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

Previous Articles     Next Articles

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

PDF (PC)

111

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
[1] XIONG R, CAO J Y, YU Q Q, et al. Critical review on the battery state of charge estimation methods for electric vehicles[J].
IEEE Access, 2017, 6: 1832-1843. DOI: 10.1109/ACCESS.2017.2780258.
[2] 王瑞, 宋树祥, 夏海英. 融合阻抗模型与扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 广西师范大学学报(自然科学版), 2021, 39(3): 1-10. DOI: 10.16088/j.issn.1001-6600.2020061109.
[3] 李小霞, 赵金镭. 电动汽车电池荷电状态估算方法研究[J]. 电子设计工程, 2021, 29(4): 177-180, 185.
[4] BI J, WANG Y X, SAI Q Y, et al. Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China[J]. Energy, 2019, 169: 833-843. DOI: 10.1016/j.energy.2018.12.061.
[5] ZHAI G R, LIU S J, WANG Z G, et al. State of energy estimation of lithium titanate battery for rail transit application[J]. Energy Procedia, 2017, 105: 3146-3151. DOI: 10.1016/j.egypro.2017.03.681.
[6] LI K Y, WEI F, TSENG K J, et al. A practical lithium-ion battery model for state of energy and voltage responses prediction incorporating temperature and ageing effects[J]. IEEE Transactions on Industrial Electronics, 2018, 65(8): 6696-6708. DOI: 10.1109/TIE.2017.2779411.
[7] 陈德海, 任永昌, 华铭, 等. 基于STM32-OCV法的纯电动汽车剩余里程预测[J]. 电子技术应用, 2017, 43(12): 33-35, 39. DOI: 10.16157/j.issn.0258-7998.172168.
[8] 林仕立, 宋文吉, 吕杰, 等. 基于电池SOE预测电动汽车的续驶里程[J]. 电池, 2017, 47(3): 137-139. DOI: 10.19535/j.1001-1579.2017.03.003.
[9] 熊瑞. 动力电池管理系统核心算法[M]. 北京: 机械工业出版社, 2018: 87-88.
[10] WANG Z, WANG X H, WANG L Z, et al. Research on electric vehicle (EV) driving range prediction method based on PSO-LSSVM[C]// 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). Piscataway, NJ: IEEE, 2017: 260-265. DOI: 10.1109/ICPHM.2017.7998338.
[11] 段化娟, 尉永清, 刘培玉, 等. 一种面向不平衡分类的改进多决策树算法[J]. 广西师范大学学报(自然科学版), 2020, 38(2): 72-80. DOI: 10.16088/j.issn.1001-6600.2020.02.008.
[12] SARRAFAN K, MUTTAQI K M, SUTANTO D, et al. A real-time range indicator for EVs using web-based environmental data and sensorless estimation of regenerative braking power[J]. IEEE Transactions on Vehicular Technology, 2018, 67 (6): 4743-4756. DOI: 10.1109/TVT.2018.2829728.
[13] 张伟彬, 吴军, 易见兵. 基于RFB网络的特征融合管制物品检测算法研究[J]. 广西师范大学学报(自然科学版), 2021, 39(4): 34-46. DOI: 10.16088/j.issn.1001-6600.2020080902.
[14] SUN S, ZHANG J, BI J, et al. A machine learning method for predicting driving range of battery electric vehicles[J]. Journal of Advanced Transportation, 2019, 2019: 4109148. DOI: 10.1155/2019/4109148.
[15] ZHAO L, YAO W, WANG Y, et al. Machine learning-based method for remaining range prediction of electric vehicles[J]. IEEE Access, 2020, 8: 212423-212441. DOI: 10.1109/ACCESS.2020.3039815.
[16] MODI S, BHATTACHARYA J, BASAK P. Convolutional neural network-bagged decision tree: a hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time[J]. Soft Computing, 2021, 25 (3): 2399-2416. DOI: 10.1007/s00500-020-05310-y.
[17] MURARI A, PELUSO E, LUNGARONI M, et al. Application of symbolic regression to the derivation of scaling laws for tokamak energy confinement time in terms of dimensionless quantities[J]. Nuclear Fusion, 2016, 56(2): 026005. DOI: 10.1088/0029-5515/56/2/026005.
[18] 马炫, 李星, 唐荣俊, 等. 一种求解符号回归问题的粒子群优化算法[J]. 自动化学报, 2020, 46(8): 1714-1726. DOI: 10.16383/j.aas.c180035.
[19] 韩博文. 考虑实时需求的需求响应式公交调度方法研究[J]. 广西师范大学学报(自然科学版), 2019, 37(3): 9-20. DOI: 10.16088/j.issn.1001-6600.2019.03.002.
