predicting charging time of battery electric vehicles based on regression and time-series methods: a case study of beijing

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ID: 215338
2018
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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.
Reference Key
bi2018energiespredicting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jun Bi;Yongxing Wang;Shuai Sun;Wei Guan
Journal acs combinatorial science
Year 2018
DOI
10.3390/en11051040
URL
http://www.mdpi.com/1996-1073/11/5/1040
Keywords
data analysis regression

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