  Electric Vehicles (EVs) are rapidly increasing in popularity as they are
environment friendly. Lithium Ion batteries are at the heart of EV technology
and contribute to most of the weight and cost of an EV. State of Charge (SOC)
is a very important metric which helps to predict the range of an EV. There is
a need to accurately estimate available battery capacity in a battery pack such
that the available range in a vehicle can be determined. There are various
techniques available to estimate SOC. In this paper, a data driven approach is
selected and a Nonlinear Autoregressive Network with Exogenous Inputs Neural
Network (NARXNN) is explored to accurately estimate SOC. NARXNN has been shown
to be superior to conventional Machine Learning techniques available in the
literature. The NARXNN model is developed and tested on various EV Drive Cycles
like LA92, US06, UDDS and HWFET to test its performance on real world
scenarios. The model is shown to outperform conventional statistical machine
learning methods and achieve a Mean Squared Error (MSE) in the 1e-5 range.