[20] NEMBHARD D A, SUN Y Z. A symbolic genetic programming approach for identifying models of learning-by-doing[J]. Computers & Industrial Engineering, 2019, 131: 524-533. DOI: 10.1016/j.cie.2018.08.020.
[21] PAN B Y. Application of XGBoost algorithm in hourly PM2. 5 concentration prediction[J]. IOP Conference Series: Earth and Environmental Science, 2018, 113: 012127. DOI: 10.1088/1755-1315/113/1/012127.
[22] 武康康, 周鹏, 陆叶, 等. 基于小批量梯度下降法的FIR滤波器[J]. 广西师范大学学报(自然科学版), 2021, 39(4): 9-20. DOI: 10.16088/j.issn.1001-6600.2020062602.
[23] DHALIWAL S S, NAHID A A, ABBAS R. Effective intrusion detection system using XGBoost[J]. Information, 2018, 9(7): 149. DOI: 10.3390/info9070149.
[24] LI W, YIN Y B, QUAN X W, et al. Gene expression value prediction based on XGBoost algorithm[J]. Frontiers in Genetics, 2019, 10: 1027. DOI: 10.3389/fgene.2019.01077.
[25] DONG W, HUANG Y M, LEHANE B, et al. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring[J]. Automation in Construction, 2020, 114: 103155. DOI: 10.1016/j.autcon.2020.103155.
[26] MA J B, GAO X Y. A filter-based feature construction and feature selection approach for classification using Genetic Programming[J]. Knowledge-Based Systems, 2020, 196: 105806. DOI: 10.1016/j.knosys.2020.105806.
[27] 张灿龙, 李燕茹, 李志欣, 等. 基于核相关滤波与特征融合的分块跟踪算法[J]. 广西师范大学学报(自然科学版), 2020, 38(5): 12-23. DOI: 10.16088/j.issn.1001-6600.2020.05.002.
[28] 杨弦, 党延忠, 吴江宁. 基于符号回归的产品评论数量与购买数量关系分析[J]. 系统工程学报, 2020, 35(3): 289-300. DOI: 10.13383/j.cnki.jse.2020.03.001.
[1] WANG Rui, SONG Shuxiang, XIA Haiying. Estimation of Lithium Battery SOC with Fusion Impedance Model and Extended Kalman Filtering [J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(3): 1-10.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] HU Jinming, WEI Duqu. Hybrid Projective Synchronization of Fractional-order PMSM with Different Orders[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 1 -8 .
[2] WU Kangkang, ZHOU Peng, LU Ye, JIANG Dan, YAN Jianghong, QIAN Zhengcheng, GONG Chuang. FIR Equalizer Based on Mini-batch Gradient Descent Method[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 9 -20 .
[3] LIU Dong, ZHOU Li, ZHENG Xiaoliang. A Very Short-term Electric Load Forecasting Based on SA-DBN[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 21 -33 .
[4] ZHANG Weibin, WU Jun, YI Jianbing. Research on Feature Fusion Controlled Items Detection Algorithm Based on RFB Network[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 34 -46 .
[5] WANG Jinyan, HU Chun, GAO Jian. An OBDD Construction Method for Knowledge Compilation[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 47 -54 .
[6] LU Miao, HE Dengxu, QU Liangdong. Grey Wolf Optimization Algorithm Based on Elite Learning for Nonlinear Parameters[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 55 -67 .
[7] LI Lili, ZHANG Xingfa, LI Yuan, DENG Chunliang. Daily GARCH Model Estimation Using High Frequency Data[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 68 -78 .
[8] LI Songtao, LI Qunhong, ZHANG Wen. Co-dimension-two Grazing Bifurcation and Chaos Control of Three-degree-of-freedom Vibro-impact Systems[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 79 -92 .
[9] ZHAO Hongtao, LIU Zhiwei. Decompositions of λ-fold Complete Bipartite 3-uniform Hypergraphs λK(3)n,n into Hypergraph Triangular Bipyramid[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 93 -98 .
[10] LI Meng, CAO Qingxian, HU Baoqing. Spatial-temporal Analysis of Continental Coastline Migration from 1960 to 2018 in Guangxi, China[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 99 -108 .
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd