Article(id=1318999847387123836, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2025.06.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741708800000, receivedDateStr=2025-03-12, revisedDate=1753977600000, revisedDateStr=2025-08-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1792403669448, onlineDateStr=2026-10-19, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792403669448, onlineIssueDateStr=2026-10-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792403669448, creator=13701087609, updateTime=1792403669448, updator=13701087609, issue=Issue{id=1318999663622079298, tenantId=1146029695717560320, journalId=1146123302524792850, year='2025', volume='38', issue='6', pageStart='1', pageEnd='147', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792403625636, creator='13701087609', updateTime=1792733312669, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320382471334646693, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320382471334646694, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=16, endPage=25, ext={EN=ArticleExt(id=1318999847592644733, articleId=1318999847387123836, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Key Technologies and Demonstration Applications of a New-Generation Stored Regenerative Braking Energy Utilization System, columnId=1152669340382966151, journalTitle=Urban Rapid Rail Transit, columnName=Forum of Rapid Rail Transit, runingTitle=null, highlight=null, articleAbstract=

To address technical challenges in capacity configuration, safety design, energy management, and energy-saving evaluation for urban rail transit regenerative braking energy utilization systems, this paper proposes the new-generation stored regenerative braking energy utilization system, eRAM. We develop: (1) capacity optimization model and algorithm based on parameter decomposition; (2) a multi-level safety architecture considering rail transit application scenarios; (3) an interval energy dynamic allocation algorithm based on key parameter identification; (4) distributed cooperative control strategy based on deep reinforcement learning; and (5) data-driven condition monitoring and life prediction of energy storage components based on data driven. We present the basic principles and key technologies of each major component and evaluate their demonstration performance on the Beijing Metro Batong Line, providing a useful reference for advancing regenerative braking energy recovery and utilization in urban rail transit.

, authors=Zhihong ZHONG¹, Yajie ZHAO¹, Fei LIN¹, Zhongping YANG¹, Yuejia SUN², Chuanfei DIAO¹, authorsList=Zhihong ZHONG, Yajie ZHAO, Fei LIN, Zhongping YANG, Yuejia SUN, Chuanfei DIAO, 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=1318999847882051710, articleId=1318999847387123836, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=新一代储存式再生制动能量利用系统关键技术及示范应用, columnId=1152669340680761737, journalTitle=都市快轨交通, columnName=快轨论坛, runingTitle=null, highlight=null, articleAbstract=

针对城市轨道交通再生制动能量利用系统在容量配置、安全设计、能量管理、节能评估等方面的技术挑战，提出新一代储存式再生制动能量利用系统eRAM。创新性地构建了：基于参数分解的容量优化模型和算法；考虑轨道交通应用场景的多级安全架构；基于关键参数辨识的区间能量动态分配算法；基于深度强化学习的分布式协同控制策略；基于数据驱动的储能元件状态监测与寿命预测。对各个重要组成部分的基本原理及其关键技术进行说明，并展示和分析这些技术在北京地铁八通线上应用示范的实际效果，以期为城市轨道交通再生制动能量吸收利用技术的发展提供有益的参考。

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钟志宏，男，博士，讲师，从事轨道交通牵引控制和节能运行，zhzhong@bjtu.edu.cn

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钟志宏，男，博士，讲师，从事轨道交通牵引控制和节能运行，zhzhong@bjtu.edu.cn

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