基于SVD-SAE-GPR的锂离子电池RUL预测

doi:10.19799/j.cnki.2095-4239.2022.0767

摘要/Abstract

摘要：

锂离子电池是重要的储能手段之一，对其剩余使用寿命(RUL)进行预测具有非常重要的实际意义。本工作首先针对传统特征提取方法依赖参数设置且对于不同锂离子电池数据集适应性差的缺陷，将电池数据视作矩阵，并引入奇异值分解(SVD)从测量数据和包含更多退化信息的特征提取对象中提取潜在健康因子(HIs)。其次，潜在HIs的冗余和不足会影响RUL的预测，同时考虑到主成分分析(PCA)的缺点，使用Spearman相关分析和堆叠自编码器(SAE)处理HIs得到一个融合HI。在此基础上，利用高斯过程回归(GPR)算法构建了融合HI与容量之间的模型，得到了带有不确定性表达的最终预测结果。最后，通过NASA提供的四个老化电池数据验证了所提预测模型的可行性和有效性。并额外选取MIT电池数据集验证特征提取方法的适应性。实验结果表明，所提出的RUL预测框架具有较好的预测性能，SVD特征提取方法在避免参数设置的前提下具有较好的适应性。本工作提取的HI与经过PCA融合的HI、其他HI相比，预测精度有显著提高。

关键词: 锂离子电池, 剩余使用寿命(RUL), 奇异值分解, 堆叠自编码器, 高斯过程回归

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

中图分类号:

TP 206

董渊昌, 庞晓琼, 贾建芳, 史元浩, 温杰, 李笑, 张鑫. 基于SVD-SAE-GPR的锂离子电池RUL预测[J]. 储能科学与技术, 2023, 12(4): 1257-1267.

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.

图/表 16

图1

图2

表1

四个电池15个潜在HIs与容量的Spearman相关分析"

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

表1

图3

表2

图4

图5

图6

表3

四个电池在不同预测起始点的预测性能"

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

表3

图7

表4

CH2和CH5容量与潜在HIs的Spearman相关分析"

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

表4

表5

图8

表6

CH2和CH5的预测性能"

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

表6

表7

表8

不同模型(M1、M2和M3)的预测性能"

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

表8

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