Lithium Battery State of Health Estimation Method Based on Variational Mode Decomposition and Feature Enhancement

doi:10.19799/j.cnki.2095-4239.2025.0033

Abstract

Abstract:

Because of its high energy density and long life cycle,lithium batteries have been widely used in electric vehicles, energy storage systems and portable devices. However, capacity regeneration is inevitable during the use of lithium batteries, which will affect the assessment accuracy of battery health. In order to reduce the influence of capacity regeneration on battery health assessment accuracy,a new method combining variational mode decomposition and generative adversarial network was proposed.Firstly, the variational mode decomposition was used to decompose the iso-pressure drop discharge time characteristic,which reflects the capacity regeneration phenomenon in the discharge process of lithium battery, and the main degradation trend component and local fluctuation component were separated according to Pearson correlation coefficient analysis,so as to better characterize the health state of the battery.The main degradation trend component reflects the overall degradation trend of the battery. The local fluctuation component reflects the local variation characteristics caused by capacity regeneration. Secondly, in order to further enhance the relevant characteristics of local wave components and capacity regeneration phenomenon,Fourier transform is carried out to extract the middle and low frequency components reflecting the capacity regeneration phenomenon.For the components of these frequency bands, the generated adversarial network is used to generate data, and the generated data is combined with the multi-feature after variational mode decomposition to construct a new multi-feature set. Then, support vector machine algorithm is used to train the new multi-feature set to achieve accurate estimation of the health state of the lithium battery.Finally, validation experiments are conducted based on NASA data sets and CALCE data sets. The experimental results show that compared with traditional methods, the RMS error of the proposed method can be controlled within 4.5%, which can effectively reduce the influence of capacity regeneration on the battery health assessment accuracy.

Key words: Capacity regeneration, Local fluctuation component, Fourier Transform, Support Vector Machine

CLC Number:

TM912

Wuzhe ZHANG, Zhiduan CAI, Chengao WU, Wei ZHENG, Jiayang TONG. Lithium Battery State of Health Estimation Method Based on Variational Mode Decomposition and Feature Enhancement[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2025.0033.

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