Volume 40 Issue 5
Nov.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(5): 129-138
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
shu

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

doi: 10.3963/j.jssn.1674-4861.2022.05.014
  • 1.

    School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, Guangdong, China

  • 2.

    SYSU-GAC R & D Center Joint Laboratory of Intelligent Transportation and Artificial Intelligence, Guangzhou 510006, China

  • 3.

    Guangdong Provincial Marine Engineering Construction and Water Emergency Rescue Engineering Technology Center, Guangzhou 510006, China

  • 4.

    Guangdong Marshell Electric Technology Co., Ltd, Zhaoqing 523268, Guangdong, China

  • Received Date: 2022-04-14
    Available Online: 2022-12-05
    Abstract
  • In order to study the relationship between driving behaviors and energy consumption of electric buses, a prediction model of energy consumption based on the random forest algorithm is developed for electric buses. In order to address the negative impacts from the randomness of the sample data and the parameters characterizing driving behaviors, the grey relational grades of the parameters for representing driving behaviors and the projection values of the sample data are calculated by a grey relational projection method. The parameters representing driving behaviors that have a high correlation with energy consumption are selected as the input variables, and the sample data with a high similarity are used as the training and testing dataset. The variables representing driving styles, which are significantly correlated with energy consumption, are introduced into the model to further improve the accuracy. Driving styles are classified and labelled by a K-means clustering method. In addition, a grey relation projection-random forest(GRP-RF)model for predicting energy consumption of electric buses is developed by taking the driving styles and the selected parameters for representing driving behavior as input variables, and the energy consumption per kilometer as the output variable. The model is tested based on the operation data of electric buses from a bus line in the City of Guangzhou, and the impacts of the parameters representing driving behaviors on the energy consumption is analyzed under the following three typical scenarios: acceleration, braking, and operation stage. The results show that the root mean square error(RMSE)and mean absolute percentage error(MAPE)of the prediction model are 0.001 8 kW·h/km and 3.42%, respectively. Compared with the GRP-RF model and the random forest model without considering the driving styles, the RMSE is decreased by 35.71% and 48.57% and the MAPE is decreased by 38.82% and 46.81%, respectively. Moreover, study results show that the average energy consumption at the acceleration, braking, and operation stage is 1.066, 0.903 7, 0.955 2 kW·h/km, respectively. To keep the energy consumption lower than the average value at each stage, the accelerator pedal opening should be within 55% of its full capacity at the acceleration stage; the brake pedal opening should be controlled within 25%of its full capacity at the braking stage, and the speed should be limited within 40 km/h at the operation stage.

     

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    • References(21)
  • [1]
    WALNUM H J, SIMONSEN M. Does driving behavior matter?An analysis of fuel consumption data from heavy-duty trucks[J]. Transportation Research Part D: Transport and Environment, 2015, 36(4): 107-120.
    [2]
    杨澜, 赵祥模, 吴国垣, 等. 智能网联汽车协同生态驾驶策略综述[J]. 交通运输工程学报, 2020, 20(5): 58-72. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202005008.htm

    YANG L, ZHAO X M, WU G Y, et al. Review on connected and automated vehicles based cooperative eco-driving strategies[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 58-72. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202005008.htm
    [3]
    付锐, 张雅丽, 袁伟. 生态驾驶研究现状及展望[J]. 中国公路学报, 2019, 32(3): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201903002.htm

    FU R, ZHANG Y L, YUAN W. Progress and prospect in research on eco-driving[J]. China Journal of Highway and Transport, 2019, 32(3): 1-12. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201903002.htm
    [4]
    严英, 庄继晖, 谢辉. 纯电动公交客车司机驾驶行为差异对能耗的影响[J]. 天津大学学报(自然科学与工程技术版), 2014, 47(3): 231-236. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201403008.htm

    YAN Y, ZHUANG J H, XIE H. Influence of the driver behavior difference on the energy consumption of the pure electric buses[J]. Journal of Tianjin University(Science and Technology), 2014, 47(3): 231-236. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201403008.htm
    [5]
    惠飞, 吴丽宁, 景首才, 等. 基于轨迹数据的多工况典型驾驶行为能耗评估[J]. 计算机应用与软件, 2020, 37(5): 50-56. doi: 10.3969/j.issn.1000-386x.2020.05.009

    HUI F, WU L N, JING S C, et al. Energy consumption assessment of typical driving behavior under multiple driving conditions based on trajectory data[J]. Computer Applications and Software, 2020, 37(5): 50-56. (in Chinese) doi: 10.3969/j.issn.1000-386x.2020.05.009
    [6]
    TIE S F, TAN C W. Prediction error applied to hybrid electric vehicle optimal fuel economy[J]. IEEE Transactions on Control Systems Technology, 2017, 26(6): 2121-2134.
    [7]
    LI S E, PENG H. Strategies to minimize fuel consumption of passenger cars during car-following scenarios[J]. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 2012, 226(3): 419-429.
    [8]
    董昊旻, 张维轩, 王文彬, 等. 基于Tri-Training的驾驶风格分类算法[J]. 汽车技术, 2021, 547(4): 6-11.

