Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation

doi:10.1155/2020/6057870

CQUET-IR

	Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation
	Wang, Yong 1,2; Cai, Hongguo 3; Liao, Yinghua 2; Gao, Jun 1
	2020
发表期刊	COMPLEXITY
ISSN	1076-2787
卷号	2020
摘要	Equipped with two power sources, the dual-driving powertrain system for pure electric vehicles has a driving mode different from traditional electric vehicles. Under the premise that the structural form of the transmission system remains unchanged, the following transmission schemes can be adopted for double drive electric vehicles according to the demand power: the main and auxiliary electric transmission scheme (two motors are driven separately with dual-motor coupling drive), the transmission scheme in which the two motors always maintain coupling drive, and the speed-regulating type electric transmission scheme (the main motor is always responsible for driving, and the auxiliary motor is responsible for speed regulation). Therefore, a significant difference exists in the design methods of the power transmission system of double drive electric vehicles and existing vehicles. As for such differences, this paper adopts intelligent algorithm to design the parameters of the transmission system and introduces the genetic algorithm into the optimization design of parameters to obtain the optimal vital parameters of the power transmission system based on computer simulation. The prototype car used in this paper is a self-owned brand car; MATLAB/Simulink platform is used to build the vehicle simulation model, which is used for the computer simulation analysis of the vehicle dynamic performance and economy. It can be seen from the analysis result that the system parameters obtained by using the global optimization method proposed in this study can improve the vehicle dynamic performance and economic performance to varying degrees, which proves the efficiency and feasibility of the optimization method. © 2020 Yong Wang et al.
关键词	Electric automobiles Electric drives Electric power transmission Electric power transmission networks Genetic algorithms Global optimization MATLAB Power transmission Powertrains Speed regulators Traction motors Vehicle performance Economic performance Global optimization method Intelligent Algorithms Power transmission systems Pure electric vehicles Transmission schemes Transmission systems Vehicle dynamic performance
DOI	10.1155/2020/6057870
收录类别	EI
语种	英语
出版者	Hindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States
EI入藏号	20203309052343
EI分类号	602.2 Mechanical Transmissions ; 662.1 Automobiles ; 662.4 Automobile and Smaller Vehicle Components ; 706.1.1 Electric Power Transmission ; 732.1 Control Equipment ; 921 Mathematics ; 921.5 Optimization Techniques
原始文献类型	Journal article (JA)
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3152
专题	重庆电子科技职业大学
作者单位	1.Chong Qing College of Electronic Engineering, Department of Intelligent Manufacturing and Automotive, Chongqing; 401331, China; 2.Sichuan Provincial Key Lab of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong; 643002, China; 3.Guangxi College of Education, Department of Mathematics and Computer Science, Nanning; 530023, China
推荐引用方式 GB/T 7714	Wang, Yong,Cai, Hongguo,Liao, Yinghua,et al. Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation[J]. COMPLEXITY,2020,2020.
APA	Wang, Yong,Cai, Hongguo,Liao, Yinghua,&Gao, Jun.(2020).Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation.COMPLEXITY,2020.
MLA	Wang, Yong,et al."Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation".COMPLEXITY 2020(2020).