Citation

Modi S, Bhattacharya J, Basak P. ISA Trans. 2019; ePub(ePub): ePub.

Affiliation

Electrical & Instrumentation Engineering Department, Thapar Institute of Engineering & Technology, Patiala 147004, India.

Copyright

(Copyright © 2019, Instrument Society of America, Publisher Elsevier Publishing)

DOI

10.1016/j.isatra.2019.08.055

PMID

31493873

Abstract

The goal of this work is to reduce driver's range anxiety by estimating the real-time energy consumption of electric vehicles using deep convolutional neural network. The real-time estimate can be used to accurately predict the remaining range for the vehicle and hence, can reduce driver's range anxiety. In contrast to existing techniques, the non-linearity and complexity induced by the combination of influencing factors make the problem more suitable for a deep learning approach. The proposed approach requires three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variants are performed to explore the impact of number of layers and input feature descriptors. The comparison of proposed approach and five of the existing techniques show that the proposed model performed consistently better than existing techniques with lowest error.

Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.

Language: en

Keywords

Deep Convolutional Neural Network; Electric vehicle; Energy consumption estimation