Article(id=1309639487127388547, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639482152943959, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260019, 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=1790171985655, onlineDateStr=2026-09-23, pubDate=1783180800000, pubDateStr=2026-07-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171985655, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171985655, creator=13701087609, updateTime=1790171985655, updator=13701087609, issue=Issue{id=1309639482152943959, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='7', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1783180800000', pubDateStr='2026-07-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171984469, creator='13701087609', updateTime=1790233653216, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309898139683877588, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639482152943959, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309898139683877589, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639482152943959, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=13, endPage=17, ext={EN=ArticleExt(id=1309639487316132228, articleId=1309639487127388547, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Methods for Improving the Efficiency of Electric Vehicle Motor Systems, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In response to the complex and variable real-world operating conditions of electric vehicles and the technical prololems of traditional motor systems relying on calibration based on single standard operating conditions leading to low efficiency across the entire operating range, this paper proposes an adaptive optimization method integrating real-time operating condition perception, multi-parameter collaborative control, and thermal management linkage. This method collects multi-dimensional operational data such as motor speed, torque, coolant temperature/flow rate, constructs a three-dimensional feature space (speed-torque-thermal state) with dynamic partitioning, and collaboratively optimizes power device driving parameters (gate resistance, driving voltage), Pulse Width Modulation Switch (PWM) switching frequency, and cooling system operation strategy based on partition characteristics. Compared to the traditional fixed redundancy design for the most severe operating conditions, the proposed method achieves precise loss control across the entire operating range through "hardware-software collaboration+thermal-electric coupling" while satisfying system safety and reliability requirements. Bench test verification shows that this method improves the motor system's CLTC comprehensive cycle efficiency by approximately 0.34~0.67 percentage points, effectively reducing the vehicle's electricity consumption per 100 km and significantly extending its driving range.

, authors=Hua Zhong, Zhiqiang Liu, authorsList=Hua Zhong, Zhiqiang Liu, 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=1309639487555207557, articleId=1309639487127388547, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=电动汽车电机系统效率提升方法, columnId=0, journalTitle=汽车文摘, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对电动汽车实际运行工况复杂多变，传统电机系统依赖单一标准工况标定导致全工况效率偏低的技术问题，提出一种融合实时工况感知、多参数协同调控与热管理联动的自适应优化方法。该方法通过采集电机转速、扭矩、冷却液温度/流量等多维运行数据，构建转速-扭矩-热状态三维特征空间并动态分区，基于分区特性协同优化功率器件驱动参数（栅极电阻、驱动电压）、脉冲宽度调制开关（PWM）的开关频率及冷却系统运行策略。相较于传统最恶劣工况固定冗余设计，所提方法在满足系统安全可靠性的前提下，通过“软硬协同+热电联动”实现全工况损耗精准控制。试验结果表明，所提出方法使电机系统CLTC综合工况效率提升0.34~0.67百分点，有效降低整车百公里电耗，显著延长续驶里程。

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