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

Shuai Sun,Jun Zhang,Yongxing Wang,Jun Bi

doi:10.1155/2019/4109148

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.

Highlights

With the rapid development of automobile industry and the continuous improvement of people’s living standard, car ownership and sales continue to rise, which brings a series of energy and environment problems
In the face of increasing energy and environmental problems, the development of new energy vehicles has become a new trend in the automobile industry [1], and the battery electric vehicle (BEV) is the main force of new energy vehicles
Many studies usually take less factors into account when establishing the prediction model of driving range, which may lead to the poor applicability and prediction accuracy of the model

Summary

Introduction

With the rapid development of automobile industry and the continuous improvement of people’s living standard, car ownership and sales continue to rise, which brings a series of energy and environment problems. In the face of increasing energy and environmental problems, the development of new energy vehicles has become a new trend in the automobile industry [1], and the battery electric vehicle (BEV) is the main force of new energy vehicles. Users of BEVs have a range anxiety problem that the residual power will be worried about not ensuring to reach the destination. All of these restrict the promotion and development of BEVs. The range anxiety is an easier case to be figured out than other issues in real-world application of BEVs [3]. It is of great significance to increase the practicability and reliability of BEVs by improving the driving range prediction accuracy to provide users with reliable information [4]

Objectives

Methods

Findings

Discussion

Conclusion