State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network

Shuqing Li, Chuankun Ju, Jianliang Li, Ri Fang, Zhifei Tao, Bo Li and Tingting Zhang
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Shuqing Li: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
Chuankun Ju: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
Jianliang Li: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
Ri Fang: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
Zhifei Tao: Bureau of Geophysical Exploration Inc., CNPC, Baoding 072751, China
Bo Li: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
Tingting Zhang: College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China

Energies, 2021, vol. 14, issue 2, 1-21

Abstract: Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input feature but also takes into account the influence of battery degradation process, including charging and discharging times, as well as the last discharge charge. The SOC of the battery is estimated by the network under three different working conditions, and the results show that the average error of the proposed neural network is less than 3%. Compared with other neural network structures, the proposed network estimation results are more stable and accurate.

Keywords: lithium-ion batteries; state of charge estimation; battery degradation process; recurrent neural network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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