Research on the influence factors of real driving cycle with statistical analysis and dynamic time warping

被引:0
|
作者
Yu, Shu [1 ]
Lue, Lin [1 ]
机构
[1] Wuhan Univ Technol, Sch Energy & Power Engn, Wuhan, Hubei, Peoples R China
关键词
statistical analysis; vehicle dynamics; influence factors; driving cycle; dynamic time; driving behaviour; RDC construction process; statistical characteristic; common factors number; cluster number; number selection principle; order principle; driving data; factors rules; optimum RDC; optimal cycle; real vehicles driving conditions; EMISSIONS;
D O I
10.1049/iet-its.2018.5275
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The real driving cycle (RDC), which aims to reflect the real driving behaviour of vehicles, plays an important role in evaluating the performance or pollution of vehicles. At present, the related researches most focus on developing RDCs of different functions or regions, while the influence factors and the rules of RDC construction are not involved. In this study, through statistical analysis and theoretical analysis of RDC construction process, the influence factors of candidate RDC are explored. These factors include statistical characteristics, common factors number, cluster number, number selection principle, and order principle. A different value of factors means a different candidate RDC and different proximity of RDCs to the real driving data. Through the dynamic time warping index, the proximity of candidate RDCs is calculated, then the factors rules and the optimum RDC are obtained. When the slope is added as one statistical characteristic, the common factors number is set as 5, the cluster number is set as 6, the number selection principle is set as ratio principle, and order principle chooses positive sequence, the candidate RDC is the optimal cycle which is closest to the real vehicles driving conditions.
引用
收藏
页码:286 / 292
页数:7
相关论文
共 50 条
  • [31] Analysis of dynamic changes in ECG signals during optical mapping by dynamic time warping
    Vyklicky, M
    Bardonova, J
    Provaznik, I
    Novakova, M
    Blaha, M
    Kozumplik, J
    COMPUTERS IN CARDIOLOGY 2005, VOL 32, 2005, 32 : 543 - 546
  • [32] Analysis of Computer Science Textbooks by Topic Modeling and Dynamic Time Warping
    Kawamata, Taisuke
    Matsuda, Yoshitatsu
    Sekiya, Takayuki
    Yamaguchi, Kazunori
    IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION, 2021, : 865 - 870
  • [33] Analysis and Prediction of Temporal Twitter Popularity Using Dynamic Time Warping
    Sermsai, Rattasit
    Laohakiat, Sirisup
    2019 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE 2019), 2019, : 176 - 180
  • [34] Comparative Analysis of the Vocal Repertoire of Eulemur: A Dynamic Time Warping Approach
    Marco Gamba
    Olivier Friard
    Isidoro Riondato
    Roberta Righini
    Camilla Colombo
    Longondraza Miaretsoa
    Valeria Torti
    Bakri Nadhurou
    Cristina Giacoma
    International Journal of Primatology, 2015, 36 : 894 - 910
  • [35] Breast cancer metastasis pattern analysis using dynamic time warping
    Kuno, Masahiro
    Yoshimura, Raiki
    Saeki, Sumito
    Ozaki, Yukinori
    Osako, Tomo
    Ohno, Shinji
    Iwami, Shingo
    Maruyama, Reo
    Takano, Toshimi
    Ueno, Takayuki
    CANCER SCIENCE, 2025, 116 : 201 - 201
  • [36] A Dynamic Time Warping Algorithm Based Analysis of Pedestrian Shockwaves at Bottleneck
    Sun, Lishan
    Gong, Qingsheng
    Yao, Liya
    Luo, Wei
    Zhang, Tianqi
    JOURNAL OF ADVANCED TRANSPORTATION, 2018,
  • [37] Comparative Analysis of the Vocal Repertoire of Eulemur: A Dynamic Time Warping Approach
    Gamba, Marco
    Friard, Olivier
    Riondato, Isidoro
    Righini, Roberta
    Colombo, Camilla
    Miaretsoa, Longondraza
    Torti, Valeria
    Nadhurou, Bakri
    Giacoma, Cristina
    INTERNATIONAL JOURNAL OF PRIMATOLOGY, 2015, 36 (05) : 894 - 910
  • [38] Similarity analysis of voice signals using wavelets with dynamic time warping
    Tashakkori, R
    Bowers, C
    INDEPENDENT COMPONENT ANALYSES, WAVELETS, AND NEURAL NETWORKS, 2003, 5102 : 168 - 177
  • [39] Automatic Rowing Kinematic Analysis Using OpenPose and Dynamic Time Warping
    Macedo, V
    Santos, J.
    Baptista, R. S.
    XXVII BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2020, 2022, : 605 - 611
  • [40] Nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces
    Schultz, David
    Jain, Brijnesh
    PATTERN RECOGNITION, 2018, 74 : 340 - 358