GPRS based driving cycle self-learning for electric vehicle

被引:0
|
作者
Zhuang, Ji-Hui [1 ]
Xie, Hui [1 ]
Yan, Ying [1 ]
机构
[1] State Key Laboratory of Engine, Tianjin University, Tianjin 300072, China
来源
Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology | 2010年 / 43卷 / 04期
关键词
Conformal mapping - Cluster analysis - Classification (of information) - Optimal control systems - Electric vehicles - Roads and streets - Self organizing maps;
D O I
暂无
中图分类号
学科分类号
摘要
A methodology to collect the driving cycle data remotely based on GPRS was presented and applied to a running electric vehicle to build a driving cycle database for road test. The self-organizing map(SOM) network was introduced into self-learning of driving cycle, so the cluster analysis was performed to classify kinematic sequence of original data. Based on the classification of kinematic sequence, three types of typical driving cycles of electric vehicle road test were constructed and provided foundation for self-adapt optimal control strategy for electric vehicle. Compared with other driving cycles, the constructed driving cycles have common regularity, which shows that self-learning of driving cycle is perfectly realized by the application of SOM network.
引用
收藏
页码:283 / 286
相关论文
共 50 条
  • [21] A Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving
    Xing, Jiaming
    Wei, Dengwei
    Zhou, Shanghang
    Wang, Tingting
    Huang, Yanjun
    Chen, Hong
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024,
  • [22] FUZZY ENERGY MANAGEMENT STRATEGY FOR A HYBRID ELECTRIC VEHICLE BASED ON DRIVING CYCLE RECOGNITION
    Wu, J.
    Zhang, C. -H.
    Cui, N. -X.
    INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2012, 13 (07) : 1159 - 1167
  • [23] Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition
    J. Wu
    C. -H. Zhang
    N. -X. Cui
    International Journal of Automotive Technology, 2012, 13 : 1159 - 1167
  • [24] Markov chain-based approach of the driving cycle development for electric vehicle application
    Yang, Ying
    Zhang, Qing
    Wang, Zhen
    Chen, Zeyu
    Cai, Xue
    CLEANER ENERGY FOR CLEANER CITIES, 2018, 152 : 502 - 507
  • [25] The Driving Cycle Characteristic Parameters Research for Hybrid Electric Vehicle
    Zhou, Nan
    Wang, Qingnian
    Wang, Pengyu
    MATERIAL SCIENCE AND ENGINEERING TECHNOLOGY, 2012, 462 : 271 - 276
  • [26] DRIVING CYCLE TESTING OF ELECTRIC VEHICLE-BATTERIES AND SYSTEMS
    BRANDT, DD
    JOURNAL OF POWER SOURCES, 1992, 40 (1-2) : 73 - 79
  • [27] Design of the electric motor with permanent magnets for electric vehicle according the driving cycle
    Grebenikov V.V.
    Priymak M.V.
    Grebenikov, V.V. (elm1153@gmail.com), 2018, Institute of Electrodynamics, National Academy of Sciences of Ukraine (2018): : 65 - 68
  • [28] An electric vehicle model and a driving cycle for mail delivery use
    Al Jed, Habib
    Mieze, Andre
    Simon, Remi
    Vinassa, Jean-Michel
    PROCEEDINGS OF THE 2011-14TH EUROPEAN CONFERENCE ON POWER ELECTRONICS AND APPLICATIONS (EPE 2011), 2011,
  • [29] Deep reinforcement learning enabled self-learning control for energy efficient driving
    Qi, Xuewei
    Luo, Yadan
    Wu, Guoyuan
    Boriboonsomsin, Kanok
    Barth, Matthew
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 99 : 67 - 81
  • [30] Self-learning system based on metadata management module(MMM) for providing self-learning service
    Shin, Sung-Oog
    Lee, Jung-Oog
    Baik, Doo-Kwon
    PROCEEDINGS OF THE 2007 1ST INTERNATIONAL SYMPOSIUM ON INFORMATION TECHNOLOGIES AND APPLICATIONS IN EDUCATION (ISITAE 2007), 2007, : 100 - +