Convolutional Gated Recurrent Unit-Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

Abstract

For most deep learning practitioners, recurrent networks are often used for sequence modeling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks. Problems here are which architecture we should use for a new sequence modeling. By integrating and systematically evaluating the general convolution and recurrent architecture used for sequence modeling, a convolution gated recurrent unit (CNN-GRU) network is proposed for the state-of-charge (SOC) estimation of lithium-ion batteries in this paper. Deep-learning models are well suited for SOC estimation because a battery management system is time-varying and non-linear. The CNN-GRU model is trained using data collected from the battery-discharging processes, such as the dynamic stress test and the federal urban driving schedule. The experimental results show that the proposed method can achieve higher estimation accuracy than two commonly used deep learning models (recurrent neural network and gated recurrent unit) and two traditional machine learning approaches (support vector machine and extreme learning machine) for SOC estimation of lithium-ion batteries.