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

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