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Article

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

1
College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
2
Bureau of Geophysical Exploration Inc., CNPC, Baoding 072751, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2021, 14(2), 306; https://doi.org/10.3390/en14020306
Received: 30 September 2020 / Revised: 21 December 2020 / Accepted: 31 December 2020 / Published: 8 January 2021
(This article belongs to the Special Issue Performance Test and Thermo-Mechanical Modeling of Lithium Batteries)
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. View Full-Text
Keywords: lithium-ion batteries; state of charge estimation; battery degradation process; recurrent neural network lithium-ion batteries; state of charge estimation; battery degradation process; recurrent neural network
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MDPI and ACS Style

Li, S.; Ju, C.; Li, J.; Fang, R.; Tao, Z.; Li, B.; Zhang, T. State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network. Energies 2021, 14, 306. https://doi.org/10.3390/en14020306

AMA Style

Li S, Ju C, Li J, Fang R, Tao Z, Li B, Zhang T. State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network. Energies. 2021; 14(2):306. https://doi.org/10.3390/en14020306

Chicago/Turabian Style

Li, Shuqing, Chuankun Ju, Jianliang Li, Ri Fang, Zhifei Tao, Bo Li, and Tingting Zhang. 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network" Energies 14, no. 2: 306. https://doi.org/10.3390/en14020306

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