Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification

被引：0

作者：

Cao, Zhipeng ^{[1
]}

Chen, Yong ^{[2
]}

He, Bolin ^{[1
]}

Xiao, Sen ^{[1
]}

Gao, Bingzhao ^{[3
]}

Yin, Xuebing ^{[1
]}

机构：

[1] Hebei University of Technology, Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, Tianjin,300130, China

[2] School of Mechanical Engineering, Guangxi University, Nanning,530004, China

[3] School of Automotive Studies, Tongji University, Shanghai,201804, China

来源：

Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 10期

关键词：

Dynamic programming;

D O I：

10.19562/j.chinasae.qcgc.2024.10.014

中图分类号：

学科分类号：

摘要：

In order to enhance the economic performance of pure electric vehicles (EVs) while maintaining better dynamic performance, a real-time shifting strategy based on driving cycle recognition is proposed for the self-developed two-speed dry dual clutch transmission (2DCT) for EVs. A radial basis neural network is adopted to predict the vehicle speed and the optimal shifting points are extracted by dynamic programming for seven types of driving cycle. Then, a driving cycle recognition model based on similarity comparison is constructed to recognize vehicle-driving conditions so as to achieve real-time shifting. The simulation based on MATLAB/Simulink and the 2DCT bench experiments are completed. The results demonstrate that the proposed real-time shifting strategy based on condition recognition can simultaneously meet the requirements of economic performance and shift frequency. © 2024 SAE-China. All rights reserved.

引用

页码：1873 / 1885

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