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.
Highlights
The market of electric vehicles is growing at an unprecedented rate in recent years, driven by the motivations to find cleaner energy powertrain systems for future transportations [1,2,3]
This paper studies the performances of the support vector regression (SVR) algorithm on the Incremental capacity analysis (ICA) for commercial lithium-ion batteries
The possible problem of overfitting and under-fitting can be solved by choosing proper standard deviation σ in the Gaussian kernel in the SVR algorithm
Summary
The market of electric vehicles is growing at an unprecedented rate in recent years, driven by the motivations to find cleaner energy powertrain systems for future transportations [1,2,3]. Incremental capacity analysis (ICA) is a method that is being pervasively used in battery SOH diagnosis in recent years, after its first proposal by Balewski & Brenet [18] and propagated by Dubarry et al [19,20]. The other researchers may find it difficult to learn the usage of the SVR method, because they may encounter problems such as: (1) how to reduce the computational time of the SVR method; (2) how to judge the accuracy of the curve fitting by the SVR method; (3) does the SVR method fit for lithium-ion cells with other chemistries other than lithium phosphate etc Those problems hindered a wider application of the SVR method in processing ICA.
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