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Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

Author

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  • Alejandro Gismero

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, 9220 Aalborg, Denmark)

  • Erik Schaltz

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, 9220 Aalborg, Denmark)

  • Daniel-Ioan Stroe

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, 9220 Aalborg, Denmark)

Abstract

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

Suggested Citation

  • Alejandro Gismero & Erik Schaltz & Daniel-Ioan Stroe, 2020. "Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage," Energies, MDPI, vol. 13(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1811-:d:343187
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    References listed on IDEAS

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    1. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    2. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

    1. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    2. Sneha Sundaresan & Bharath Chandra Devabattini & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation," Energies, MDPI, vol. 15(23), pages 1-23, December.
    3. Bachir Zine & Haithem Bia & Amel Benmouna & Mohamed Becherif & Mehroze Iqbal, 2022. "Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles," Energies, MDPI, vol. 15(21), pages 1-15, November.
    4. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    5. Chien-Chun Huang & Yu-Chen Liu & Chia-Ching Lin & Chih-Yu Ni & Huang-Jen Chiu, 2020. "Stacked Buck Converter: Current Ripple Elimination Effect and Transient Response," Energies, MDPI, vol. 14(1), pages 1-25, December.
    6. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.
    7. Diego Salazar & Marcelo Garcia, 2022. "Estimation and Comparison of SOC in Batteries Used in Electromobility Using the Thevenin Model and Coulomb Ampere Counting," Energies, MDPI, vol. 15(19), pages 1-13, September.
    8. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    9. Yang Guo & Ziguang Lu, 2022. "A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias," Energies, MDPI, vol. 15(4), pages 1-18, February.
    10. Bizhong Xia & Guanyong Zhang & Huiyuan Chen & Yuheng Li & Zhuojun Yu & Yunchao Chen, 2022. "Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-20, April.
    11. Jingyeong Park & Jeonghyeon Choi & Hyeondeok Jo & Daisuke Kodaira & Sekyung Han & Moses Amoasi Acquah, 2022. "Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function," Energies, MDPI, vol. 15(21), pages 1-16, October.

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