IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i6p444-d71749.html
   My bibliography  Save this article

A Parameter Identification Method for Dynamics of Lithium Iron Phosphate Batteries Based on Step-Change Current Curves and Constant Current Curves

Author

Listed:
  • Zhichao He

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Geng Yang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Languang Lu

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

Abstract

Parameterization of battery dynamics based on terminal operating data is a main concern in engineering applications of batteries. The key technology is designing an adequate test procedure and a data processing procedure to excite different inner dynamics and then estimate the parameters of a corresponding equivalent circuit model (ECM). This paper proposes a parameter identification method that utilizes the terminal voltage curves (TVC) under step-change current conditions and constant current conditions. With this method, I - V characteristics of battery’s Ohmic resistance, mass diffusion process, thermal process and SOC varying process are decoupled and parametric functions of an ECM are obtained. Experimental results show that the method is easy to be implemented and modeling accuracy is sufficient for applications.

Suggested Citation

  • Zhichao He & Geng Yang & Languang Lu, 2016. "A Parameter Identification Method for Dynamics of Lithium Iron Phosphate Batteries Based on Step-Change Current Curves and Constant Current Curves," Energies, MDPI, vol. 9(6), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:6:p:444-:d:71749
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/6/444/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/6/444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daehyun Kim & Keunhwi Koo & Jae Jin Jeong & Taedong Goh & Sang Woo Kim, 2013. "Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model," Energies, MDPI, vol. 6(10), pages 1-14, October.
    2. Shifei Yuan & Hongjie Wu & Chengliang Yin, 2013. "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model," Energies, MDPI, vol. 6(1), pages 1-27, January.
    3. Fei Feng & Rengui Lu & Chunbo Zhu, 2014. "A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide Ambient Temperature Range," Energies, MDPI, vol. 7(5), pages 1-29, May.
    4. Jaeshin Yi & Boram Koo & Chee Burm Shin, 2014. "Three-Dimensional Modeling of the Thermal Behavior of a Lithium-Ion Battery Module for Hybrid Electric Vehicle Applications," Energies, MDPI, vol. 7(11), pages 1-16, November.
    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. Yingjie Chen & Geng Yang & Xu Liu & Zhichao He, 2019. "A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries," Energies, MDPI, vol. 12(9), pages 1-20, May.
    2. Qingxia Yang & Jun Xu & Binggang Cao & Xiuqing Li, 2017. "A simplified fractional order impedance model and parameter identification method for lithium-ion batteries," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.

    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. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    2. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    3. 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).
    4. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    5. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    6. Hossam M. Hussein & Ahmed Aghmadi & Mahmoud S. Abdelrahman & S M Sajjad Hossain Rafin & Osama Mohammed, 2024. "A review of battery state of charge estimation and management systems: Models and future prospective," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    7. Qiao Zhu & Neng Xiong & Ming-Liang Yang & Rui-Sen Huang & Guang-Di Hu, 2017. "State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H ? Method," Energies, MDPI, vol. 10(5), pages 1-19, May.
    8. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    9. Wenhui Zheng & Bizhong Xia & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer," Energies, MDPI, vol. 12(13), pages 1-14, June.
    10. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    11. 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.
    12. Van-Thanh Ho & Kyoungsik Chang & Sang Wook Lee & Sung Han Kim, 2020. "Transient Thermal Analysis of a Li-Ion Battery Module for Electric Cars Based on Various Cooling Fan Arrangements," Energies, MDPI, vol. 13(9), pages 1-15, May.
    13. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    14. Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    15. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    16. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    17. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.
    18. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    19. Xiaobin Hong & Nianzhi Li & Jinheng Feng & Qingzhao Kong & Guixiong Liu, 2015. "Multi-Electrode Resistivity Probe for Investigation of Local Temperature Inside Metal Shell Battery Cells via Resistivity: Experiments and Evaluation of Electrical Resistance Tomography," Energies, MDPI, vol. 8(2), pages 1-23, January.
    20. Yuechen Liu & Linjing Zhang & Jiuchun Jiang & Shaoyuan Wei & Sijia Liu & Weige Zhang, 2017. "A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries," Energies, MDPI, vol. 10(5), pages 1-15, April.

    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:9:y:2016:i:6:p:444-:d:71749. 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.