Article(id=1317501485315896249, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260160, 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=1792046432093, onlineDateStr=2026-10-15, pubDate=1791129600000, pubDateStr=2026-10-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792046432093, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792046432093, creator=13701087609, updateTime=1792046432093, updator=13701087609, issue=Issue{id=1317501484401541130, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='10', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1791129600000', pubDateStr='2026-10-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792046431875, creator='13701087609', updateTime=1792121205555, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317815108026356240, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317815108026356241, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=23, endPage=30, ext={EN=ArticleExt(id=1317501485475279802, articleId=1317501485315896249, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on Adaptive Energy Recovery Control for Electric Vehicles Based on Multi-Source Information Fusion, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

An adaptive regenerative braking control method based on multi-source information fusion is proposed to address the fixed recovery intensity of conventional electric vehicles, insufficient adaptability to complex conditions and degraded deceleration consistency when battery recovery capability is limited. Navigation speed limits, preceding and multiple dynamic targets, road grade, road-surface state and available battery recovery power are integrated in a perception-decision-execution architecture. Signal validity judgment, dynamic regenerative torque calculation and electro-hydraulic compensation are then designed. The hydraulic compensation is activated only when the available battery recovery power cannot meet the deceleration demand, thereby improving redundancy and deceleration consistency. Road tests on the Voyah Dreamer vehicle model show that the proposed method improves energy consumption by 4.02%, reduces braking frequency by 44.64%, and limits deceleration fluctuation within ±0.02 g under multi-speed-limit and mildly congested conditions. The method improves energy economy and drivability while maintaining vehicle safety.

, authors=Ke Zeng, Xu Cai, Zhirong Zhou, Hongzhang Fang, Ming Zhang, Ya Zhang, Bo Wu, Wanting Li, authorsList=Ke Zeng, Xu Cai, Zhirong Zhou, Hongzhang Fang, Ming Zhang, Ya Zhang, Bo Wu, Wanting Li, 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=1317501485731132347, articleId=1317501485315896249, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于多源信息融合的电动汽车自适应能量回收控制研究, columnId=1317501485198458892, journalTitle=汽车文摘, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

针对电动汽车滑行能量回收强度固定、复杂工况适应性不足以及电池回收受限时，减速度一致性下降等问题，提出一种基于多源信息融合的自适应能量回收控制方法。该方法融合导航限速、前车及多动态目标、道路坡度、前方路面和电池可回收功率等信息，构建“感知-决策-执行”跨域协同架构，并设计信号有效性判定、动态回收扭矩计算和电液协同补偿策略。电液补偿仅在电池可回收功率不足时介入，用于补足减速度缺口并提升安全冗余。以岚图梦想家车型为例开展道路验证，结果表明：在多限速和轻度拥堵工况下，平均能耗改善率为4.02%，制动频次减少44.64%，减速度波动控制在±0.02 g以内，所提方法可在保证安全性的同时改善能耗和驾驶平顺性。

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