Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (4): 754-763.doi: 10.13229/j.cnki.jdxbgxb20210172

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Influence of driving behavior on energy consumption of pure electric shared vehicles

Shao-bo JI1(),Yang LI1,Meng LI1,Shi-bin SU2,Xiao-long MA2,Shao-qing HE3,Guo-rui JIA3,Yong CHENG1   

  1. 1.School of Energy and Power Engineering，Shandong University，Jinan 250061，China
    2.Hisense Trans Tech Co. ，Ltd. ，Qingdao 256000，China
    3.China Automotive Technology and Research Center Co. ，Ltd. ，Tianjin 300300，China
  • Received:2021-02-06 Online:2022-04-01 Published:2022-04-20

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Abstract:

In this paper, the daily operation data of a pure electric shared vehicles was taken as the research object, and 25 driving behavior characteristic parameters of five categories were determined. Vehicles′ data were divided into starting, parking and running three phases. The correlation among 25 characteristic parameters was calculated by the maximum information coefficient (MIC) algorithm, which is suitable for correlation analysis of high?dimensional big data. The effect law of driving behavior was studied in energy consumption. The characteristic parameters that have significant influence on total energy consumption and 100 km energy consumption at different vehicle service stages were identified. The results show that there are differences in driving behavior characteristic parameters that have significant effects on energy consumption in different stages. Through analyzing the influence rules of different driving behavior characteristic parameters, optimization suggestions for energy saving and consumption reduction driving behaviors in different stages are put forward.

Key words: pure electric shared vehicle, driving behavior, characteristic parameters, maximum information coefficient

CLC Number: 

  • U471.15

Fig.1

Division of driving events"

Table1

Driving behavior characteristic parameters"

缩略名中文名称缩略名中文名称
Mean_V平均车速Mean_BP制动踏板平均开度
Std_V车速标准差dBP制动踏板开度变化率
Max_A最大加速度Std_dBP制动踏板开度变化率标准差
Mean_A平均加速度Max_C方向盘最大转角
Max_dRA最大加速度变化率Mean_C方向盘平均转角
Max_SA最大减速度dC方向盘变化率
Mean_SA平均减速度Total_Energy总能耗
Max_dSA最大减速度变化率Energy100百公里能耗
Max_GP最大加速踏板开度RA加速时间占比
Mean_GP加速踏板平均开度Rc匀速时间占比
dGP加速踏板开度变化率Rd减速时间占比
Std_dGP加速踏板开度变化率标准差Ri怠速时间占比
Max_BP最大制动踏板开度

Fig.2

Correlation between characteristic parameters of driving behavior and energy consumption in starting stage"

Fig.3

Impact of average speed on energy consumption"

Fig.4

Influence of average opening of accelerator pedal on energy consumption"

Fig.5

Effect of vehicle speed standard deviation on energy consumption"

Fig.6

Correlation between characteristic parameters of driving behavior and energy consumption in stopping stage"

Fig.7

Impact of average speed on energy consumption"

Fig.8

Influence of various motion states on energy consumption"

Fig.9

Correlation between characteristic parameters of driving behavior and energy consumption in running stage"

Fig.10

Impact of running time on energy consumption"

Fig.11

Impact of average speed on energy consumption"

Fig.12

Influence of average opening degree of accelerator pedal and standard deviation of vehicle speed on energy consumption"

