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有轨电车典型行驶工况的构建

陈维荣 刘禺贝 戴朝华 郭爱 安祺 时方力

陈维荣, 刘禺贝, 戴朝华, 郭爱, 安祺, 时方力. 有轨电车典型行驶工况的构建[J]. 西南交通大学学报, 2020, 55(6): 1141-1146, 1190. doi: 10.3969/j.issn.0258-2724.20190268
引用本文: 陈维荣, 刘禺贝, 戴朝华, 郭爱, 安祺, 时方力. 有轨电车典型行驶工况的构建[J]. 西南交通大学学报, 2020, 55(6): 1141-1146, 1190. doi: 10.3969/j.issn.0258-2724.20190268
CHEN Weirong, LIU Yubei, DAI Chaohua, GUO Ai, AN Qi, SHI Fangli. Construction of Typical Driving Cycle for Tram[J]. Journal of Southwest Jiaotong University, 2020, 55(6): 1141-1146, 1190. doi: 10.3969/j.issn.0258-2724.20190268
Citation: CHEN Weirong, LIU Yubei, DAI Chaohua, GUO Ai, AN Qi, SHI Fangli. Construction of Typical Driving Cycle for Tram[J]. Journal of Southwest Jiaotong University, 2020, 55(6): 1141-1146, 1190. doi: 10.3969/j.issn.0258-2724.20190268

有轨电车典型行驶工况的构建

doi: 10.3969/j.issn.0258-2724.20190268
基金项目: 国家重点研发计划(2017YFB1201005-11,2017YFB1201004,2017YFB1201003)
详细信息
    作者简介:

    陈维荣(1965—),男,教授,博士生导师,研究方向为电力系统及其自动化、新能源技术及其应用,E-mail:wrchen@home.swjtu.edu.cn

  • 中图分类号: V221.3

Construction of Typical Driving Cycle for Tram

  • 摘要: 为合理评估有轨电车在轨道交通中的行驶特征及运行指标,为有轨电车设计与控制提供依据,对有轨电车典型行驶工况进行构建. 首先,以巴黎、布达佩斯、墨尔本城市的有轨电车线路及行驶数据为基础,采用聚类方法获取降维行驶特征;然后,基于马尔可夫链理论,构建有轨电车典型行驶工况;最后,将构建工况与实际工况进行特征值对比分析,并基于所构建的典型工况进行仿真验证. 结果表明:构建的典型行驶工况与实际工况样本数据库总体特征的平均偏差仅为2.63%,满足偏差低于5%的开发精度要求;此外,典型行驶工况与实际行驶工况下的需求功率误差也仅为1.78%,验证了典型工况模型的准确性和有效性.

     

  • 图 1  短行程

    Figure 1.  Sketch of short stroke

    图 2  Calinski-Harabasz评估

    Figure 2.  Plot of Calinski-Harabasz evaluation

    图 3  聚类散点

    Figure 3.  Scatter plot of cluster analysis

    图 4  状态转移概率矩阵

    Figure 4.  Histogram of state transition probability matrix

    图 5  构建的典型工况

    Figure 5.  Typical driving cycle

    图 6  不同工况下能耗对比

    Figure 6.  Comparison of energy consumption with different driving cycles

    表  1  短行程特征参数

    Table  1.   Characteristic parameters of short stroke

    符号含义符号含义
    Vavg/(km•h−1平均速度V0-30低速因子
    Vstd/(km•h−1速度标准差V30-70中速因子
    Vmax/(km•h−1最大速度a0-0.5低加速因子
    aavg/(m•s−2平均加速度a0.5-1中加速因子
    astd/(m•s−2加速度标准差r0-0.5低减速因子
    amax/(m•s−2最大加速度r0.5-1中减速因子
    rstd/(m•s−2减速度标准差a= 0匀速因子
    ravg/(m•s−2平均减速度tD/s怠速时间
    下载: 导出CSV

    表  2  短行程样本数据库

    Table  2.   Database of short stroke samples

    序号Vavg/(km•h−1Vstd/(km•h−1···tD/sa= 0
    134.9822.40···2920.30
    228.8120.89···313.84
    $ \vdots $$ \vdots $$ \vdots $$ \vdots $$ \vdots $
    40048.8125.96···3044.77
    下载: 导出CSV

