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Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness

Author

Listed:
  • Jinhwan Park

    (Graduate School of Mechanical Design & Production Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Donghyeon Yoo

    (Graduate School of Mechanical Design & Production Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Jaemin Moon

    (Graduate School of Mechanical Design & Production Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Janghyeok Yoon

    (Department of Industrial Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Jungtae Park

    (Department of Chemical Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Seungae Lee

    (Department of Chemical Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Doohee Lee

    (Department of Electrical and Electronics Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Changwan Kim

    (School of Mechanical Engineering, Konkuk University, 120, Neung dong-ro, Gwangjin-gu, Seoul 05029, Korea)

Abstract

Lithium-ion batteries (LIBs) are increasingly employed in electric vehicles (EVs) owing to their advantages, such as low weight, and high energy and power densities. However, the uncertainty encountered in the manufacturing of LIB cells increases the failure rate and causes cell-to-cell variations, thereby degrading the battery capacity and lifetime. In this study, the reliability and robustness of LIB cells were improved using the design of experiments (DOE), and the reliability-based robust design optimization (RBRDO) approaches. First, design factors sensitive to the energy density and power density were selected as design variables through sensitivity analysis using the DOE. RBRDO was performed to maximize the energy density while reducing the failure rate and cell-to-cell variations. To verify the superiority of the reliability and robustness offered by RBRDO, the obtained results were compared with those from conventional deterministic design optimization (DDO), and reliability-based design optimization (RBDO). RBRDO increased the mean of the energy density by 33.5% compared to the initial value and reduced the failure rate by 98.9%, due to improved reliability, compared to DDO. Moreover, RBRDO reduced the standard deviation in the energy density (i.e., cell-to-cell variations) by 30.0% due to the improved robustness compared to RBDO.

Suggested Citation

  • Jinhwan Park & Donghyeon Yoo & Jaemin Moon & Janghyeok Yoon & Jungtae Park & Seungae Lee & Doohee Lee & Changwan Kim, 2021. "Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness," Energies, MDPI, vol. 14(19), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6236-:d:647503
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    References listed on IDEAS

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    1. Zubi, Ghassan & Dufo-López, Rodolfo & Carvalho, Monica & Pasaoglu, Guzay, 2018. "The lithium-ion battery: State of the art and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 292-308.
    2. Donghyeon Yoo & Jinhwan Park & Jaemin Moon & Changwan Kim, 2021. "Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes," Energies, MDPI, vol. 14(19), pages 1-15, September.
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    Cited by:

    1. Yang, Jiong & Cheng, Fanyong & Liu, Zhi & Duodu, Maxwell Mensah & Zhang, Mingyan, 2023. "A novel semi-supervised fault detection and isolation method for battery system of electric vehicles," Applied Energy, Elsevier, vol. 349(C).
    2. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    3. Khan, F.M. NizamUddin & Rasul, Mohammad G. & Sayem, A.S.M. & Mandal, Nirmal K., 2024. "A computational analysis of effects of electrode thickness on the energy density of lithium-ion batteries," Energy, Elsevier, vol. 288(C).

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