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Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Author

Listed:
  • Jun Bi

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Yongxing Wang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Shuai Sun

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Wei Guan

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Battery electric vehicles (BEVs) reduce energy consumption and air pollution as compared with conventional vehicles. However, the limited driving range and potential long charging time of BEVs create new problems. Accurate charging time prediction of BEVs helps drivers determine travel plans and alleviate their range anxiety during trips. This study proposed a combined model for charging time prediction based on regression and time-series methods according to the actual data from BEVs operating in Beijing, China. After data analysis, a regression model was established by considering the charged amount for charging time prediction. Furthermore, a time-series method was adopted to calibrate the regression model, which significantly improved the fitting accuracy of the model. The parameters of the model were determined by using the actual data. Verification results confirmed the accuracy of the model and showed that the model errors were small. The proposed model can accurately depict the charging time characteristics of BEVs in Beijing.

Suggested Citation

  • Jun Bi & Yongxing Wang & Shuai Sun & Wei Guan, 2018. "Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing," Energies, MDPI, vol. 11(5), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1040-:d:142973
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    References listed on IDEAS

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    Cited by:

    1. Kai Liu & Sijia Luo & Jing Zhou, 2020. "En-Route Battery Management and a Mixed Network Equilibrium Problem Based on Electric Vehicle Drivers’ En-Route Recharging Behaviors," Energies, MDPI, vol. 13(16), pages 1-14, August.
    2. Yongxing Wang & Jun Bi & Chaoru Lu & Cong Ding, 2020. "Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network," Energies, MDPI, vol. 13(9), pages 1-24, May.

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