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Data-driven designs and multi-scale simulations of enhanced ion transport in low-temperature operation for lithium-ion batteries

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Abstract

The low-temperature operation of lithium-ion batteries (LIBs) is a challenge in achieving high-stability battery technology. Moreover, the design and analysis of low-temperature electrolytes are impeded by the limited understanding of various solvent components and their combinations. In this study, we present a data-driven strategy to design electrolytes with high ionic conductivity at low temperature using various machine-learning algorithms, such as random forest and feedforward neural networks. To establish a link between prediction of electrolyte chemistry and cell performance of LIBs, we performed parameter-free molecular dynamics (MD) prediction of various salt concentrations and temperatures for target solvents. Finally, electrochemical modeling was performed using these properties as the required material parameters. Combining works of the fully parameterized Newman models, parameter-free MD, and data-driven prediction of electrolyte chemistry can help measure the discharge voltage of batteries and enable in silico engineering of electrolyte development for realizing low-temperature operation of LIBs.

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Acknowledgements

This research was supported by the Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2021M1A2A2038142). This research was also supported by the Chung-Ang University Research Scholarship Grants in 2021.

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  1. These authors are equally contributed.

Authors and Affiliations

  1. School of Energy Systems Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Korea

    Hongjun Chang, Yoojin Park, Ju-Hee Kim, Seowan Park & Janghyuk Moon

  2. Next Generation Battery Research Center, Korea Electrotechnology Research Institute (KERI), 12, Jeongiui-gil, Seongsan-gu, Changwon-si, Gyeongsangnam-do, 51543, Korea

    Byung Gon Kim

  3. Electro-functional Materials Engineering, University of Science and Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon, 34113, Korea

    Byung Gon Kim

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  1. Hongjun Chang
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  2. Yoojin Park
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  3. Ju-Hee Kim
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  4. Seowan Park
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Correspondence to Byung Gon Kim or Janghyuk Moon.

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Data-driven designs and multi-scale simulations of enhanced ion transport in low-temperature operation for lithium-ion batteries

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Chang, H., Park, Y., Kim, JH. et al. Data-driven designs and multi-scale simulations of enhanced ion transport in low-temperature operation for lithium-ion batteries. Korean J. Chem. Eng. 40, 539–547 (2023). https://doi.org/10.1007/s11814-022-1364-0

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  • DOI: https://doi.org/10.1007/s11814-022-1364-0

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