The automotive industry is moving toward a more cleaner energy source and the usage of Electric Vehicles is increasing. One of the major problems of the electricvehicle is the driving range covered with a fully charged battery. The main reason for this is the power consumption by different electronic components in the vehicle. The major source of power consumption is an electric motor, and apart from this, there are many other electric units in the vehicle which consumes power.To achieveoptimalpowerdeliveredtothedifferentpowerconsuming components and delivering the optimal required power to the electric motor and thereby the increasing the driving range of the electric vehicle is a major challenge. In the proposed method, a deep learning algorithm based on MRNN (Modular Recurrent Neural network) is used to predict the power requirements of the electric vehicle from different data inside the vehicle. The proposed method uses different data and parameter from the electric vehicle like power requirement to the electric motor under different driving conditions, power requirements of other devices in the vehicle are used to model the system and with the MRNN deep learning algorithm to train the network to predictthepowerrequirementsandprovidingoptimalpowerand thereby enhancing the driving range. By predicting the power demand of the vehicle, the battery power distribution to the motor can be more optimized as instead of delivering the power as such it can be controlled based on the classification of the powerdemand. © 2019 IEEE.