Article(id=1321491336990143349, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.6.76, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666627200000, receivedDateStr=2022-10-25, revisedDate=1679500800000, revisedDateStr=2023-03-23, acceptedDate=1683475200000, acceptedDateStr=2023-05-08, onlineDate=1792997686857, onlineDateStr=2026-10-26, pubDate=1761753600000, pubDateStr=2025-10-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997686857, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997686857, creator=13701087609, updateTime=1792997686857, updator=13701087609, issue=Issue{id=1321491279368794534, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='6', pageStart='1', pageEnd='352', issueExtLink='null', onlineDate='null', pubDate='1761753600000', pubDateStr='2025-10-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997673122, creator='13701087609', updateTime=1792997908848, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492268125631431, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492268125631432, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491279368794534, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=76, endPage=85, ext={EN=ArticleExt(id=1321491337388602230, articleId=1321491336990143349, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Variable Universe Fuzzy PI Predictive Control for Novel On-board Charging DC-DC Converters, columnId=1152281491305755501, journalTitle=Journal of Power Supply, columnName=DC-DC Converters, runingTitle=null, highlight=null, articleAbstract=
Aimed at the problems in traditional on-board charging DC-DC converters such as a narrow soft switching range, poor dynamic response and rectifier diode voltage spike, a novel wide-range soft switching full-bridge converter is designed, and a double-closed-loop control strategy combining variable universe fuzzy PI with model prediction is adopted. First, the working mode of the novel full-bridge converter is analyzed, and its simplified state space average model is established. Second, model predictive control is used in the current loop, and compensation improvements are made to obtain the predictive expression of inductance current at the time point of k+2, thereby improving the large time delay of an actual system. Third, the control of voltage loop is changed to the transform domain fuzzy PI control with fuzzy universe self-adaptation, so as to optimize the system adaptability and improve the control precision. Simulation results show that the soft switching range of the novel converter is extended to 30% of load, and the rectifier diode voltage spike is also eliminated. Under sudden changes in load, the proposed control strategy reduces the output voltage regulation time by 17.8% and 25.6%, as well as 12.9% and 9.1%, respectively. Finally, the effectiveness of the proposed topology and control strategy was verified by experimental results.
, authors=Junjie SHANG
1, Taiqiang CAO
1, Xuan LIN
1, Jin DAI
2, Min ZHENG
1, authorsList=Junjie SHANG, Taiqiang CAO, Xuan LIN, Jin DAI, Min ZHENG, authorCompany=null, correspAuthors=Taiqiang CAO, 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=1321491337921278841, articleId=1321491336990143349, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=一种新型车载充电DC-DC变换器的变论域模糊PI预测控制, columnId=1149829799759339522, journalTitle=电源学报, columnName=DC-DC变换器, runingTitle=null, highlight=null, articleAbstract=
针对传统车载充电DC-DC变换器软开关范围窄、动态响应差及整流二极管存在电压尖峰等问题,设计了1种新型宽范围软开关全桥变换器,并采用1种变论域模糊PI与模型预测相结合的双闭环控制策略。首先,分析新型全桥变换器的工作模态,建立简化后的变换器状态空间平均模型。然后,在电流环采用模型预测控制,并进行补偿改进,得到k+2时刻电感电流的预测表达式,进而改善实际系统的大时滞问题。其次,将电压环改用模糊论域自适应的变论域模糊PI控制,优化系统适应性、提高控制精度。仿真结果表明,新型变换器的软开关范围扩大至30%负载,并消除了整流二极管电压尖峰。所提控制策略在负载突变情况下,输出电压调节时间分别减少17.8%、25.6%和12.9%、9.1%。最后通过实验验证了所提拓扑和控制策略的有效性。
, authors=尚俊杰
中国电源学会学生会员1, 曹太强
1, 林轩
1, 代锦
2, 郑敏
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尚俊杰(1996— ),男,中国电源学会学生会员,硕士研究生。研究方向:电力电子与电气传动。E-mail:shangjunjie2020@163.com。
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尚俊杰(1996— ),男,中国电源学会学生会员,硕士研究生。研究方向:电力电子与电气传动。E-mail:shangjunjie2020@163.com。
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林轩(1988— ),男,博士,讲师。研究方向:轨道交通电气化与自动化。E-mail:linstu@163.com。
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林轩(1988— ),男,博士,讲师。研究方向:轨道交通电气化与自动化。E-mail:linstu@163.com。
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