Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (2): 27-36.doi: 10.16088/j.issn.1001-6600.2021042106

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Remaining Driving Range Prediction Based on Symbol Conversion and XGBoost Algorithm

TIAN Sheng*, GAN Zhiheng, LÜ Qing   

  1. School of Civil and Transportation, South China University of Technology, Guangzhou Guangdong 510641, China
  • Received:2021-04-21 Revised:2021-05-18 Published:2022-05-31

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Abstract: Improving the prediction accuracy of remaining driving range can alleviate the “driving anxiety” of drivers, help vehicle manufacturers develop fine battery management system and improve the acceptance of pure electric vehicles. Based on the improved symbolic regression algorithm, a new data feature field closely related to the label field is automatically generated to expand the data dimension. Then the dimension expanded data is transmitted to the xgboost model optimized by super parameters to predict the remaining driving range. Compared with the original data using only classical feature fields, the maximum relative absolute error of the dimension expanded data in the prediction accuracy decreases by 4.9%, and the average absolute error and root mean square error decrease by more than 20%. With the increase of time, the error of prediction using the dimension expanded data decreases faster. The results show that the proposed method can optimize the quality of the data set, improve the accuracy of the prediction results and reduce the error, which provides a new idea for the prediction of the remaining driving range of pure electric vehicles.

Key words: electric vehicle, remaining driving range prediction, improved symbolic regression, feature construction, XGBoost

CLC Number: 

  • TP181
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