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

Xuning Feng, Caihao Weng, Xiangming He, Li Wang, Dongsheng Ren, Languang Lu, Xuebing Han and Minggao Ouyang
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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

Energies, 2018, vol. 11, issue 9, 1-21

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.

Keywords: lithium-ion batteries; state-of-health; incremental capacity analysis; support vector regression; curve fitting; energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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