A Method for Estimating State of Charge of Lithium-Ion Batteries Based on Deep Learning

Abstract

State of charge (SOC) estimation of lithium-ion batteries is a problem of time series. In deep learning methods, both convolutional neural network (CNN) and recurrent neural network (RNN) can be used to solve such problems. In this paper, based on deep learning, a hybrid neural network model is proposed to estimate the SOC of lithium-ion batteries by taking the sequence of sampling points of voltage, current and temperature as input. The model is mainly composed of three modules, namely, convolutional module, ultra-lightweight subspace attention mechanism (ULSAM) module and the gated recurrent unit (GRU) module. Convolutional module and ULSAM module are responsible for extracting the feature information from the sequence of sampling points and outputting feature maps. GRU module is responsible for processing the sequences of the feature maps and outputting the value of SOC. The proposed model is tested on the public NASA Randomized Battery Usage dataset and Oxford Battery Degradation dataset. The experimental results show that the proposed model can obtain a relatively accurate SOC estimation at unknown aging state and complex operating conditions.