IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1486-d113132.html
   My bibliography  Save this article

Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance

Author

Listed:
  • Cunxue Wu

    (College of Automotive Engineering, Chongqing University, Chongqing 40044, China
    China Chang’an Automotive Engineering Institute, Chongqing 401120, China)

  • Rujian Fu

    (A123 Systems, LLC., Livonia, MI 48377, USA)

  • Zhongming Xu

    (College of Automotive Engineering, Chongqing University, Chongqing 40044, China)

  • Yang Chen

    (A123 Systems, LLC., Livonia, MI 48377, USA)

Abstract

For high power Li-ion batteries, an important approach to improve the accuracy of modeling and algorithm development is to consider the current dependence of internal resistance, especially for large current applications in mild/median hybrid electric vehicles (MHEV). For the first time, the work has experimentally captured the decrease of internal resistance at an increasing current of up to the C-rate of 25 and developed an equivalent circuit model (ECM) with current dependent parameters. The model is integrated to extended Kalman filter (EKF) to improve SOC estimation, which is validated by experimental data collected in dynamic stress testing (DST). Results show that EKF with current dependent parameters is capable of estimating SOC with a higher accuracy when it is compared to EKF without current dependent parameters.

Suggested Citation

  • Cunxue Wu & Rujian Fu & Zhongming Xu & Yang Chen, 2017. "Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance," Energies, MDPI, vol. 10(10), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1486-:d:113132
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marongiu, Andrea & Nußbaum, Felix Gerd Wilhelm & Waag, Wladislaw & Garmendia, Maitane & Sauer, Dirk Uwe, 2016. "Comprehensive study of the influence of aging on the hysteresis behavior of a lithium iron phosphate cathode-based lithium ion battery – An experimental investigation of the hysteresis," Applied Energy, Elsevier, vol. 171(C), pages 629-645.
    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. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    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. 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.

    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. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    2. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    3. 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.
    4. Qinghe Liu & Shouzhi Liu & Haiwei Liu & Hao Qi & Conggan Ma & Lijun Zhao, 2019. "Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter," Energies, MDPI, vol. 12(11), pages 1-13, May.
    5. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    6. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    7. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    8. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    9. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    10. 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).
    11. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    12. Duong, Van-Huan & Bastawrous, Hany Ayad & See, Khay Wai, 2017. "Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation," Applied Energy, Elsevier, vol. 204(C), pages 560-571.
    13. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    14. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    15. 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.
    16. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    17. 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.
    18. Yun Bao & Yuansheng Chen, 2021. "Lithium-Ion Battery Real-Time Diagnosis with Direct Current Impedance Spectroscopy," Energies, MDPI, vol. 14(15), pages 1-16, July.
    19. Rejaul Islam & S M Sajjad Hossain Rafin & Osama A. Mohammed, 2022. "Comprehensive Review of Power Electronic Converters in Electric Vehicle Applications," Forecasting, MDPI, vol. 5(1), pages 1-59, December.
    20. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.

    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:10:y:2017:i:10:p:1486-:d:113132. 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.