Article(id=1309639665632772701, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250207, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1748880000000, revisedDateStr=2025-06-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172028215, onlineDateStr=2026-09-23, pubDate=1774281600000, pubDateStr=2026-03-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172028215, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172028215, creator=13701087609, updateTime=1790172028215, updator=13701087609, issue=Issue{id=1309639664567415657, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='3', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1774281600000', pubDateStr='2026-03-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172027961, creator='13701087609', updateTime=1790232938103, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895140290945772, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895140290945773, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=37, ext={EN=ArticleExt(id=1309639665846682206, articleId=1309639665632772701, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Emission Simulation of Car-Following Models Based on Vehicle Specific Power Distribution Error Minimization Calibration, columnId=1309639665217532778, journalTitle=Automobile Technology, columnName=Special Topic on Dynamic Trajectory Prediction, Planning, and Tracking Control Technology of Intelligent Vehicle, runingTitle=null, highlight=null, articleAbstract=

To achieve accurate emission simulation for car-following vehicles, this paper proposes a calibration and optimization method based on minimizing the error between the Vehicle Specific Power (VSP) distribution of car-following models and the measured lead vehicle. By constructing an objective function to minimize VSP distribution error, the parameters of the car-following models are calibrated to enhance the model's realism. Using measured emission data of the lead vehicle, the emission characteristics of various car-following models are fitted and compared. The results show that Gipps, IDM, OVM, GFM, and FVDM models all demonstrate favourable VSP distributions and effective fitting of following vehicle emission; The Gipps model demonstrated optimal performance in VSP distribution fitting accuracy (mean Root Mean Square Error (RMSE) of 0.003 7) and emission factor fitting for CO₂, NO_x, CO, and Partical Number (PN) emission factors (mean relative error to the reference vehicle of 0.93%). In multi-vehicle platooning emission simulations, the Gipps model exhibits the smallest emission fluctuations. The mean relative errors for CO₂, NO_x, CO, and PN emission factors are 0.48%, 0.52%, 0.87%, and 1.89% respectively. The model demonstrates optimal consistency in fitting emissions across all platooning vehicles.

, authors=Zhilei Ma^{1, 2}, Chao He¹, Jiaqiang Li^{1, 2}, authorsList=Zhilei Ma, Chao He, Jiaqiang Li, authorCompany=null, correspAuthors=Chao He, 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=1309639666056397407, articleId=1309639665632772701, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于最小化车辆比功率分布误差标定的车辆跟驰模型排放仿真, columnId=1309639665360139116, journalTitle=汽车技术, columnName=智能车辆动态轨迹预测、规划与跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

为了获得良好的跟驰车辆排放仿真效果，提出一种基于最小化跟驰车辆比功率（VSP）分布与实测头车误差的标定优化方法。利用VSP分布误差最小化目标函数标定跟驰模型参数，优化跟驰模型真实性；结合实测头车的排放数据，拟合多种跟驰模型的排放情况。试验结果表明：Gipps、IDM、OVM、GFM、FVDM模型均表现出良好的VSP分布和跟驰车辆排放拟合效果；Gipps模型在VSP分布拟合精度（均方根误差均值为0.003 7）和CO₂、NO_x、CO、颗粒物数量（PN）排放因子拟合效果（与头车的相对误差均值为0.93%）上表现最优；多车辆跟驰车队排放仿真中，Gipps模型的排放波动最小，CO₂、NO_x、CO、PN排放因子相对误差均值分别为0.48%、0.52%、0.87%、1.89%，各跟驰车辆拟合排放的一致性最优。

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