|Table of Contents|

Identification strategy of heavy duty truck driving condition(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年3期
Page:
125-133
Research Field:
机械与汽车工程
Publishing date:

Info

Title:
Identification strategy of heavy duty truck driving condition
Author(s):
SHI Pei-long1;  CHEN Zi-tong2;  FU Kai1;  ZHAO Xuan1
(1. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China; 2. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)
Keywords:
automobile engineering;  heavy duty truck;  driving condition;  hidden Markov model;  T-S fuzzy neural network model
PACS:
U461
DOI:
10.19721/j.cnki.1671-8879.2023.03.013
Abstract:
For heavy trucks running on mountain roads, the key to realize active intervention and exit of multiple continuous braking systems was the accurate identification of vehicle driving conditions, therefore, a two-layer compound conservative driving condition identification strategy based on the hidden Markov model(HMM)and the Tkagi-Sugeno fuzzy neural network model(T-S FNN)was proposed. Firstly, the average brake pedal opening, brake pedal action ratio, braking times and average single braking duration were selected to characterize the brake pedal action characteristics in the time window, and the working condition models of HMM and T-S FNN models with different time window lengths were established. Secondly, through the road test method, rolling time window principle and K-means spatial clustering method were obtained to establish uphill driving conditions, small undulating road driving conditions and long downhill driving conditions. Then the HMM and T-S FNN models were trained offline. In order to verify the proposed strategy, online identification verification was performed. The results show that compared with the HMM-based identification strategy, the two-layer conservative identification strategy is more sensitive and accurate to small rolling road conditions and long downhill conditions.The simulation results of active exhaust brake control show that the exhaust brake on time accounts for 91.72% of the total time during the control with HMM-based identification strategy, while 19.58% less when controlled with a conservative identification strategy, and the opening times is 3, which is 1 less than the former, with better robustness performance.1 tab, 14 figs, 22 refs.

References:

