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Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study

Author

Listed:
  • Xuning Feng

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Caihao Weng

    (Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48104, USA)

  • Xiangming He

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Li Wang

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Dongsheng Ren

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

  • Languang Lu

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

  • Xuebing Han

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

  • Minggao Ouyang

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

Abstract

Incremental capacity analysis (ICA) has been used pervasively to characterize the degradation mechanisms of the lithium-ion batteries, and several online state-of-health estimation algorithms are built based on ICA. However, the stairs and the noises in the discrete sampled voltage data obstruct the calculation of the capacity differentiation over voltage (d Q /d V ), therefore we need methods to fit the sampled voltage first. In this paper, the support vector regression (SVR) algorithm is used to smooth the sampled voltage curve using Gaussian kernels. A parametric study has been conducted to show how to enhance the performances of the SVR algorithm, including (1) speeding up the algorithm by downsampling; (2) avoiding overfitting and under-fitting using proper standard deviation σ in the Gaussian kernel; (3) making precise capture of the characteristic peaks. A novel method using linear approximation has been proposed to help judge the accuracy of the SVR algorithm in tracking the ICA peaks. And advanced SVR algorithms using double σ and using cost function that directly regulates the differentiation result have been proposed. The advanced SVR algorithm can make accurate curve fitting for ICA with overall error less than 1% (maximum 3%) throughout cycle lives, for four kinds of commercial lithium-ion batteries with LiFePO 4 and LiNi x Co y Mn z O 2 cathodes, making it promising to be further applied in online SOH estimation algorithms.

Suggested Citation

  • Xuning Feng & Caihao Weng & Xiangming He & Li Wang & Dongsheng Ren & Languang Lu & Xuebing Han & Minggao Ouyang, 2018. "Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study," Energies, MDPI, vol. 11(9), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2323-:d:167521
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    References listed on IDEAS

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    3. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).

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