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Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

Author

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  • Ana-Irina Stroe

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Jinhao Meng

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark
    School of Automation, Northwestern Polytechnical University, 710072 Xi’an, China)

  • Daniel-Ioan Stroe

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Maciej Świerczyński

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Remus Teodorescu

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Søren Knudsen Kær

    (Department of Energy, Aalborg University, 9220 Aalborg, Denmark)

Abstract

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

Suggested Citation

  • Ana-Irina Stroe & Jinhao Meng & Daniel-Ioan Stroe & Maciej Świerczyński & Remus Teodorescu & Søren Knudsen Kær, 2018. "Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries," Energies, MDPI, vol. 11(4), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:795-:d:138804
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    References listed on IDEAS

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    1. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    3. Wei, Zhongbao & Lim, Tuti Mariana & Skyllas-Kazacos, Maria & Wai, Nyunt & Tseng, King Jet, 2016. "Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery," Applied Energy, Elsevier, vol. 172(C), pages 169-179.
    4. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    5. Ximing Cheng & Liguang Yao & Yinjiao Xing & Michael Pecht, 2016. "Novel Parametric Circuit Modeling for Li-Ion Batteries," Energies, MDPI, vol. 9(7), pages 1-15, July.
    6. Thomas R. B. Grandjean & Andrew McGordon & Paul A. Jennings, 2017. "Structural Identifiability of Equivalent Circuit Models for Li-Ion Batteries," Energies, MDPI, vol. 10(1), pages 1-16, January.
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