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Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study

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  • Song, Ziyou
  • Hofmann, Heath
  • Lin, Xinfan
  • Han, Xuebing
  • Hou, Jun

Abstract

This paper presents an experimental study on the parameter identification of a battery pack, which determines the relationship between identification accuracy and measurement data. Parameter identification of the lithium-ion battery is poor when the input-output data, i.e., the input current and output voltage, is not appropriate. In addition, selection/optimization of an appropriate data set for estimation needs to adapt to different applications. A first-order equivalent circuit model is adopted to model a battery pack, and the identification accuracy is analyzed for both the single-parameter and multi-parameter identification scenarios. It is found that the accuracy of different identification scenarios is influenced by the voltage noise, the current amplitude, and the current frequency. Three experiments using sine waves with different frequencies are then performed to characterize a lithium-ion battery pack. Experimental results show that the current profile with the optimal frequency content achieves the best identification performance. Therefore, it is validated that the identification accuracy can be improved when the current excitation satisfies certain criteria.

Suggested Citation

  • Song, Ziyou & Hofmann, Heath & Lin, Xinfan & Han, Xuebing & Hou, Jun, 2018. "Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study," Applied Energy, Elsevier, vol. 231(C), pages 1307-1318.
    • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:1307-1318
    DOI: 10.1016/j.apenergy.2018.09.126
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    Cited by:

    1. 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.
    2. Peng Guo & Xiaobo Wu & António M. Lopes & Anyu Cheng & Yang Xu & Liping Chen, 2022. "Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method," Mathematics, MDPI, vol. 10(17), pages 1-11, August.
    3. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    4. Lai, Qingzhi & Ahn, Hyoung Jun & Kim, YoungJin & Kim, You Na & Lin, Xinfan, 2021. "New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery," Applied Energy, Elsevier, vol. 295(C).
    5. Ahmed Fathy & Dalia Yousri & Abdullah G. Alharbi & Mohammad Ali Abdelkareem, 2023. "A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters," Sustainability, MDPI, vol. 15(7), pages 1-22, March.
    6. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    7. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.

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