A Recurrent Neural Network with Long Short-Term Memory for State of Charge Estimation of Lithium-ion Batteries

Abstract

Lithium-ion batteries (LiBs) have been widely applied in energy storage systems, and accurate State of Charge (SOC) estimation of LiBs has become a key issue. However, SOC is an important LiBs state that cannot be directly observed. It needs to be indirectly estimated by the observable variables, and SOC estimation is a challenging task due to strong nonlinear and complex electrochemical reactions change with temperature in the battery. In this paper, a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) for SOC estimation of LiBs is introduced. By training on the observable variables of LiBs, the voltage, current and temperature can be mapped directly to SOC. Based on the dataset of LiB measured under drive cycles of electric vehicles, the effectiveness of the LSTM-RNN for SOC estimation of LiBs proposed in this paper is validated.