[1] 许世维,马 建,汪贵平,等.基于制动意图识别的增程式重型商用车复合制动控制策略[J].中国公路学报,2017,30(4):140-151.
XU Shi-wei,MA Jian,WANG Gui-ping,et al.Composite braking control strategy based on braking intention recognition for range-extended heavy commercial vehicles[J].China Journal of Highway and Transport,2017,30(4):140-151.
[2]韩云武,罗禹贡,李克强,等.混合动力汽车下坡辅助控制方法[J].机械工程学报,2016,52(6):136-144.
HAN Yun-wu,LUO Yu-gong,LI Ke-qiang,et al.Downhill assist control method for hybrid electric vehicle[J].Journal of Mechanical Engineering,2016,52(6):136-144.
[3]韩云武,罗禹贡,李克强.基于驾驶员意图和行车安全的下坡辅助制动退出方法[J].汽车工程,2015,37(10):1144-1149,1143.
HAN Yun-wu,LUO Yu-gong,LI Ke-qiang.A retreat scheme for downhill assist braking based on driver's intent and driving safety[J].Automotive Engineering,2015,37(10):1144-1149,1143.
[4]ZHANG X,ZU B,ZHOU J,et al.Research on torque control method of hybrid electric vehicle based on driving intention recognition[C]//IEEE.Proceedings of 2021 5th CAA International Conference on Vehicular Control and Intelligence(CVCI).New York:IEEE,2021:1-6.
[5]王庆年,王 俊,陈慧勇,等.混合动力车辆中的加速与制动意图识别[J].吉林大学学报(工学版),2014,44(2):281-286.
WANG Qing-nian,WANG Jun,CHEN Hui-yong,et al.Accelerating and braking intention identification in hybrid vehicle[J].Journal of Jilin University(Engineering and Technology Edition),2014,44(2):281-286.
[6]王庆年,唐先智,王鹏宇,等.基于神经网络的混合动力汽车驾驶意图识别方法[J].农业机械学报,2012,43(8):32-36.
WANG Qing-nian,TANG Xian-zhi,WANG Peng-yu,et al.Driving intention identification method for hybrid vehicles based on neural network[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(8):32-36.
[7]张元才,余卓平,徐 乐,等.基于制动意图的电动汽车复合制动系统制动力分配策略研究[J].汽车工程,2009,31(3):244-249.
ZHANG Yuan-cai,YU Zhuo-ping,XU Le,et al.A study on the strategy of braking force distribution for the hybrid braking system in electric vehicles based on braking intention[J].Automotive Engineering,2009,31(3):244-249.
[8]潘 宁,于良耀,宋 健.考虑舒适性的电动汽车制动意图分类与识别方法[J].清华大学学报(自然科学版),2016,56(10):1097-1103.
PAN Ning,YU Liang-yao,SONG Jian.Braking intention classification and identification considering braking comfort for electric vehicles[J].Journal of Tsinghua University(Science and Technology),2016,56(10):1097-1103.
[9]BALAU A,KOOIJMAN D,RODARTE I V,et al.Stochastic real-world drive cycle generation based on a two stage Markov chain approach[J].SAE International Journal of Materials and Manufacturing,2015,8(2):390-397.
[10]SILVAS E,HEREIJGERS K,PENG H E,et al.Synthesis of realistic driving cycles with high accuracy and computational speed,including slope information[J].IEEE Transactions on Vehicular Technology,2016,65(6):4118-4128.
[11]石 琴,仇多洋,吴 冰,等.基于粒子群优化支持向量机算法的行驶工况识别及应用[J].中国机械工程,2018,29(15):1875-1883.
SHI Qin,QIU Duo-yang,WU Bing,et al.DCR and applications based on PSO-SVM algorithm[J].China Mechanical Engineering,2018,29(15):1875-1883.
[12]曹 骞,李 君,曲大为.大连市乘用车典型行驶工况的构建[J].上海交通大学学报,2018,52(11):1537-1542.
CAO Qian,LI Jun,QU Da-wei.Construction of typical driving cycle for passenger cars in the city of Dalian[J].Journal of Shanghai Jiao Tong University,2018,52(11):1537-1542.
[13]张 宏,姚延钢,杨晓勤.城市道路轻型汽车行驶工况构建[J].西南交通大学学报,2019,54(6):1139-1146,1154.
ZHANG Hong,YAO Yan-gang,YANG Xiao-qin.Light-duty vehicles driving cycle construction based on urban roads[J].Journal of Southwest Jiaotong University,2019,54(6):1139-1146,1154.
[14]胡正云.乘用车城市行驶工况构建方法研究[J].公路交通科技,2019,36(11):142-150.
HU Zheng-yun.Study on construction method of urban driving cycle of passenger vehicles[J].Journal of Highway and Transportation Research and Development,2019,36(11):142-150.
[15]PUCHALSKI A,KOMORSKA I.Stochastic simulation and validation of Markov models of real driving cycles[J].Diagnostyka,2019,3(3):31-36.
[16]TAZELAAR E,BRUINSMA J,VEENHUIZEN P,et al.Driving cycle characterization and generation,for design and control of fuel cell buses[J].World Electric Vehicle Journal,2009,3(4):812-819.
[17]高建平,高小杰.改进模糊C均值聚类法的车辆实际行驶工况构建[J].河南科技大学学报(自然科学版),2017,38(6):21-27,4.
GAO Jian-ping,GAO Xiao-jie.Vehicle real driving cycle construction based on improved fuzzy C-means clustering algorithm[J].Journal of Henan University of Science and Technology(Natural Science),2017,38(6):21-27,4.
[18]丁一峰,李 君,盖洪超,等.小波变换在行驶工况构建用车速数据处理中的应用[J].科学技术与工程,2017,17(28):274-279.
DING Yi-feng,LI Jun,GAI Hong-chao,et al.Application of vehicle speed data processing in driving cycle construction with wavelet transform[J].Science Technology and Engineering,2017,17(28):274-279.
[19]FÖRSTER D,INDERKA R B,GAUTERIN F.Data-driven identification of characteristic real-driving cycles based on K-means clustering and mixed-integer optimization[J].IEEE Transactions on Vehicular Technology,2020,69(3):2398-2410.
[20]周志华.机器学习[M].北京:清华大学出版社,2016.
ZHOU Zhi-hua.Machine learning[M].Beijing:Tsinghua University Press,2016.
[21]王小川,史 峰,郁 磊.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
WANG Xiao-chuan,SHI Feng,YU Lei.Analysis of 43 cases of MATLAB neural network[M].Beijing:Beijing University of Aeronautics & Astronautics Press,2013.
[22]阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J].上海交通大学学报,2004,38(4):524-526,532.
YAN Wei-wu,CHANG Jun-lin,SHAO Hui-he.Least square SVM regression method based on sliding time window and its simulation[J].Journal of Shanghai Jiao Tong University,2004,38(4):524-526,532.

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Last Update: 2023-06-30