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A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles
Joint Authors
Bi, Jun
Sun, Shuai
Zhang, Jun
Wang, Yongxing
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-09
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information.
A model built by the conventional multiple linear regression method is feasible to predict the driving range, but the residual errors between -3.6975 km and 3.3865 km are relatively unfaithful for real-world driving.
The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods.
The result of the machine learning method shows that the maximum prediction error is 1.58 km, the minimum prediction error is -1.41 km, and the average prediction error is about 0.7 km.
The predictive accuracy of the gradient boosting decision tree is compared against that of the conventional approaches.
American Psychological Association (APA)
Sun, Shuai& Zhang, Jun& Bi, Jun& Wang, Yongxing. 2019. A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1169840
Modern Language Association (MLA)
Sun, Shuai…[et al.]. A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles. Journal of Advanced Transportation No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1169840
American Medical Association (AMA)
Sun, Shuai& Zhang, Jun& Bi, Jun& Wang, Yongxing. A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1169840
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169840