    表  3  主成分特征值和贡献率

    Table  3.   Eigen values and contribution rates of principal components

    主成分序号主成分特征值主成分贡献率/%主成分序号主成分特征值主成分贡献率/%
    1 7.355 4 45.97 9 0.105 9 0.67
    2 3.890 3 24.31 10 0.061 8 0.39
    3 2.190 2 13.69 11 0.052 5 0.33
    4 0.759 7 4.75 12 0.037 8 0.24
    5 0.481 3 3.01 13 0.030 0 0.19
    6 0.390 5 2.44 14 0.026 8 0.17
    7 0.322 4 2.02 15 0.015 5 0.10
    8 0.274 4 1.72 16 0.005 5 0.03
    下载: 导出CSV

    表  4  短行程主成分得分矩阵

    Table  4.   Score matrix of short-stroke principal components

    样本序号T1T2T3
    13.8361.990−0.206
    24.4672.133−0.912
    $ \vdots $$\vdots$$ \vdots $$ \vdots $
    400−1.8531.4102.026
    下载: 导出CSV

    表  5  聚类结果特征值对比

    Table  5.   Characteristic value comparison of clustering results

    工况 样本占
    比/%
    tD/s Vavg/
    (km•h−1
    Vmax/
    (km•h−1
    aavg/
    (m•s−2
    V0-30 a=0
    类1 10.3 23 51.9 70 0.91 16.1 47.1
    类2 17.3 27 38.3 60 0.94 27.6 43.5
    类3 27.6 30 23.4 38 0.91 64.0 53.2
    类4 44.8 30 32.5 50 0.70 32.0 24.1
    下载: 导出CSV

    表  6  构建工况与数据库样本特征参数对比

    Table  6.   Comparison of characteristic parameters between constructed cycle and sample population

    特征参数样本总体构建工况绝对偏差/%
    Vavg/(km•h−132.9931.654.06
    Vstd/(km•h−118.5218.811.56
    Vmax/(km•h−170700
    aavg/(m•s−20.820.802.44
    astd/(m•s−20.190.184.58
    amax/(m•s−21.301.253.85
    rstd/(m•s−21.261.313.78
    ravg/(m•s−2−1.34−1.404.24
    V0-300.380.373.04
    V30-700.620.631.90
    a0-0.50.080.082.33
    a0.5-10.150.161.93
    r0-0.50.020.022.03
    r0.5-10.010.011.78
    a= 037.8636.822.75
    tD/s28.7629.291.84
    下载: 导出CSV
  • OLATOMIWA L, MEKHILEF S, ISMAIL M S, et al. Energy management strategies in hybrid renewable energy systems:a review[J]. Renewable and Sustainable Energy Reviews, 2016, 62: 821-835. doi: 10.1016/j.rser.2016.05.040
    BAÑOS R, MANZANO-AGUGLIARO F, MONTOYA F G, et al. Optimization methods applied to renewable and sustainable energy:a review[J]. Renewable & Sustainable Energy Reviews, 2011, 15(4): 1753-1766.
    付稳超, 齐洪峰, 戴朝华, 等. 计及整车服役周期成本的有轨电车燃料电池混合动力系统配置优化方法[J/OL]. 西南交通大学学报: 1-9[2019-03-11]. http://kns.cnki.net/kcms/detail/51.1277.u.20181226.1123.010.html.

    FU Wenchao, QI Hongfeng, DAI Chaohua, et al. Optimal configuration method of tram fuel cell hybrid power system considering vehicle service cycle cost[J/OL]. Journal of Southwest Jiaotong University, 1-9[2019-03-11]. http://kns.cnki.net/kcms/detail/51.1277.u.20181226.1123.010.html.
    YAN Y, LI Q, CHEN W R, et al. Optimal energy management & control in multi-mode equivalent energy consumption of fuel cell/supercapacitor of hybrid electric tram[J]. IEEE Transactions on Industrial Electronics, 2018, 66(8): 6065-6076.
    TALLA J, STREIT L, PEROUTKA Z, et al. Position-based T-S fuzzy power management for tram with energy storage system[J]. IEEE Transactions on Industrial Electronics, 2015, 62(5): 3061-3071. doi: 10.1109/TIE.2015.2396871
    BISHOP J D K, AXON C J, MCCULLOCH M D. A robust,data-driven methodology for real-world driving cycle development[J]. Transportation Research,Part D: Transport and Environment, 2012, 17(5): 389-397.
    SHI Q, ZHENG Y B, WANG R S, et al. The study of a new method of driving cycles construction[J]. Procedia Engineering, 2011, 16(1): 79-87.
    姜平,石琴,陈无畏. 聚类和马尔科夫方法结合的城市汽车行驶工况构建[J]. 中国机械工程,2010,21(23): 2893-2897.

