IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipds0360544222028584.html
   My bibliography  Save this article

Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy

Author

Listed:
  • He, Rong
  • He, Yongling
  • Xie, Wenlong
  • Guo, Bin
  • Yang, Shichun

Abstract

As a non-destructive method for characterizing battery dynamic behavior, electrochemical impedance spectroscopy (EIS) is becoming increasingly important to analyze electrode process kinetics, electric double layers, and diffusion in the study of battery performance. However, overlapping features of the EIS semicircle of commercial batteries over the lifetime bring ambiguities concerning their physicochemical significance spectra in the timescale distribution. In this work, we analyze the impedance of kinetic processes and corresponding time constants in three types of commercial batteries at different aging stages via the optimized distribution of relaxation time (DRT) investigation which extracts evolution details of different time scales that could not be distinguished. Firstly, informative aging tests for battery LiNi0·8Co0·1Mn0·1O2 (NCM), LixFePO4 (LFP), and LiNixCoyAlzO2 (NCA) during the whole life cycle were designed to simulate different electric vehicles working conditions, followed by periodical EIS measurements. And then, the capacity retention, as well as the deconvolution of EIS for discriminating the electrochemical mechanisms of commercial LiBs during aging were compared. Gaussian process and ridge regression are dedicated to complex superposed impedance spectra to DRT. Combining both experimental measurements and multi-peak analysis for DRT, the analysis determined the impedance distribution characteristics of batteries with different materials during cycling, which facilitates rapid identification of aging mechanisms and further life prediction. Therefore, the peak characteristic analysis method based on the DRT analysis can be employed to diagnose and predict the impact of battery performance.

Suggested Citation

  • He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222028584
    DOI: 10.1016/j.energy.2022.125972
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222028584
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125972?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Angeles Cabañero, Maria & Altmann, Johannes & Gold, Lukas & Boaretto, Nicola & Müller, Jana & Hein, Simon & Zausch, Jochen & Kallo, Josef & Latz, Arnulf, 2019. "Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries," Energy, Elsevier, vol. 171(C), pages 1217-1228.
    2. Soo-Bin Han & Hwanyeong Oh & Won-Yong Lee & Jinyeon Won & Suyong Chae & Jongbok Baek, 2021. "On-Line EIS Measurement for High-Power Fuel Cell Systems Using Simulink Real-Time," Energies, MDPI, vol. 14(19), pages 1-14, September.
    3. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    4. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    5. Ecker, Madeleine & Shafiei Sabet, Pouyan & Sauer, Dirk Uwe, 2017. "Influence of operational condition on lithium plating for commercial lithium-ion batteries – Electrochemical experiments and post-mortem-analysis," Applied Energy, Elsevier, vol. 206(C), pages 934-946.
    6. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    7. Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).
    9. Kriegler, Johannes & Hille, Lucas & Stock, Sandro & Kraft, Ludwig & Hagemeister, Jan & Habedank, Jan Bernd & Jossen, Andreas & Zaeh, Michael F., 2021. "Enhanced performance and lifetime of lithium-ion batteries by laser structuring of graphite anodes," Applied Energy, Elsevier, vol. 303(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).

    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. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    2. Angeles Cabañero, Maria & Altmann, Johannes & Gold, Lukas & Boaretto, Nicola & Müller, Jana & Hein, Simon & Zausch, Jochen & Kallo, Josef & Latz, Arnulf, 2019. "Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries," Energy, Elsevier, vol. 171(C), pages 1217-1228.
    3. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    6. David Marcos & Maitane Garmendia & Jon Crego & José Antonio Cortajarena, 2021. "Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries," Energies, MDPI, vol. 14(21), pages 1-19, October.
    7. Yang, Shuxia & Wang, Xiongfei & Xu, Jiayu & Tang, Mingrun & Chen, Guang, 2023. "Distribution network adaptability assessment considering distributed PV “reverse power flow” behavior - a case study in Beijing," Energy, Elsevier, vol. 275(C).
    8. Kriegler, Johannes & Hille, Lucas & Stock, Sandro & Kraft, Ludwig & Hagemeister, Jan & Habedank, Jan Bernd & Jossen, Andreas & Zaeh, Michael F., 2021. "Enhanced performance and lifetime of lithium-ion batteries by laser structuring of graphite anodes," Applied Energy, Elsevier, vol. 303(C).
    9. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    10. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    11. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    12. Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    13. Gao, Yizhao & Sun, Ziqiang & Zhang, Dong & Shi, Dapai & Zhang, Xi, 2023. "Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries," Applied Energy, Elsevier, vol. 349(C).
    14. Nithin Somasundaran & Nessa Fereshteh Saniee & Truong Quang Dinh & James Marco, 2023. "Study on the Extensibility of Voltage-Plateau-Based Lithium Plating Detection for Electric Vehicles," Energies, MDPI, vol. 16(6), pages 1-15, March.
    15. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    16. Li, Yalun & Gao, Xinlei & Feng, Xuning & Ren, Dongsheng & Li, Yan & Hou, Junxian & Wu, Yu & Du, Jiuyu & Lu, Languang & Ouyang, Minggao, 2022. "Battery eruption triggered by plated lithium on an anode during thermal runaway after fast charging," Energy, Elsevier, vol. 239(PB).
    17. 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).
    18. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    19. Liang, Jialin & Gan, Yunhua & Tan, Meixian & Li, Yong, 2020. "Multilayer electrochemical-thermal coupled modeling of unbalanced discharging in a serially connected lithium-ion battery module," Energy, Elsevier, vol. 209(C).
    20. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.

    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:eee:energy:v:263:y:2023:i:pd:s0360544222028584. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.