Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous

The lithium-ion battery (LiB) has become the most widely used energy storage system for electric vehicles (EVs) due to its many advantages. The EV battery pack needs a battery management system (BMS) to estimate the state of charge (SOC) and balance the energy capacity through the cells. Apart from the fact that it is still challenging to accurately solve, the SOC forecasting represents an important concern in the study sector. This research proposes an effective battery SOC forecasting approach utilizing the non-linear autoregressive exogenous model (NARX) time’s series optimized Levenberg-Marquardt training algorithm, and Bayesian-Regularization (BR). The suggested technique is well-known for its resilience and high performance in nonlinear and complex system prediction, and it is extensively used in a wide range of disciplines. Also, the precision of the NARX technique has been investigated as a function of training data sets, error classifications based on experimental data of LiB. Both algorithms were evaluated with experimental data. Discharging followed by resting process was conducted on a 2.6 Ah LiB. They demonstrate good convergence in the low error and regression. In an effort to address a gap in the field, this paper offers a comparison between NARX-LM and NARX-BR algorithms for the LiB SOC prediction. Both algorithms are optimized the ANN using times series analysis based in the same training data. The results show that NARX-BR is more rapid and accurate with a low mean square error (MSE) of 2.39 10^-5 than NARX-LM, which achieved an MSE of 1.11. Thus, it shows NARX-BR as an effective technique for LiB SOC prediction.