Data on Machine Learning regenerated Lithium-ion battery impedance

基于机器学习的数据重新计算了锂离子电池阻抗

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Abstract

This paper describes and provides the data on the regenerated-impedance spectra that is computed from experimental results of electrochemical impedance spectroscopy measurements taken from a commercial Li-ion battery. The empirical impedance data of secondary coin type Li-ion batteries were collected in different states of charge ranging from empty to full state of charge configurations. This approach utilizes only a small seed (ex grano) experimental data set to first build an ensemble of weighted disparate models selected based on performance and non-correlative criteria ("co-modelling") then second to generate what would be the remaining experimental data synthetically. The "Cooperative Model Framework" demonstrates the efficacy of this approach by assessing the synthetically generated data.

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