A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles

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 a...

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Bibliographic Details
Published inJournal of advanced transportation Vol. 2019; no. 2019; pp. 1 - 14
Main Authors Wang, Yongxing, Bi, Jun, Zhang, Jun, Sun, Shuai
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
Hindawi Limited
Hindawi-Wiley
Subjects
Accuracy
Algorithms
Automobile industry
Decision trees
Electric vehicles
Energy consumption
Environmental impact
Errors
Learning algorithms
Machine learning
Predictions
Regression analysis
Studies
Transportation
Variables
Wireless networks
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2019/4109148