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A noise-tolerant model parameterization method for lithium-ion battery management system

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  • Wei, Zhongbao
  • Zhao, Difan
  • He, Hongwen
  • Cao, Wanke
  • Dong, Guangzhong

Abstract

A well-parameterized battery model is prerequisite of the model-based estimation and control of lithium-ion battery (LIB). However, the unexpected yet inevitable noises may markedly discount the identification of model parameters in real applications. This paper focuses on the noise-immune and unbiased model parameter identification for LIB. The signal-disturbance interface in LIB model identification is firstly analyzed by reformulating an overdetermined nonlinear system, on the premise of a cautiously-designed instrumental vector estimator. The multi-variable identification is then solved in the framework of a separable nonlinear least squares (SNLS) problem via a novel two-step method combining least squares (LS) and variable projection algorithm (VPA), to co-estimate the noise variances and unbiased model parameters. A numerical solver is further exploited for the proposed LSVPA, giving rise to a recursive and computational efficient algorithmic architecture which is favorable for online applications. The proposed method is validated with both simulations and experiments in terms of the noise tolerance and the parameterization accuracy.

Suggested Citation

  • Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s030626192030444x
    DOI: 10.1016/j.apenergy.2020.114932
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    Cited by:

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    6. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.
    7. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    8. Han, Lijin & Yang, Ke & Ma, Tian & Yang, Ningkang & Liu, Hui & Guo, Lingxiong, 2022. "Battery life constrained real-time energy management strategy for hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 259(C).
    9. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
    10. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    11. Mikel Oyarbide & Mikel Arrinda & Denis Sánchez & Haritz Macicior & Paul McGahan & Erik Hoedemaekers & Iosu Cendoya, 2020. "Capacity and Impedance Estimation by Analysing and Modeling in Real Time Incremental Capacity Curves," Energies, MDPI, vol. 13(18), pages 1-18, September.
    12. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    13. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    14. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios," Energy, Elsevier, vol. 263(PB).
    15. Radana Kahankova & Martina Mikolasova & Radek Martinek, 2022. "Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-23, April.
    16. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    17. Luca Lavagna & Giuseppina Meligrana & Claudio Gerbaldi & Alberto Tagliaferro & Mattia Bartoli, 2020. "Graphene and Lithium-Based Battery Electrodes: A Review of Recent Literature," Energies, MDPI, vol. 13(18), pages 1-28, September.
    18. Hesham Alhumade & Ahmed Fathy & Abdulrahim Al-Zahrani & Muhyaddin Jamal Rawa & Hegazy Rezk, 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization," Mathematics, MDPI, vol. 9(9), pages 1-19, May.
    19. Hemmati, S. & Doshi, N. & Hanover, D. & Morgan, C. & Shahbakhti, M., 2021. "Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle," Applied Energy, Elsevier, vol. 283(C).
    20. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    21. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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