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SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

Author

Listed:
  • Ngoc-Tham Tran

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Abdul Basit Khan

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Thanh-Tung Nguyen

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Dae-Wook Kim

    (Department of Economics, Soongsil University, Seoul 06978, Korea)

  • Woojin Choi

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

Suggested Citation

  • Ngoc-Tham Tran & Abdul Basit Khan & Thanh-Tung Nguyen & Dae-Wook Kim & Woojin Choi, 2018. "SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation," Energies, MDPI, vol. 11(7), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1620-:d:153619
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    References listed on IDEAS

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    1. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
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    Cited by:

    1. Richard Bustos & Stephen Andrew Gadsden & Pawel Malysz & Mohammad Al-Shabi & Shohel Mahmud, 2022. "Health Monitoring of Lithium-Ion Batteries Using Dual Filters," Energies, MDPI, vol. 15(6), pages 1-16, March.
    2. Taysa Millena Banik Marques & João Lucas Ferreira dos Santos & Diego Solak Castanho & Mariane Bigarelli Ferreira & Sergio L. Stevan & Carlos Henrique Illa Font & Thiago Antonini Alves & Cassiano Moro , 2023. "An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles," Energies, MDPI, vol. 16(13), pages 1-18, June.
    3. Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2021. "Battery Model Identification Approach for Electric Forklift Application," Energies, MDPI, vol. 14(19), pages 1-26, September.
    4. Dongcheul Lee & Seohee Kang & Chee Burm Shin, 2022. "Modeling the Effect of Cell Variation on the Performance of a Lithium-Ion Battery Module," Energies, MDPI, vol. 15(21), pages 1-15, October.

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