Robust feedforward neural network-based state of charge estimation of lithium-ion batteries

State-of-charge (SOC) estimation is essential for electric vehicles (EVs') optimal functioning, but it is a difficult problem due to the very dynamic operating environment. By far, a lot of studies have been done on techniques and modelling to precisely estimate SOC for Lithium-Ion Batteries (LiBs) used in electric vehicles. In this paper, Feedforward Neural Network (FNN)-based SOC estimation has been proposed. This network uses discharge current, terminal voltage, and temperature as input and provides SOC as an output. The proposed FNN estimator is applied to Lithium-ion batteries (LiBs) to check its validity in SOC estimation. The proposed model is simulated in MATLAB software and the results show that the estimation of SOC by FNN is better compared to conventional methods with reduced MAE.