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考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林预测模型

刘强 严修 鲁誉 解孝民

刘强, 严修, 鲁誉, 解孝民. 考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林预测模型[J]. 交通信息与安全, 2022, 40(5): 129-138. doi: 10.3963/j.jssn.1674-4861.2022.05.014
引用本文: 刘强, 严修, 鲁誉, 解孝民. 考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林预测模型[J]. 交通信息与安全, 2022, 40(5): 129-138. doi: 10.3963/j.jssn.1674-4861.2022.05.014
LIU Qiang, YAN Xiu, LU Yu, XIE Xiaomin. A Grey Relation Projection-Random Forest Prediction Model of Energy Consumption for Electric Buses Considering Driving Style[J]. Journal of Transport Information and Safety, 2022, 40(5): 129-138. doi: 10.3963/j.jssn.1674-4861.2022.05.014
Citation: LIU Qiang, YAN Xiu, LU Yu, XIE Xiaomin. A Grey Relation Projection-Random Forest Prediction Model of Energy Consumption for Electric Buses Considering Driving Style[J]. Journal of Transport Information and Safety, 2022, 40(5): 129-138. doi: 10.3963/j.jssn.1674-4861.2022.05.014

考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林预测模型

doi: 10.3963/j.jssn.1674-4861.2022.05.014
基金项目: 

国家自然科学基金项目 51675540

广东省基础与应用基础研究基金项目 2022A1515010692

详细信息
    通讯作者:

    刘强(1981—),博士,教授.研究方向:新能源汽车及安全.E-mail:liuq32@mail.sysu.edu.cn

  • 中图分类号: U491.1+7

A Grey Relation Projection-Random Forest Prediction Model of Energy Consumption for Electric Buses Considering Driving Style

  • 摘要: 为探索驾驶员驾驶行为与电动公交车能耗之间的关系,采用随机森林算法建立电动公交车能耗预测模型。为克服驾驶行为特征参数和样本数据的随机性对电动公交车能耗预测模型的负面影响,运用灰色关联投影法计算各驾驶行为特征参数的灰色关联度以及各样本数据的投影值,筛选出与能耗具有高关联性的驾驶行为特征参数作为模型的输入变量,以及相似度较高的样本数据作为训练集和测试集。同时,引入了与能耗具有显著相关性的驾驶风格变量以进一步提升模型的预测能力,运用K-means聚类方法将驾驶风格分类并得到驾驶风格标签。将驾驶风格标签和筛选后驾驶行为特征参数作为输入变量,单位里程能耗作为输出变量,基于筛选后的数据集建立了考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林(GRP-RF)预测模型。基于广州市某线路电动公交车运营数据对模型进行检验,并运用该模型分析加速、制动和运行3种典型场景下相应驾驶行为特征参数对电动公交车能耗的影响。结果表明:该模型预测能耗的均方根误差(RMSE)和平均绝对百分比误差(MAPE)分别为0.001 8 kW·h/km和3.42%。相比于不考虑驾驶风格的GRP-RF模型和随机森林模型,该模型的RMSE分别降低了35.71%和48.57%,MAPE分别降低了38.82%和46.81%。研究结果表明:加速、制动和运行阶段的平均能耗分别为1.066,0.903 7,0.955 2 kW·h/km;为使各阶段能耗在相应均值以下,加速阶段应控制加速踏板开度在55%以内;制动阶段应控制制动踏板开度在25%以内;运行阶段应控制车速在40 km/h以内。

     

  • 图  1  不同驾驶风格各行为特征参数

    Figure  1.  The characteristic parameters of each behavior of different driving styles

    图  2  不同驾驶风格的能耗

    Figure  2.  Energy consumption for different driving styles

    图  3  各特征参数与能耗的灰色关联度

    Figure  3.  The grey relation degree of each characteristic parameter and energy consumption

    图  4  各样本灰色关联投影值

    Figure  4.  The grey relation projectionvalue of eachsample

    图  5  本模型结构图

    Figure  5.  The structure diagram of the model

    图  6  随机森林中决策树棵数对性能的影响

    Figure  6.  The influence of the number of decision trees on the performance in RF

    图  7  交叉验证结果

    Figure  7.  The result of cross-validation

    图  8  随机森林模型(其中1棵树)

    Figure  8.  Model of RF(one tree)

    图  9  加速踏板开度与能耗关系

    Figure  9.  Relationship between accelerator pedal opening and energy consumption

    图  10  制动踏板开度与能耗关系

    Figure  10.  Relationship between brake pedal opening and energy consumptions

    图  11  车速与能耗关系

    Figure  11.  Relationship between vehicle speed and energy consumption

    表  1  驾驶员驾驶行为特征和能耗统计描述

    Table  1.   Statistical description of driver driving behavior characteristics and energy consumption

    特征参数 最小值 最大值 平均值 标准差 离散系数
    加速踏板平均开度/% 7.185 25.76 17.03 2.780 0.163 182
    加速踏板开度标准差/% 19.20 40.49 28.54 3.562 0.124 813
    制动踏板平均开度/% 1.046 7.600 3.440 1.354 0.393 651
    制动踏板开度标准差/% 4.950 11.06 7.519 1.357 0.180 453
    平均车速/(km/h) 1.115 13.66 11.27 1.310 0.116 266
    车速标准差/(km/h) 2.422 13.37 11.97 0.909 4 0.075 992
    加速踏板比例/% 0.153 8 0.463 7 0.336 1 0.041 77 0.124 295
    制动踏板比例/% 0.049 77 0.416 0 0.233 2 0.077 57 0.332 617
    制动踏板开度>30%比例/% 0 0.040 62 0.011 79 0.007 332 0.622 107
    车速>40 km/h比例/% 0 0.022 31 0.004 857 0.004 593 0.945 595
    单位里程能耗/(kW∙h/km) 0.686 7 1.123 0.831 3 0.076 00 0.091 426
    下载: 导出CSV

    表  2  部分驾驶员各评测指标

    Table  2.   The evaluation indicators of some driver

    驾驶员编号 车速平均值/(km/h) 车速标准差/(km/h) 车速最大值/(km/h) 加速度平均值/(m/s2 加速度标准差/(m/s2
    1 13 12.6 45 0.767 2 0.992 0
    2 10.8 11.15 40 0.750 6 0.933 0
    3 10.77 10.86 40 0.841 4 1.103
    4 12.27 12.52 44 0.653 5 0.839 7
    5 8.05 11.1 43 0.618 6 0.766 8
    下载: 导出CSV

    表  3  不同K值下的平均轮廓系数

    Table  3.   The value of S under different values of K

    参数 平均轮廓系数S
    K = 3 0.696 7
    K = 4 0.743 2
    K = 5 0.704 9
    下载: 导出CSV

    表  4  各模型预测误差

    Table  4.   Prediction error of each model

    误差指标 RMSE/ (kW∙h/km) MAPE/% 运算时间/s
    本模型 0.001 8 3.42 1.756 3
    GRP-RF 0.002 8 5.59 1.738 0
    RF 0.003 5 6.43 1.731 4
    下载: 导出CSV
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  • 收稿日期:  2022-04-14
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