Article(id=1309640003043557452, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250754, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1765900800000, revisedDateStr=2025-12-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172108659, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172108659, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172108659, creator=13701087609, updateTime=1790172108659, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=37, endPage=44, ext={EN=ArticleExt(id=1309640003228106829, articleId=1309640003043557452, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Energy Consumption Prediction of Electric Vehicle Based on Driving Style Recognition, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the critical issues of range anxiety among battery electric vehicle users and the insufficient accuracy of energy consumption prediction, this paper proposes a two-stage energy consumption prediction method considering driving styles. First, characteristic parameters strongly related to driving styles, such as speed, accelerator pedal depth, and brake pedal depth, are extracted and calculated, and a back propagation neural network is used to construct a driving style recognition model. Second, for different driving styles, decision tree models are respectively used for energy consumption prediction. Verified by real vehicle test data, the average absolute percentage error (MAPE) of the energy consumption prediction model proposed in this paper is 5.84%. Compared with the average energy consumption method, the Deep Neural Network (DNN) and decision tree energy consumption prediction methods that do not consider driving styles, the errors are reduced by 16.74%, 9.77% and 1.44% respectively. The research results demonstrate that the energy consumption prediction method incorporating user driving style can effectively alleviate user range anxiety and significantly improve the accuracy of energy consumption prediction.

, authors=Zhoulun Zhao, Zhao Lu, Qiangqiang Ni, Haozhe Dong, Xiaohui Hu, authorsList=Zhoulun Zhao, Zhao Lu, Qiangqiang Ni, Haozhe Dong, Xiaohui Hu, authorCompany=null, correspAuthors=Zhao Lu, 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=1309640003442016334, articleId=1309640003043557452, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于驾驶风格识别的纯电动汽车能耗预测研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对纯电动汽车用户续航里程焦虑与能耗预测精度不足的关键问题，提出了一种考虑驾驶风格的两阶段能耗预测方法。首先提取并计算速度、加速踏板深度、制动踏板深度等与驾驶风格强相关的特征参数，并使用反向传播（Back Propagation，BP）神经网络构建驾驶风格识别模型；其次，针对不同的驾驶风格，分别使用决策树模型进行能耗预测。经实车测试数据验证，本文提出的能耗预测模型的平均绝对百分比误差为5.84%；相较于平均能耗法、不考虑驾驶风格的深度神经网络（Deep Neural Network，DNN）和决策树能耗预测方法，误差分别降低了16.74%、9.77%和1.44%。研究结果表明，考虑用户驾驶风格的能耗预测方法可有效缓解用户里程焦虑，并显著提升能耗预测的精准度。

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