Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

Abstract

The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and Nonlinear Auto-Regressive network with exogenous inputs (NARX) have been used for designing a neural network model. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.