Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety.

Clicks: 362
ID: 55468
2019
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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.
Reference Key
modi2019estimationisa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Modi, Shatrughan;Bhattacharya, Jhilik;Basak, Prasenjit;
Journal ISA transactions
Year 2019
DOI
S0019-0578(19)30401-X
URL
Keywords
deep convolutional neural network electric vehicle energy consumption estimation

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