A Cycle-based Recurrent Neural Network for State-of-Charge Estimation of Li-ion Battery Cells

Abstract

This paper proposes a neural network model for state-of-charge (SOC) estimation in lithium-ion battery cells. The proposed deep neural network model is a cycle-based recurrent model that leverages relevant information from historical cycles to provide reliable estimates of the state-of-charge of on-going cycles within a mean-absolute error (MAE) of 1%. In addition, the proposed model can be trained in a relatively short time. Details on the model followed by experimental verification are provided.