Sparse autoencoded long short-term memory network for state-of-charge estimations

Abstract

This paper proposes the Sparse Autoencoded Long Short-Term Memory network (SAEL) for long-term State-of-Charge (SOC) estimations. SAEL addresses the challenge of estimating the SOC near the end-of-life after only running a few charge-discharge cycles. SAEL transforms the inputs (e.g., voltage) into a space of informative features for SOC estimations. SAEL then feeds the transformed features into an LSTM network to identify temporal trends that support long-term SOC estimation. In our experiments, SAEL outperformed benchmark models by over 63% when evaluated on three battery cells. SAEL showed an MAE of 2.6% for the last twenty cycles when trained only on the initial five charge-discharge cycles.