SOH ESTIMATION METHOD OF LITHIUM-ION BATTERIES BASED ON DOD-LN-GPR MODEL

Huang Jiayin; Bai Junqi; Xian Yanhua

doi:10.19912/j.0254-0096.tynxb.2023-1683

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 60-69. DOI: 10.19912/j.0254-0096.tynxb.2023-1683

SOH ESTIMATION METHOD OF LITHIUM-ION BATTERIES BASED ON DOD-LN-GPR MODEL

Huang Jiayin, Bai Junqi, Xian Yanhua

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Abstract

To address the issues of low prediction accuracy, redundant health feature inputs, and cumbersome data preprocessing in the estimation of the State of Health （SOH） of Lithium-ion batteries, an improved SOH prediction model of Gaussian process regression based on depth of discharge （DOD） is proposed. In the discharge curve of the lithium battery, the discharge depth of the lithium-ion battery is calculated and taken as the only health feature. At the same time, the traditional Gaussian process regression （GPR） algorithm is improved, and the combined kernel function （LIN+NN） of linear （LIN） and neural network （NN） is used to fit the trend of global decline and local fluctuation of lithium-ion battery capacity, and the DOD-LN-GPR lithium-ion battery SOH estimation model is established. In the NASA data set, the experimental comparison of different kernel functions is carried out to verify the superiority of the prediction accuracy of the proposed combined kernel functions. Then, by reducing the ratio of training set to test set, it is proved that the proposed estimation method can still have good prediction effect on a small number of training samples. Finally, the proposed DOD-LN-GPR model is compared with other SOH estimation models under different training sets, and the results show that the model has better accuracy and robustness.

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lithium-ion batteries / state estimation / battery management systems / Gaussian process regression / depth of discharge

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Huang Jiayin, Bai Junqi, Xian Yanhua. SOH ESTIMATION METHOD OF LITHIUM-ION BATTERIES BASED ON DOD-LN-GPR MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 60-69 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1683

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