Remaining useful life prediction of lithium-ion batteries based on SVD-SAE-GPR

doi:10.19799/j.cnki.2095-4239.2022.0767

Abstract

Abstract:

Lithium-ion batteries are an important energy storage sources, and it is of great practical importance to predict their remaining useful life (RUL). First, the battery data are treated as matrices, and singular value decomposition (SVD) is introduced to extract the potential health indicator (HI) from the measured data and the feature extraction objects containing more degradation information. This will address the drawbacks of traditional feature extraction methods that rely on parameter settings and poor adaptability to different lithium-ion battery datasets. Second, the redundancy and deficiency of potential HIs affect the prediction of RUL, and thus, a fused HI is obtained by processing HIs using Spearman correlation analysis and stacked autoencoder, considering the shortcomings of principal component analysis (PCA). Accordingly, a model between fused HI and capacity is constructed using the Gaussian process regression algorithm, and the final prediction results with uncertainty expression are obtained. Finally, the feasibility and validity of the proposed prediction model are verified by four aging batteries provided by NASA. The MIT battery dataset is used to verify the adaptability of the feature extraction method. The experimental results show that the proposed RUL prediction framework has good prediction performance and that the SVD feature extraction method has good adaptability while avoiding parameter settings. The HI extracted in this paper has significantly improved the prediction accuracy compared with the HI after PCA fusion and other HIs.

Key words: lithium-ion batteries, remaining useful life, singular value decomposition, stacked autoencoder, gaussian process regression

CLC Number:

TP 206

Yuanchang DONG, Xiaoqiong PANG, Jianfang JIA, Yuanhao SHI, Jie WEN, Xiao LI, Xin ZHANG. Remaining useful life prediction of lithium-ion batteries based on SVD-SAE-GPR[J]. Energy Storage Science and Technology, 2023, 12(4): 1257-1267.

Figures/Tables 16

Fig. 1

Fig. 2

Table 1

Spearman correlation analysis of 15 potential HIs and capacity of four batteries"

特征提取对象	HIs	Spearman相关系数
特征提取对象	HIs	B0005	B0006	B0007	B0018
放电电压	HI1	0.9943	0.9982	0.9953	0.9876
放电电流	HI2	0.9873	0.9986	0.9949	0.9888
放电温度	HI3	0.8118	0.6690	0.8864	0.9688
放电负载电压	HI4	0.9953	0.9986	0.9945	0.9917
放电负载电流	HI5	0.9870	0.9987	0.9945	0.9892
放电数据测量时间	HI6	0.9972	0.9976	0.9951	0.9929
充电电压	HI7	-0.3348	-0.3289	-0.3518	0.9707
充电电流	HI8	0.9908	0.9908	0.9912	0.9734
充电温度	HI9	-0.2286	-0.2668	-0.2123	0.9567
充电负载电压	HI10	-0.3144	-0.3237	-0.5934	0.9634
充电负载电流	HI11	0.9908	0.9907	0.9913	0.9734
充电数据测量时间	HI12	-0.3560	-0.3241	-0.3674	0.9349
$d Q / d V$	HI13	0.9975	0.9990	0.9959	0.9949
$d V / d Q$	HI14	-0.5057	-0.7387	-0.3497	0.6355
$d T / d V$	HI15	0.9833	0.9871	0.9730	0.9929

Table 1

Fig. 3

Table 2

Fig. 4

Fig. 5

Fig. 6

Table 3

Prediction performance of four batteries at different prediction starting points"

评价指标	预测起始点	B0005	B0006	B0007	B0018
$R M S E$	60	0.0103	0.0301	0.0198	0.0128
$R M S E$	80	0.0062	0.0267	0.0070	0.0066
$R 2$	60	0.9918	0.9369	0.9501	0.9519
$R 2$	80	0.9946	0.9400	0.9893	0.9572
$A E$	60	2	4	—	6
$A E$	80	0	2	—	1

Table 3

Fig. 7

Table 4

Spearman correlation analysis between capacity and potential HIs of CH2 and CH5"

特征提取对象	HIs	Spearman相关系数
特征提取对象	HIs	CH2	CH5
时间	HI1	0.9353	0.9868
充电容量	HI2	0.9984	0.9986
电流	HI3	0.9983	0.9978
电压	HI4	0.9405	0.9835
温度	HI5	0.9980	0.9880
$d Q / d V$	HI6	0.9984	0.9998
$d V / d Q$	HI7	-0.9973	-0.9922
$d T / d V$	HI8	-0.2448	0.1870

Table 4

Table 5

Fig. 8

Table 6

Predicted performance of CH2 and CH5"

评价指标	CH2	CH5
$R M S E$	0.0072	0.0031
$R 2$	0.9505	0.9471

Table 6

Table 7

Table 8

Predicted performance of different models (M1, M2 and M3)"

评价指标	模型	B0005	B0006	B0007	B0018
$R M S E$	M1	0.0062	0.0267	0.0070	0.0066
	M2	0.0176	0.0325	0.0113	0.0195
	M3	0.0270	0.0395	0.0138	0.0075
$R 2$	M1	0.9946	0.9400	0.9893	0.9572
	M2	0.9567	0.8959	0.9719	0.9314
	M3	0.8984	0.8469	0.9583	0.9457
$A E$	M1	0	2	—	1
	M2	2	3	—	3
	M3	10	4	—	2

Table 8

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