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

Author

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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
    These authors contributed equally to this work.)

  • Jianliang Li

    (College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:306-:d:476774
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    References listed on IDEAS

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    Cited by:

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    3. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    4. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    5. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    6. Yirga Belay Muna & Cheng-Chien Kuo, 2022. "Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology," Energies, MDPI, vol. 15(12), pages 1-20, June.
    7. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    8. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).
    9. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    10. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Maria Cortada-Torbellino & Abdelali El Aroudi & Hugo Valderrama-Blavi, 2023. "Outlook of Lithium-Ion Battery Regulations and Procedures to Improve Cell Degradation Detection and Other Alternatives," Energies, MDPI, vol. 16(5), pages 1-13, March.
    12. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).

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