IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2825-d792621.html
   My bibliography  Save this article

Peukert Generalized Equations Applicability with Due Consideration of Internal Resistance of Automotive-Grade Lithium-Ion Batteries for Their Capacity Evaluation

Author

Listed:
  • Nataliya N. Yazvinskaya

    (Department of Cybersecurity of Information Systems, Don State Technical University, 344000 Rostov-on-Don, Russia)

  • Mikhail S. Lipkin

    (Department of Chemical Technologies, Platov South-Russian State Polytechnic University, 346428 Novocherkassk, Russia)

  • Nikolay E. Galushkin

    (Laboratory of Electrochemical and Hydrogen Energy, Don State Technical University, 346500 Shakhty, Russia)

  • Dmitriy N. Galushkin

    (Laboratory of Electrochemical and Hydrogen Energy, Don State Technical University, 346500 Shakhty, Russia)

Abstract

In this paper, the applicability of the Peukert equation and its generalizations were investigated for capacity evaluation of automotive-grade lithium-ion batteries. It is proved that the classical Peukert equation is applicable within the range of the discharge currents from 0.2 C n to 2 C n ( C n is the nominal battery capacity). As a rule, the operating currents of many automotive-grade lithium-ion batteries are exactly within this range of the discharge currents. That is why, successfully, the classical Peukert equation is used in many analytical models developed for these batteries. The generalized Peukert equation C = C m /(1 + ( i / i 0) n ) is applicable within the discharge currents range from zero to approximately 10C n . All kinds of operating discharge currents (including both very small ones and powerful short-term bursts) fall into this discharge currents range. The modified Peukert equation C = C m (1 − i / i 1)/((1 − i / i 1) + ( i / i 0) n ) is applicable at any discharge currents. This equation takes into account the battery’s internal resistance and has the smallest error of experimental data approximation. That is why the discussed modified Peukert equation is most preferable for use in analytical models of automotive-grade lithium-ion batteries. The paper shows that all the parameters of the generalized Peukert equations have a clear electrochemical meaning in contrast to the classical Peukert equation, where all the parameters are just empirical constants.

Suggested Citation

  • Nataliya N. Yazvinskaya & Mikhail S. Lipkin & Nikolay E. Galushkin & Dmitriy N. Galushkin, 2022. "Peukert Generalized Equations Applicability with Due Consideration of Internal Resistance of Automotive-Grade Lithium-Ion Batteries for Their Capacity Evaluation," Energies, MDPI, vol. 15(8), pages 1-11, April.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2825-:d:792621
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2825/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/8/2825/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zubi, Ghassan & Dufo-López, Rodolfo & Carvalho, Monica & Pasaoglu, Guzay, 2018. "The lithium-ion battery: State of the art and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 292-308.
    2. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    3. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    4. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    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. Fabian Steger & Jonathan Krogh & Lasantha Meegahapola & Hans-Georg Schweiger, 2022. "Calculating Available Charge and Energy of Lithium-Ion Cells Based on OCV and Internal Resistance," Energies, MDPI, vol. 15(21), pages 1-23, 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. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Xu Lei & Xi Zhao & Guiping Wang & Weiyu Liu, 2019. "A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power," Energies, MDPI, vol. 12(19), pages 1-24, September.
    4. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    5. Noshin Omar & Peter Van den Bossche & Thierry Coosemans & Joeri Van Mierlo, 2013. "Peukert Revisited—Critical Appraisal and Need for Modification for Lithium-Ion Batteries," Energies, MDPI, vol. 6(11), pages 1-17, October.
    6. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    7. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    8. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    9. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. 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.
    11. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    12. Zhao, Xiaowei & Cai, Yishan & Yang, Lin & Deng, Zhongwei & Qiang, Jiaxi, 2017. "State of charge estimation based on a new dual-polarization-resistance model for electric vehicles," Energy, Elsevier, vol. 135(C), pages 40-52.
    13. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    14. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    15. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.
    16. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    17. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    18. Saeed Mian Qaisar, 2020. "Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge," Energies, MDPI, vol. 13(21), pages 1-20, October.
    19. Ren, Zhijun & Li, Huajie & Yan, Wenyi & Lv, Weiguang & Zhang, Guangming & Lv, Longyi & Sun, Li & Sun, Zhi & Gao, Wenfang, 2023. "Comprehensive evaluation on production and recycling of lithium-ion batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    20. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.

    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:15:y:2022:i:8:p:2825-:d:792621. 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.