Article(id=1308462990337004325, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250315, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1765987200000, revisedDateStr=2025-12-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891486962, onlineDateStr=2026-09-20, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891486962, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891486962, creator=13701087609, updateTime=1789891486962, updator=13701087609, issue=Issue{id=1308462989946934053, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='6', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891486870, creator='13701087609', updateTime=1789892888486, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468868809248787, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468868809248788, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1308462990504776486, articleId=1308462990337004325, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=A Vehicle Handling Stability Control Method Considering Motor Response Differences, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the stability issues of disributed drive electric vehicles attributed to the response discrepancies of in-wheel motors, such as loss of control, understeer and oversteer, the paper proposes a hierarchical vehicle stability control framework. The upper layer incorporates an additional yaw moment controller designed using Model Predictive Control (MPC), which computes the required corrective yaw moment online to track both the desired yaw rate and the sideslip angle at the vehicle's center of mass. The lower layer performs optimal four-wheel torque allocation while coordinating vehicle stability and energy efficiency. A co-simulation platform combining Simulink and CarSim is developed, and straight-line driving and double-lane-change maneuvers are selected for validation. To assess the effectiveness of the MPC-based controller, comparative simulations are conducted against a reference upper-layer controller based on Sliding Mode Control (SMC). The results indicate that the proposed hierarchical strategy effectively compensates for motor response discrepancies, enhances vehicle stability, and improves overall driving economy. The simulation results demonstrate that under the straight-line driving scenario, the proposed MPC strategy reduces the lateral deviation from 13.01 m to 1.71 m, achieving a control efficiency of 86.9%, while simultaneously decreasing motor energy consumption by approximately 6.8%. In the double lane change maneuver, the strategy effectively suppresses the oscillations of both yaw rate and sideslip angle. These results validate that the proposed hierarchical control strategy can effectively compensate for the impact of hub motor response discrepancies, thereby significantly enhancing driving stability and improving overall vehicle energy efficiency.

, authors=Shuai Wu, Hao Jin, Yuhang Yang, Jianpeng Li, Chengkang Zhao, authorsList=Shuai Wu, Hao Jin, Yuhang Yang, Jianpeng Li, Chengkang Zhao, 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=1308462990722880295, articleId=1308462990337004325, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=考虑电机响应差异的电动汽车操纵稳定性控制方法, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对分布式驱动电动汽车因轮毂电机响应差异而引发的车辆失控、转向不足和转向过度等稳定性问题，提出了一种分层式车辆稳定性控制策略。上层采用基于模型预测控制（MPC）的附加横摆力矩控制器，可在线计算附加横摆力矩，以跟踪车辆期望横摆角速度和质心侧偏角，下层控制器负责四轮驱动力矩分配，在保证车辆稳定性的同时兼顾能量消耗优化。搭建了基于Simulink与CarSim的联合仿真平台，以上层基于滑模控制（SMC）的附加横摆力矩控制器作为对比开展仿真分析，结果表明：MPC算法在直线工况下将车辆横向偏移由13.01 m收敛至1.71 m，对横向偏移的抑制率达86.9%，且电机能耗降低约6.8%，在双移线工况下有效抑制了横摆角速度与质心侧偏角的振荡，证明了该策略能够有效补偿轮毂电机响应差异的影响，显著提升车辆行驶稳定性，并改善整车经济性。

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