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Energies 2018, 11(5), 1040; https://doi.org/10.3390/en11051040

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

1,2,*, 1,2, 1 and 1,2
1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Received: 19 March 2018 / Revised: 31 March 2018 / Accepted: 12 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Energy Efficient and Smart Cities)
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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. View Full-Text
Keywords: battery electric vehicles; charging time prediction; data analysis; regression; time-series model battery electric vehicles; charging time prediction; data analysis; regression; time-series model
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Bi, J.; Wang, Y.; Sun, S.; Guan, W. Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing. Energies 2018, 11, 1040.

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