Particle Swarm Optimization and Real-Road/Driving-Cycle Analysis Based Powertrain System Design for Dual Motor Coupling Electric Vehicle

World Electric Vehicle Journal

Jie Gao¹  Chao Ma¹  Di Tan¹  Dechao Yan¹  Shiwei Jin¹  Kun Yang¹ 

[1] College of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China;

关键词: dual motor configuration;    planetary gear;    driving condition analysis;    real road driving data collection;    parameter matching;    particle swarm optimization;   

DOI  :  10.3390/wevj11040069

来源: DOAJ

【 摘 要 】

In this study, a planetary gear based dual motor coupling electric vehicle is proposed, which achieves higher system efficiency by enabling motor working under high operating efficiency area. Firstly, the dynamic characteristics of the proposed configuration are analyzed and the reasonable working modes are established. Secondly, the optimal dual motor parameters are derived according to the statistical analysis on the typical driving conditions and the collected real road driving data. Especially, the optimal parameters of planetary gear and final transmission ratio are obtained using particle swarm optimization algorithm. Finally, based on the developed mode shift algorithm, the dual motor coupling full vehicle model is developed and the vehicle performance is analyzed using MATLAB/Simulink. For the UDDS (Urban Dynamometer Driving Schedule) driving cycle, it is seen from the simulation results of motor operating points that the proposed dual motor configuration is mostly operated under the high efficiency range, and the power consumption is significantly reduced by 7.6% compared with the single motor configuration. For the NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Cycle) and real road driving conditions, the proposed dual motor configuration also achieves system efficiency improvement of 5.0%~16.3%, which confirms the validity of the proposed configuration and its corresponding parameter matching and control algorithm development.

【 授权许可】

Unknown