Hybrid Energy Storage System State-Of-Charge Estimation Using Artificial Neural Network For Micro-Hybrid Applications

Abstract

Using experimental data from a hybrid energy storage system (HESS) composed of two 12V batteries in parallel 60Ah Lead acid (LA) and 8Ah Lithium Iron Phosphate (LFP)–a machine learning approach known as feedforward backpropagation artificial neural network (BPNN) was developed to estimate the state-of-charge (SOC) of both batteries using only one neural network structure. In order to minimize the SOC estimation error the BPNN was trained using a set of five different homologation automotive drive cycles and tested on a sixth drive cycle known as worldwide harmonized light vehicles test cycle (WLTC) to compute the estimation accuracy. A rootmean- squared error (RMSE) of 0.33% and 0.84% is obtained over the test cycle for the LFP and the LA batteries, respectively. The results were then compared to the estimation obtained from a commercially available battery management system (BMS) showing better performance for the proposed approach.