A hybrid long short-term memory network for state-of-charge estimation of li-ion batteries

Abstract

This paper proposes a hybrid LSTM network for robust state-of-charge estimation of Li-ion batteries. The proposed model improves the estimation accuracy of a typical LSTM by using the SOC estimations of other trained machine learning (ML) models in addition to the original measurable battery cell parameters to train the LSTM. The hybrid LSTM intrinsically learns to timely activate the proper ML model by learning the complex dependencies between the accuracy of ML models and cell parameters. The proposed model is shown to achieve around 25% improvement in MAE for the last twenty cycles (near end-of-life) SOC estimation.