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

TL;DR

This paper presents a deep convolutional neural network model that accurately estimates real-time energy consumption of electric vehicles, aiming to reduce drivers' range anxiety by providing reliable remaining range predictions.

Contribution

The study introduces a novel deep learning approach using vehicle speed, tractive effort, and road elevation to improve energy consumption estimation over existing methods.

Findings

Proposed model outperforms five existing techniques in accuracy.

Deep CNN effectively captures non-linear factors affecting energy consumption.

Model demonstrates consistent lower error across multiple experiments.

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.