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Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation

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  • Kim, Jonghoon
  • Cho, B.H.

Abstract

This paper investigates a practical universal modeling of multi-cell battery strings in series and parallel connections to show high an accuracy SOC (state-of-charge) estimation based on the EKF (extended Kalman filter) if cell-to-cell variations are taken into account and settled by the screening process. Through the screening process for the selection of the cells that have similar electrochemical characteristics, this study describes an effort to provide each equivalent circuit model for multi-cell battery strings in series, parallel, and series/parallel connections. Three circuit-based multi-cell battery models are validated against the experimental data of the discharging/charging behavior in terms of the discharging/charging voltage, discharge capacity, OCV (open-circuit voltage), and internal resistances when compared with the experimental data of a single cell. According to the relation between a multi-cell battery string and a single cell, these models can be easily developed from a single cell model, the validity of which was demonstrated regarding its high accuracy in predicting cell performance. The proposed multi-cell battery model has been extensively validated by the model-based SOC estimation using the EKF for a Li-Ion cell. If the model parameters of a single cell are correctly measured and used in the multi-cell battery model, the accuracy in the SOC estimation of a multi-cell battery string could be significantly improved.

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  • Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    • Handle: RePEc:eee:energy:v:57:y:2013:i:c:p:581-599
    DOI: 10.1016/j.energy.2013.04.050
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    References listed on IDEAS

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    Cited by:

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    3. Bizhong Xia & Yadi Yang & Jie Zhou & Guanghao Chen & Yifan Liu & Huawen Wang & Mingwang Wang & Yongzhi Lai, 2019. "Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis," Energies, MDPI, vol. 12(15), pages 1-17, August.
    4. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    5. Dai, Haifeng & Guo, Pingjing & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan, 2015. "ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries," Energy, Elsevier, vol. 80(C), pages 350-360.
    6. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    7. Thomas Bruen & James Michael Hooper & James Marco & Miguel Gama & Gael Henri Chouchelamane, 2016. "Analysis of a Battery Management System (BMS) Control Strategy for Vibration Aged Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion 18650 Battery Cells," Energies, MDPI, vol. 9(4), pages 1-20, April.
    8. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.
    9. Lee, Seongjun & Kim, Jonghoon, 2015. "Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles," Energy, Elsevier, vol. 83(C), pages 462-473.
    10. Pei, Lei & Zhu, Chunbo & Wang, Tiansi & Lu, Rengui & Chan, C.C., 2014. "Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 66(C), pages 766-778.
    11. Chuanxue Song & Yulong Shao & Shixin Song & Cheng Chang & Fang Zhou & Silun Peng & Feng Xiao, 2017. "Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control," Energies, MDPI, vol. 10(3), pages 1-13, March.
    12. Xiaoyu Li & Kai Song & Guo Wei & Rengui Lu & Chunbo Zhu, 2015. "A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm," Energies, MDPI, vol. 8(8), pages 1-26, July.
    13. Zhang, Xu & Wang, Yujie & Yang, Duo & Chen, Zonghai, 2016. "An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model," Energy, Elsevier, vol. 115(P1), pages 219-229.
    14. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
    15. Yanhui, Zhang & Wenji, Song & Guoqing, Xu, 2014. "Relaxation effect analysis on the initial state of charge for LiNi0.5Co0.2Mn0.3O2/graphite battery," Energy, Elsevier, vol. 74(C), pages 368-373.
    16. Shen, Yanqing, 2014. "Hybrid unscented particle filter based state-of-charge determination for lead-acid batteries," Energy, Elsevier, vol. 74(C), pages 795-803.
    17. Firouz, Y. & Relan, R. & Timmermans, J.M. & Omar, N. & Van den Bossche, P. & Van Mierlo, J., 2016. "Advanced lithium ion battery modeling and nonlinear analysis based on robust method in frequency domain: Nonlinear characterization and non-parametric modeling," Energy, Elsevier, vol. 106(C), pages 602-617.
    18. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.

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