IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i3p769-d491238.html
   My bibliography  Save this article

Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

Author

Listed:
  • Ji’ang Zhang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Ping Wang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Yushu Liu

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Ze Cheng

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

In the battery management system, it is important to accurately and efficiently estimate the state of charge (SOC) of lithium-ion batteries, which generally requires the establishment of a equivalent circuit model of the battery, whose accuracy and rationality play an important role in accurately estimating the state of lithium-ion batteries. The traditional single order equivalent circuit models do not take into account the changes of impedance spectrum under the action of multiple factors, nor do they take into account the balance of practicality and complexity of the model, resulting the low accuracy and poor practicability. In this paper, the theory of electrochemical impedance spectroscopy is used to guide and improve the equivalent circuit model. Based on the analysis of the variation of the high and intermediate frequency range of the impedance spectrum with the state of charge and temperature of the battery, a variable order equivalent model (VOEM) is proposed by Arrhenius equation and Bayesian information criterion (BIC), and the state equation and observation equation of VOEM are improved by autoregressive (AR) equations. Combined with the unscented Kalman filter (UKF), a SOC online estimation method is proposed, named VOEM-AR-UKF. The experimental results show that the proposed method has high accuracy and good adaptability.

Suggested Citation

  • Ji’ang Zhang & Ping Wang & Yushu Liu & Ze Cheng, 2021. "Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 14(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:769-:d:491238
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/3/769/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/3/769/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    3. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    4. Jianlin Wang & Le Zhang & Dan Xu & Peng Zhang & Gairu Zhang, 2019. "A Simplified Fractional Order Equivalent Circuit Model and Adaptive Online Parameter Identification Method for Lithium-Ion Batteries," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, April.
    5. Hu, Minghui & Li, Yunxiao & Li, Shuxian & Fu, Chunyun & Qin, Datong & Li, Zonghua, 2018. "Lithium-ion battery modeling and parameter identification based on fractional theory," Energy, Elsevier, vol. 165(PB), pages 153-163.
    6. Eun Ryung Lee & Hohsuk Noh & Byeong U. Park, 2014. "Model Selection via Bayesian Information Criterion for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 216-229, March.
    7. 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.
    8. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    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. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    2. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    3. Zhao, Xu & Chen, Yongan & Chen, Luowen & Chen, Ning & Wang, Hao & Huang, Wei & Chen, Jiayao, 2023. "On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer," Applied Energy, Elsevier, vol. 351(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. Hao Sun & Bo Jiang & Heze You & Bojian Yang & Xueyuan Wang & Xuezhe Wei & Haifeng Dai, 2021. "Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions," Energies, MDPI, vol. 14(2), pages 1-19, January.
    2. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    3. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    4. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    5. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    6. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    7. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    8. He, Xitian & Sun, Bingxiang & Zhang, Weige & Fan, Xinyuan & Su, Xiaojia & Ruan, Haijun, 2022. "Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery," Energy, Elsevier, vol. 244(PB).
    9. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    10. Zhang, Yuan Ci & Briat, Olivier & Boulon, Loïc & Deletage, Jean-Yves & Martin, Cyril & Coccetti, Fabio & Vinassa, Jean-Michel, 2019. "Non-isothermal Ragone plots of Li-ion cells from datasheet and galvanostatic discharge tests," Applied Energy, Elsevier, vol. 247(C), pages 703-715.
    11. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2018. "Successive-approximation algorithm for estimating capacity of Li-ion batteries," Energy, Elsevier, vol. 159(C), pages 61-73.
    12. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    13. Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
    14. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    15. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    16. Edoardo Locorotondo & Fabio Corti & Luca Pugi & Lorenzo Berzi & Alberto Reatti & Giovanni Lutzemberger, 2021. "Design of a Wireless Charging System for Online Battery Spectroscopy," Energies, MDPI, vol. 14(1), pages 1-17, January.
    17. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    18. Shida Jiang & Zhengxiang Song, 2021. "Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance," Energies, MDPI, vol. 14(16), pages 1-20, August.
    19. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    20. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.

    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:14:y:2021:i:3:p:769-:d:491238. 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.