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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