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Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification
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Zhipeng Cao1, Yong Chen2, Bolin He1, Sen Xiao1, Bingzhao Gao3, Xuebing Yin1
Automotive Engineering | 2024, 46(10) : 1873 - 1885
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Automotive Engineering | 2024, 46(10): 1873-1885
• Selected Papers •
Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification
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Zhipeng Cao1, Yong Chen2, Bolin He1, Sen Xiao1, Bingzhao Gao3, Xuebing Yin1
Affiliations
  • 1. Hebei University of Technology，Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles，Tianjin 300130
  • 2. School of Mechanical Engineering，Guangxi University，Nanning 530004
  • 3. School of Automotive Studies，Tongji University，Shanghai 201804
Published: 2024-10-25 doi: 10.19562/j.chinasae.qcgc.2024.10.014
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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.

pure electric vehicle  /  two-speed dry dual clutch transmission  /  shifting strategy  /  driving cycle recognition
Zhipeng Cao, Yong Chen, Bolin He, Sen Xiao, Bingzhao Gao, Xuebing Yin. Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification[J]. Automotive Engineering, 2024 , 46 (10) : 1873 -1885 . DOI: 10.19562/j.chinasae.qcgc.2024.10.014
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.10.014
  • Receive Date：2024-03-30
  • Online Date：2025-07-21
  • Published：2024-10-25
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History
  • Received：2024-03-30
  • Revised：2024-05-15
Funding
Affiliations
    1. Hebei University of Technology，Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles，Tianjin 300130
    2. School of Mechanical Engineering，Guangxi University，Nanning 530004
    3. School of Automotive Studies，Tongji University，Shanghai 201804
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.10.014
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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