1 Chen Si-cong. On the relationship between energy consumption and driving behavior of electric vehicles based on statistical features[C]∥Chinese Control Conference, Guangzhou, China,2019: 730-735.
2 Han Q, Hu X, He S, et al. Evaluate good bus driving behavior with LSTM[C]∥The 5th International Conference on Internet of Vehicles Technologies and Services Towards Smart City. Paris, France: Springer International Publishing, 2018:122-32.
3 Larusdottir E B, Ulfarsson G F. Effect of driving behavior and vehicle characteristics on energy consumption of road vehicles running on alternative energy sources[J]. International Journal of Sustainable Transportation, 2015, 9(8): 592-601.
4 魏涛. 车联网环境下汽车节能驾驶行为与速度优化方法研究[D]. 西安: 长安大学信息工程学院, 2019.
Wei Tao. Research on energy-saving driving behavior and speed optimization method of vehicles in internet of vehicles environment[D]. Xi'an: School of Information Engineering, Chang'an University, 2019.
5 王萍,万蔚,张克, 等.出租车驾驶员生态驾驶行为评价[J]. 交通工程,2018,18(6):41-44, 50.
Wang Ping, Wan Wei, Zhang Ke, et al. Evaluation of low energy consumption driving behavior of taxi drivers[J]. Journal of Transportation Engineering, 2018, 18(6): 41-44, 50.
6 赵晓华, 陈晨, 伍毅平, 等. 出租车驾驶员驾驶行为对油耗的影响及潜力分析[J].交通运输系统工程与信息,2015,15(4):85-91.
Zhao Xiao-hua, Chen Chen, Wu Yi-ping, et al. The effect and potential study of eco-driving on taxi fuel consumption [J]. Journal of Transportation Systems Engineering and Information Technology, 2015,15(4):85-91.
7 李萌. 基于车辆运行数据的驾驶能耗及驾驶风格分类分析[D]. 济南: 山东大学能源与动力工程学院, 2020.
Li Meng. Classification analysis of driving energy consumption and driving style based on vehicle running data[D]. Ji'nan: School of Energy and Power Engineering,Shandong University, 2020.
8 李熙,冯拔,谢勇波,等. 基于决策树的司机驾驶行为研究[J]. 控制与信息技术,2019, 41(5):17-20, 26.
Li Xi, Feng Ba, Xie Yong-bo, et al. Study on driver driving behavior based on decision tree[J]. Control and Information Technology, 2019, 41(5): 17-20, 26.
9 李佩,彭斯俊. 一种新的组合权重在组合预测模型中的应用[J].河南科技大学学报: 自然科学版,2018,39(2):87-93, 98.
Li Pei, Peng Si-jun. Application of a new combination weight in combination prediction model[J]. Journal of Henan University of Science and Technology(Natural Science), 2018, 39(2):87-93, 98.
10 张长聪,刘庆华,刘根,等. 基于改进灰色关联分析的雷达费用驱动因子选取方法[J]. 兵器装备工程学报,2019,40(8):130-135.
Zhang Chang-cong, Liu Qing-hua, Liu Gen, et al. Selection method of radar cost driver based on improved grey correlation analysis[J]. Journal of Ordnance Equipment Engineering, 2019, 40(8): 130-135.
11 刘震,党耀国,周伟杰, 等. 新型灰色接近关联模型及其拓展[J].控制与决策,2014,29(6):1071-1075.
Liu Zhen, Dang Yao-guo, Zhou Wei-jie, et al. New ggrey nearness incidence model and its extension[J]. Control and Decision, 2014, 29(6): 1071-1075.
12 Reshef D N, Reshef Y A, Finucane K H, et al. Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
13 王文广,王朔,赵文杰. 基于最大信息系数变量选择的电站锅炉NO x 排放量在线预估[J]. 华北电力大学学报: 自然科学版,2019,46(6):66-72.
Wang Wen-guang, Wang Shuo, Zhao Wen-jie. On-line estimation of NO x emissions from power plant boilers based on variable selection of maximum information coefficient[J]. Journal of North China Electric Power University(Natural Science Edition), 2019, 46(6): 66-72.
14 靳冰洁,张步涵,姚建国,等. 基于信息熵的大型电力系统元件脆弱性评估[J].电力系统自动化,2015,39(5):61-68.
Jin Bing-jie, Zhang Bu-han, Yao Jian-guo, et al. Vulnerability assessment of large power system components based on information entropy[J]. Automation of Electric Power Systems, 2015, 39(5): 61-68.
15 尹世庄,王韬,谢方方,等. 基于互信息和轮廓系数的聚类结果评估方法[J].兵器装备工程学报,2020,41(8):207-213.
Yin Shi-zhuang, Wang Tao, Xie Fang-fang, et al. Clustering result evaluation method based on mutual information and contour coefficient[J]. Journal of Ordnance Equipment Engineering, 2020, 41(8): 207-213.
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