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Energies 2018, 11(9), 2323; https://doi.org/10.3390/en11092323

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

1
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
2
Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48104, USA
3
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
*
Authors to whom correspondence should be addressed.
Received: 8 August 2018 / Revised: 28 August 2018 / Accepted: 28 August 2018 / Published: 3 September 2018
(This article belongs to the Section Energy Storage and Application)
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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 (dQ/dV), 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 LiFePO4 and LiNixCoyMnzO2 cathodes, making it promising to be further applied in online SOH estimation algorithms. View Full-Text
Keywords: lithium-ion batteries; state-of-health; incremental capacity analysis; support vector regression; curve fitting; energy storage lithium-ion batteries; state-of-health; incremental capacity analysis; support vector regression; curve fitting; energy storage
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Feng, X.; Weng, C.; He, X.; Wang, L.; Ren, D.; Lu, L.; Han, X.; Ouyang, M. Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study. Energies 2018, 11, 2323.

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