Scipediacontent: Scipediacontent moved page Draft Content 136774953 to 238,295m

2021-03-22T18:19:02Z

Scipediacontent moved page Draft Content 136774953 to 238,295m

Scipediacontent: Created page with " == Abstract == It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model buil..."

2021-03-22T18:18:55Z

Created page with " == Abstract == It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model buil..."

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== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_136774953-beopen1012-2719-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://downloads.hindawi.com/journals/jat/2019/4109148.pdf 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 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.pdf],
: [http://downloads.hindawi.com/journals/jat/2019/4109148.xml http://downloads.hindawi.com/journals/jat/2019/4109148.xml],
: [http://dx.doi.org/10.1155/2019/4109148 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 http://dx.doi.org/10.1155/2019/4109148],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2019/4109148 https://www.hindawi.com/journals/jat/2019/4109148],
: [http://downloads.hindawi.com/journals/jat/2019/4109148.pdf http://downloads.hindawi.com/journals/jat/2019/4109148.pdf],
: [https://academic.microsoft.com/#/detail/2909143934 https://academic.microsoft.com/#/detail/2909143934]

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Scipediacontent: Scipediacontent moved page Draft Content 136774953 to 238,295m

Scipediacontent: Created page with " == Abstract == It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model buil..."