDDS、WLTC标准工况完成仿真与实测试验。结果表明，所提方法SOC估算均方根误差降至0.013 5，电池充放电效率提升9.3%，最大放电电流降低23.7%，能够减缓电池容量衰减、降低热失控风险，可为动力电池管理系统优化提供理论支撑与工程参考。

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