IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3233-d1426837.html
   My bibliography  Save this article

Determination of Sodium Ion Diffusion Coefficient in Tin Sulfide@Carbon Anode Material Using GITT and EIS Techniques

Author

Listed:
  • Andrzej P. Nowak

    (Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
    Advanced Materials Center, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

  • Paweł Rutecki

    (Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

  • Mariusz Szkoda

    (Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
    Advanced Materials Center, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

  • Konrad Trzciński

    (Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
    Advanced Materials Center, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

Abstract

The electroanalytical behavior of SnS x (x = 1, 2) encapsulated into a carbon phase was studied using the galvanostatic intermittent titration technique (GITT) and electrochemical impedance spectroscopy (EIS). These techniques are widely utilized in battery systems to investigate the diffusion of alkali metal cations in anode and cathode materials depending on the concentration of ions in the host material. Here, we report different calculation methods showing how the applied model affects the derived diffusion coefficient. The calculated value of the apparent chemical diffusion coefficient of sodium ions ( D N a + ) is in the range of 1 × 10 −10 to 1 × 10 −15 cm 2 /s depending on the technique, mathematical protocol, geometry of the electrode material, and applied potential.

Suggested Citation

  • Andrzej P. Nowak & Paweł Rutecki & Mariusz Szkoda & Konrad Trzciński, 2024. "Determination of Sodium Ion Diffusion Coefficient in Tin Sulfide@Carbon Anode Material Using GITT and EIS Techniques," Energies, MDPI, vol. 17(13), pages 1-11, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3233-:d:1426837
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3233/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/13/3233/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    2. Lin, Mingqiang & Wu, Denggao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "Health prognosis for lithium-ion battery with multi-feature optimization," Energy, Elsevier, vol. 264(C).
    3. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    4. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    5. Li, Yong & Wang, Liye & Feng, Yanbiao & Liao, Chenglin & Yang, Jue, 2024. "An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework," Energy, Elsevier, vol. 298(C).
    6. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
    7. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    8. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    9. Ding, Xiaofeng & Zhang, Donghuai & Cheng, Jiawei & Wang, Binbin & Luk, Patrick Chi Kwong, 2019. "An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles," Applied Energy, Elsevier, vol. 254(C).
    10. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
    11. Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
    12. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    13. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    14. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    15. Wang, Tianyu & Ma, Zhongjing & Zou, Suli & Chen, Zhan & Wang, Peng, 2024. "Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels," Applied Energy, Elsevier, vol. 355(C).
    16. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    17. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    18. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    19. Seyedamin Valedsaravi & Abdelali El Aroudi & Luis Martínez-Salamero, 2022. "Review of Solid-State Transformer Applications on Electric Vehicle DC Ultra-Fast Charging Station," Energies, MDPI, vol. 15(15), pages 1-35, August.
    20. Tang, Aihua & Wu, Xinyu & Xu, Tingting & Hu, Yuanzhi & Long, Shengwen & Yu, Quanqing, 2024. "State of health estimation based on inconsistent evolution for lithium-ion battery module," Energy, Elsevier, vol. 286(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3233-:d:1426837. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.