Research on Shift Schedule of DCT Vehicle on Flat-Straight Road Based on Driving Data Mining

被引：0
|
作者：
Qin D. [1 ]
Wang K. [1 ]
Feng J. [1 ]
Liu Y. [1 ]
机构：
[1] Chongqing University, State Key Laboratory of Mechanical Transmission, Chongqing
来源：
Qiche Gongcheng/Automotive Engineering | 2022年 / 44卷 / 11期
关键词：
data mining; flat-straight road; random forest algorithm; shift schedule;
D O I：
10.19562/j.chinasae.qcgc.2022.11.014
中图分类号：
学科分类号：
摘要：
In view of the poor adaptability of the shift schedule formulated based on vehicle dynamics，and the inability of gear decision model trained based on multidimensional data and intelligent algorithm in being directly applied to real vehicles，a method is proposed of extracting the shift schedule on flat-straight road by mining the driving data of skilled drivers. Firstly，a test is conducted to collect a large amount of driving data. Then three most important features of flat-straight road driving are extracted by using wavelet de-noising，spearman correlation analysis and information gain calculation. Finally，by comparing the classification accuracy of six machine learning algorithms for decision values（upshift，downshift and maintenance）in each gear，the random forest algorithm with the highest accuracy is selected to generate the three-parameter shift schedule，in which the parameters are vehicle speed，accelerator pedal position and engine angular acceleration. The simulation results show that this method can effectively collect the shift strategy of skilled drivers running on flat-straight road，and the shift schedule extracted achieves a fuel consumption level close to that with the economic shift strategy and a good power performance. © 2022 SAE-China. All rights reserved.
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页码：1663 / 1771+1796
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