    JIANG Ping, SHI Qin, CHEN Wuwei. Construction of city vehicle driving conditions combined with clustering and Markov methods[J]. China Mechanical Engineering, 2010, 21(23): 2893-2897.
    BRADY J, O’MAHONY M. Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas[J]. Applied Energy, 2016, 177: 165-178. doi: 10.1016/j.apenergy.2016.05.094
    曹骞,李君,刘宇,等. 基于大数据和马尔科夫链的行驶工况构建[J]. 东北大学学报(自然科学版),2019,40(1): 77-81.

    CAO Qian, LI Jun, LIU Yu, et al. Construction of driving cycle based on big data and Markov chain[J]. Journal of Northeastern University (Natural Science), 2019, 40(1): 77-81.
    张曼,施树明. 面向汽车运行工况设计的马氏链非等长交叉进化算法[J]. 浙江大学学报(工学版),2018,52(9): 33-41.

    ZHANG Man, SHI Shuming. Non-isometric crossover evolution algorithm of Markov chain for designing vehicle driving cycles[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(9): 33-41.
    MONTAZERIGH M, FOTOUHI A. Traffic condition recognition using the k-means clustering method[J]. Scientia Iranica, 2011, 18(4): 930-937. doi: 10.1016/j.scient.2011.07.004
    NESAMANI K S, SUBRAMANIAN K P. Development of a driving cycle for intra-city buses in Chennai,India[J]. Atmospheric Environment, 2011, 45(31): 5469-5476. doi: 10.1016/j.atmosenv.2011.06.067
    秦大同,詹森,漆正刚,等. 基于K-均值聚类算法的行驶工况构建方法[J]. 吉林大学学报(工学版),2016,46(2): 383-389.

    QIN Datong, ZHAN Sen, QI Zhenggang, et al. Driving cycle construction using K-means clustering method[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(2): 383-389.
    胡志远,秦艳,谭丕强,等. 基于大样本的上海市乘用车行驶工况构建[J]. 同济大学学报(自然科学版),2015,43(10): 1523-1527.

    HU Zhiyuan, QIN Yan, TAN Piqiang, et al. Large sample based car driving cycle in Shanghai City[J]. Journal of Tongji University (Natural Science), 2015, 43(10): 1523-1527.
    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(6): 1139-1146,1154.

    ZHANG Hong, YAO Yangang, YANG Xiaoqin. Study on light weight vehicles driving cycle construction based on urban roads[J]. Journal of Southwest Jiaotong University, 2019(6): 1139-1146,1154.
    高建平,孙中博,丁伟,等. 车辆行驶工况的开发和精度研究[J]. 浙江大学学报(工学版),2017,51(10): 2046-2054.

    GAO Jianping, SUN Zhongbo, DING Wei, et al. Development of vehicle driving cycle and accuracy of research[J]. Journal of Zhejiang University (Engineering Science), 2017, 51(10): 2046-2054.
    余曼,赵轩,魏朗,等. 基于FCM聚类算法的电动车城市循环工况构建[J]. 公路交通科技,2018,35(10): 144-153,162.

    YU Man, ZHAO Xuan, WEI Lang, et al. Construction of electric vehicle urban driving cycle based on FCM clustering algorithm[J]. Journal of Highway and Transportation Research and Development, 2018, 35(10): 144-153,162.
    李永亮,张伟,戎亚萍. 现代有轨电车发展对我国的启示[J]. 交通运输系统工程与信息,2013(5): 206-210.

    LI Yongliang, ZHANG Wei, RONG Yaping. The enlightenment of modern tram’s development to China[J]. Journal of Transportation Systems Engineering and Information Technology, 2013(5): 206-210.
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出版历程
  • 收稿日期:  2019-03-29
  • 修回日期:  2020-03-27
  • 网络出版日期:  2020-04-10
  • 刊出日期:  2020-12-15

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