An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery

Wenxian Duan, Chuanxue Song, Silun Peng, Feng Xiao, Yulong Shao and Shixin Song
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Wenxian Duan: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Chuanxue Song: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Silun Peng: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Feng Xiao: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Yulong Shao: Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China
Shixin Song: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

Energies, 2020, vol. 13, issue 23, 1-19

Abstract: An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions.

Keywords: state-of-charge; lithium-ion battery; gated recurrent unit; non-Gaussian noises; robustness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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