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

Jun Bi, Yongxing Wang, Shuai Sun and Wei Guan
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Jun Bi: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Yongxing Wang: School of Traffic and Transportation, 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

Energies, 2018, vol. 11, issue 5, 1-18

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.

Keywords: battery electric vehicles; charging time prediction; data analysis; regression; time-series model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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