Latest revision as of 19:19, 22 March 2021

Abstract

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.

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• http://downloads.hindawi.com/journals/jat/2019/4109148.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2019/4109148 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2019/4109148.pdf,

http://downloads.hindawi.com/journals/jat/2019/4109148.xml,

http://dx.doi.org/10.1155/2019/4109148 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2019/4109148,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2019/4109148,

http://downloads.hindawi.com/journals/jat/2019/4109148.pdf,

https://academic.microsoft.com/#/detail/2909143934