摘要
The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.
基金
supported in part by National Natural Science Foundation(NNSF)of China(Nos.61803079,61890924,61991404)
in part by Fundamental Research Funds for the Central Universities(No.N2108006)
in part by Liaoning Revitalization Talents Program(No.XLYC1907087)。
作者简介
Shiji Sun received the B.S.degree in the College of Information Science and Engineering from Northeastern University,Shenyang,China,in 2021.He is currently pursuing the M.S.degree in the State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University.His current research interests include communication and efficiency,privacy protection and machine learning;Corresponding author:Mingxin Kang received the M.S.degree in Automotive Engineering in 2012 from Yanshan University and the Ph.D.degree in Control Engineering in 2015 from Sophia University(Japan).He is currently an associate Professor in the State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang,China.His research interests include realtime optimization control,logical optimal control for automotive powertrain system and Traffic-info based energy optimization of intelligent vehicle system.Email:kangmx@mail.neu.edu.cn;Yuzhe Li received the B.S.degree in mechanics from Peking University,China in 2011 and the Ph.D.degree in electronic and computer engineering from the Hong Kong University of Science and Technology(HKUST)in 2015.He is currently a Professor in the State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang,China.His research interests include cyber-physical systems security,sensor power control and networked state estimation.