    DONG H M, ZHANG W X, WANG W B, et al. Driving style classification algorithm based on Tri-Training[J]. Automobile Technology, 2021, 547(4): 6-11. (in Chinese)
    [9]
    赵玮, 徐良杰, 冉斌. 基于Adaptive Lasso及RF算法的冰雪天气交通事故分析[J]. 中国安全科学学报, 2017, 27(2): 98-103. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201702018.htm

    ZHAO W, XU L J, RAN B. Analyzing traffic crash under iced and snow weather condition based on Adaptive Lasso and RF[J]. China Safety Science Journal, 2017, 27(2): 98-103. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201702018.htm
    [10]
    王日升, 谢红薇, 安建成. 基于分类精度和相关性的随机森林算法改进[J]. 科学技术与工程, 2017, 17(20): 67-72. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201720012.htm

    WANG R S, XIE H W, AN J C. Improvement of random forests algorithm based on classification accuracy and correlation[J]. Science Technology and Engineering, 2017, 17(20): 67-72. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201720012.htm
    [11]
    朱林艳. 基于随机森林的行人交通事故研究[D]. 武汉: 华中科技大学, 2018.

    ZHU L Y. Study on pedestrian traffic accidents based on random forest[D]. Wuhan: Huazhong University of Science and Technology, 2018. (in Chinese)
    [12]
    XING Y, LYU C, CAO D, et al. Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling[J]. Applied Energy, 2020(261): 114471.
    [13]
    GUO J, JIANG Y, YU Y, et al. A novel energy consumption prediction model with combination of road information and driving style of BEVs[J]. Sustainable Energy Technologies and Assessments, 2020(42): 100826.
    [14]
    赵佳伟, 胡明辉, 荣正璧, 等. 驾驶风格对纯电动汽车能耗的影响[J]. 重庆大学学报, 2021, 44(12): 103-115. https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202112009.htm

    ZHAO J W, HU M H, RONG Z B, et al. Effect of driving style on the energy consumption of an electric vehicle[J]. Journal of Chongqing University, 2021, 44(12): 103-115. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202112009.htm
    [15]
    刘迪, 郑建明, 覃斌, 等. 基于自然驾驶场景大数据的驾驶风格研究[J]. 汽车技术, 2022, 584(9): 36-42. https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS202209005.htm

    LIU D, ZHENG J M, QIN B, et al. Research on driving style based on big data of natural driving scene[J]. Automobile Technology, 2022, 584(9): 36-42. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS202209005.htm
    [16]
    张雅丽, 付锐, 袁伟, 等. 考虑能耗的进出站驾驶风格分类及识别模型[J/OL]. 吉林大学学报(工学版): (2022-03-22)[2022-04-14]. DOI: 10.13229/j.cnki.jdxbgxb-20211000.

    ZHANG Y L, FU R, YUAN W, et al. Classification and recognition model of entering and leaving stops'driving style considering energy consumption[J]. Journal of Jilin University(Engineering and Techno-logy Edition): (2022-03-22)[2022-04-14]. DOI: 10.13229/j.cnki.jdxbgxb-20211000.(in Chinese).
    [17]
    VAITKUS V, LENGVENIS P, ZYLIUS G. Driving style classification using long-term accelerometer information[C]. 19th International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland: IEEE, 2014.
    [18]
    熊坚, 施锦浩, 万华森. 人车路综合风险场模型构建及驾驶风格评估[J]. 交通运输系统工程与信息, 2021, 21(6): 105-114. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202106012.htm

    XIONG J, SHI J H, WAN H S. Modeling of driver-vehicle-road integrated risk field and driving style assessment[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 105-114. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202106012.htm
    [19]
    李经纬, 赵治国, 沈沛鸿, 等. 驾驶风格K-means聚类与识别方法研究[J]. 汽车技术, 2018, 519(12): 8-12. https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS201812002.htm

    LI J W, ZHAO Z G, SHEN P H, et al. Research on methods of k-means clustering and recognition for driving style[J]. Automobile Technology, 2018, 519(12): 8-12. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS201812002.htm
    [20]
    李升朝, 吴越, 白雪萌, 等. 基于TOPSIS与灰色关联的危货车辆违规报警研究[J]. 重庆交通大学学报(自然科学版), 2021, 40(7): 61-66. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202107009.htm

    LI S C, WU Y, BAI X M, et al. Violation alarm of dangerous goods vehicle based on TOPSIS and gray correlation[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(7): 61-66. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202107009.htm
    [21]
    王旭, 马菲, 廖小棱, 等. 基于多分类监督学习的驾驶风格特征指标筛选[J]. 交通信息与安全, 2022, 40(1): 162-168. doi: 10.3963/j.jssn.1674-4861.2022.01.019

    WANG X, MA F, LIAO X L, et al. Feature selection for recognition of driving styles based on multi-classification and supervised learning[J]. Journal of Transport Information and Safety, 2022, 40(1): 162-168. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.01.019
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