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Electrochemical impedance characteristics at various conditions for commercial solid–liquid electrolyte lithium-ion batteries: Part 1. experiment investigation and regression analysis

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  • Feng, Fei
  • Yang, Rui
  • Meng, Jinhao
  • Xie, Yi
  • Zhang, Zhiguo
  • Chai, Yi
  • Mou, Lisha

Abstract

Solid–liquid electrolyte lithium-ion batteries (SLELBs) have good commercial viability in electric vehicle applications because they combine the safety of solid electrolyte lithium-ion batteries with the high ionic conductivity of liquid electrolyte lithium-ion batteries (LELBs). The safe and efficient operation of electric vehicles is inseparable from the key battery management algorithms such as battery state of charge (SOC), state of health and state of power estimation. In the process of designing the battery management algorithms for SLELBs, it is essential to have an accurate understanding of battery behavior under different influencing factors and build a high-fidelity battery model. Electrochemical impedance spectroscopy (EIS) can be used to study the electrode process dynamics and ion transport mechanism in lithium-ion batteries. However, it can be a huge challenge to use EIS to experiment and analyze the characteristic impedances of SLELBs under the full-scale factors, and to construct the battery model and simulate the battery impedance under the premise of a reasonable number of tests.

Suggested Citation

  • Feng, Fei & Yang, Rui & Meng, Jinhao & Xie, Yi & Zhang, Zhiguo & Chai, Yi & Mou, Lisha, 2022. "Electrochemical impedance characteristics at various conditions for commercial solid–liquid electrolyte lithium-ion batteries: Part 1. experiment investigation and regression analysis," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221031297
    DOI: 10.1016/j.energy.2021.122880
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    References listed on IDEAS

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    1. Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    2. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    3. 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.
    4. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
    5. Fei Feng & Rengui Lu & Chunbo Zhu, 2014. "A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide Ambient Temperature Range," Energies, MDPI, vol. 7(5), pages 1-29, May.
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    Cited by:

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    4. Kang, Jihyeon & Atwair, Mohamed & Nam, Inho & Lee, Chul-Jin, 2023. "Experimental and numerical investigation on effects of thickness of NCM622 cathode in Li-ion batteries for high energy and power density," Energy, Elsevier, vol. 263(PE).

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