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Modeling the Effect of Cell Variation on the Performance of a Lithium-Ion Battery Module

Author

Listed:
  • Dongcheul Lee

    (Department of Chemical Engineering and Division of Energy Systems Research, Ajou University, Suwon 16499, Korea)

  • Seohee Kang

    (Department of Chemical Engineering and Division of Energy Systems Research, Ajou University, Suwon 16499, Korea)

  • Chee Burm Shin

    (Department of Chemical Engineering and Division of Energy Systems Research, Ajou University, Suwon 16499, Korea)

Abstract

Owing to the variation between lithium-ion battery (LIB) cells, early discharge termination and overdischarge can occur when cells are coupled in series or parallel, thereby triggering a decrease in LIB module performance and safety. This study provides a modeling approach that considers the effect of cell variation on the performance of LIB modules in energy storage applications for improving the reliability of the power quality of energy storage devices and efficiency of the energy system. Ohm’s law and the law of conservation of charge were employed as the governing equations to estimate the discharge behavior of a single strand composing of two LIB cells connected in parallel based on the polarization properties of the electrode. Using the modeling parameters of a single strand, the particle swarm optimization algorithm was adopted to predict the discharge capacity and internal resistance distribution of 14 strands connected in series. Based on the model of the LIB strand to predict the discharge behavior, the effect of cell variation on the deviation of the discharge termination voltage and depth of discharge imbalance was modeled. The validity of the model was confirmed by comparing the experimental data with the modeling results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8054-:d:957687
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    References listed on IDEAS

    as
    1. 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.
    2. Majid Astaneh & Jelena Andric & Lennart Löfdahl & Dario Maggiolo & Peter Stopp & Mazyar Moghaddam & Michel Chapuis & Henrik Ström, 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications," Energies, MDPI, vol. 13(14), pages 1-27, July.
    3. David Beck & Philipp Dechent & Mark Junker & Dirk Uwe Sauer & Matthieu Dubarry, 2021. "Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review," Energies, MDPI, vol. 14(11), pages 1-25, June.
    4. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    5. Killer, Marvin & Farrokhseresht, Mana & Paterakis, Nikolaos G., 2020. "Implementation of large-scale Li-ion battery energy storage systems within the EMEA region," Applied Energy, Elsevier, vol. 260(C).
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