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Electrochemical impedance characteristics at various conditions for commercial solid–liquid electrolyte lithium-ion batteries: Part. 2. Modeling and prediction

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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 battery management algorithms for SLELBs, it is essential to have an accurate understanding of battery behavior under different influencing factors and to build a high-fidelity battery simulation model. Electrochemical impedance spectroscopy (EIS) can be used to study the electrode process dynamics and ion transport mechanism in lithium-ion batteries. It is an urgent 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. 2. Modeling and prediction," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221033405
    DOI: 10.1016/j.energy.2021.123091
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    References listed on IDEAS

    as
    1. 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.
    2. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. E, Jiaqiang & Zeng, Yan & Jin, Yu & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2020. "Heat dissipation investigation of the power lithium-ion battery module based on orthogonal experiment design and fuzzy grey relation analysis," Energy, Elsevier, vol. 211(C).
    4. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    5. Xu, Li-Wen & Yang, Fang-Qin & Abula, Aji’erguli & Qin, Shuang, 2013. "A parametric bootstrap approach for two-way ANOVA in presence of possible interactions with unequal variances," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 172-180.
    6. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian & Liao, Sheng-li, 2017. "Hydropower system operation optimization by discrete differential dynamic programming based on orthogonal experiment design," Energy, Elsevier, vol. 126(C), pages 720-732.
    7. Wang, Yujie & Li, Mince & Chen, Zonghai, 2020. "Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation," Applied Energy, Elsevier, vol. 278(C).
    8. Xi, Huan & Zhang, Honghu & He, Ya-Ling & Huang, Zuohua, 2019. "Sensitivity analysis of operation parameters on the system performance of organic rankine cycle system using orthogonal experiment," Energy, Elsevier, vol. 172(C), pages 435-442.
    9. 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.
    10. 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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    2. Tian, Jiaqiang & Liu, Xinghua & Li, Siqi & Wei, Zhongbao & Zhang, Xu & Xiao, Gaoxi & Wang, Peng, 2023. "Lithium-ion battery health estimation with real-world data for electric vehicles," Energy, Elsevier, vol. 270(C).
    3. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    4. Lai, Xin & Zhou, Long & Zhu, Zhiwei & Zheng, Yuejiu & Sun, Tao & Shen, Kai, 2023. "Experimental investigation on the characteristics of coulombic efficiency of lithium-ion batteries considering different influencing factors," Energy, Elsevier, vol. 274(C).
    5. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
    6. 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).
    7. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).

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