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Electric Vehicles (EVs) are rapidly increasing in popularity as they areenvironment friendly. Lithium Ion batteries are at the heart of EV technologyand 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 isa need to accurately estimate available battery capacity in a battery pack suchthat the available range in a vehicle can be determined. There are varioustechniques available to estimate SOC. In this paper, a data driven approach isselected and a Nonlinear Autoregressive Network with Exogenous Inputs NeuralNetwork (NARXNN) is explored to accurately estimate SOC. NARXNN has been shownto be superior to conventional Machine Learning techniques available in theliterature. The NARXNN model is developed and tested on various EV Drive Cycleslike LA92, US06, UDDS and HWFET to test its performance on real worldscenarios. The model is shown to outperform conventional statistical machinelearning methods and achieve a Mean Squared Error (MSE) in the 1e-5 range.

Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles