Gaussian process regression based on indirect health indicators for SOH estimation of lithium battery

doi:10.19799/j.cnki.2095-4239.2022.0611

Abstract

Abstract:

The performance of a lithium battery degrades gradually with increasing use time. If the replacement is not completed on time, serious accidents such as explosions may occur. Rapid and accurate prediction of battery state of health (SOH) is critical for lithium battery system management, maintenance, and safe use. In this paper, a machine learning model based on indirect His (health indicators) and GPR (Gaussian process regression) is proposed to predict the SOH of lithium batteries. First, the analysis of the lithium battery discharge process extracts some easily available and suitable for the direct external features of dynamic operations as indirect His, and their correlation with SOH, eventually selecting average discharge voltage, such as pressure drop discharge time, maximum discharge temperature, and platform stage discharge voltage initial plummet in value as the health index. Second, using the above mentioned His as input features, the GPR algorithm is used to establish a lithium battery degradation model, and the MAE (mean absolute error) is less than 2% for the prediction of NASA lithium battery datasets, while the RMSE is kept within 4%. Finally, the model is compared to other commonly used machine learning models, and then into multiple experimental conditions of battery model generalization performance analysis, control about 6% of the maximum error of the prediction. The experimental results show that the proposed indirect His and GPR have relatively higher prediction precision and good generalization ability.

Key words: health indicators, state of health, Gaussian process regression, support vector regression

CLC Number:

TM 912

Ruijie WANG, Zhouli HUI, Ming YANG. Gaussian process regression based on indirect health indicators for SOH estimation of lithium battery[J]. Energy Storage Science and Technology, 2023, 12(2): 560-569.

Figures/Tables 10

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References 24

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HIs	B0005	B0006	B0007	B0018
最高放电温度	-0.9353	-0.8504	-0.7495	-0.6952
平均放电电压	0.9824	0.9652	0.9611	0.9856
放电电压初始骤降值	-0.9607	-0.9160	-0.7439	-0.8853
等压降放电时间	0.9934	0.9928	0.9939	0.9949
最终放电温度	0.5863	0.9348	-0.0218	0.4962
最低电压	-0.4871	-0.4897	-0.3147	0.1176

电池编号	本工作所提健康指标	文献[21]	文献[22]	文献[23]
B0005	0.0072	0.0153	0.1210	0.0290
B0006	0.0196	0.0290	0.2251	0.0452
B0007	0.0210	—	0.2070	0.0259
B0018	0.0186	0.0289	—	0.0342

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