Estimation of the state of health of lithium-ion batteries based on feature extraction of the relaxation process

doi:10.19799/j.cnki.2095-4239.2023.0510

Abstract

Abstract:

Lithium-ion battery is an important part of the fixed electrochemical energy storage, and the estimation of its state of health (SOH) is of great significance for its safe and stable operation. At present, health-feature extraction is focused on the charging stage of a battery, and few methods have been developed to extract health features at the relaxation stage. This study proposes a method to extract health features from the relaxation stage and use Gaussian process regression (GPR) to estimate the SOH. First, based on the accelerated cycle aging test data of Li-NMC batteries, the variation of the time constant at the relaxation stage is analyzed, and a power function, which reflects the terminal voltage in the relaxation stage well, is used for modeling. Second, the key features that can characterize the relaxation stage are extracted, and the SOH estimation model is established based on GPR. Finally, the accuracy was verified using batteries with different aging current multiplicities. The results are compared when the model is generated using 15- and 60-minute relaxation-stage curves. The accuracy of the Gaussian-process regression method is also compared with support vector machine and tree regression method, and the accuracy of SOH estimation was verified under multiple states of charge. The proposed SOH estimation model was validated to achieve an optimal root mean square error (RMSE) of 0.6%; the RMSE is still less than 1% when using 15-minute data for SOH estimation.

Key words: lithium-ion battery, SOH estimation, relaxation stage, gaussian process regression, feature extraction

CLC Number:

TM 912

Chen GENG, Jinhao MENG, Qiao PENG, Tianqi LIU, Xueyang ZENG, Gang CHEN. Estimation of the state of health of lithium-ion batteries based on feature extraction of the relaxation process[J]. Energy Storage Science and Technology, 2023, 12(11): 3479-3487.

Figures/Tables 13

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电池名称	充电电流
Cell1和Cell2	1 A
Cell3和Cell4	2 A
Cell5和Cell6	4 A

算法	回归树方法		SVM回归方法		GPR回归方法
数据长度	60 min	15 min	60 min	15 min	60 min	15 min
RMSE	0.0190	0.0246	0.0154	0.0127	0.0064	0.009
R²	0.91	0.85	0.94	0.96	0.99	0.98
MAE	0.0119	0.0132	0.0105	0.0080	0.0046	0.0057

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