基于VF-DW-DFN的锂离子电池剩余寿命预测

doi:10.19799/j.cnki.2095-4239.2021.0665

摘要/Abstract

摘要：

锂离子电池作为各类储能系统与设备的重要组成部分，准确预测锂离子电池的剩余使用寿命对于保障电池相关产业和设施的可靠性与安全性起着关键作用。针对锂离子电池剩余寿命预测中存在的非平稳、非线性特性导致单一数据驱动方法的预测精度低、泛化性能差等问题，提出了一种基于变分滤波、数据规整和深度融合网络的数据驱动融合(VF-DW-DFN)方法。首先，利用变分滤波法去除原始电池退化序列中的随机噪声干扰，得到相对平稳的退化特征数据。然后，采用最优嵌入法构造预测滑窗，实现特征数据规整，减少信息损失。其次，设计了一种新型深度融合网络对电池非线性退化数据进行建模，辨识电池数据中的退化模式，实现最终的锂离子电池剩余寿命预测。最后，在钴酸锂锂离子电池数据集上进行了剩余寿命预测实验，实验预测的平均均方根误差为1.41%，平均剩余寿命绝对误差小于2个循环周期。实验结果表明所提出的方法泛化性能好，预测精度高，误差小，能够对锂离子电池的退化过程进行有效建模和准确预测。

关键词: 剩余寿命预测, 锂离子电池, 变分滤波(VF)法, 数据规整(DW), 深度融合网络(DFN)

Abstract:

Lithium-ion battery is an important part of different energy storage systems and equipment. An accurate forecast of lithium-ion battery remaining usable life is critical for guaranteeing the dependability and safety of battery-related enterprises and facilities. In this study, a new data-driven method is proposed to enhance the low forecasting accuracy and poor generalization performance in the remaining useful life prediction of lithium-ion batteries. Poor performance is frequently the result of a single data-driven strategy and is caused by non-stationary, nonlinear dynamics in battery deterioration dynamics. The proposed method is based on variational filtering, data wrapping, and a deep fusion network. First, the random noise interference of the original battery degradation sequence is eliminated to obtain the relatively stable degradation characteristic data by using the variational filtering method. Then, using the optimum embedding approach to accomplish feature data wrapping and limit information loss, an unique deep fusion network is constructed to model non-linear battery deterioration data, detect the degradation pattern in the battery data, and realize the final remaining usable life prediction. Lastly, remaining useful life prediction experiments are conducted on the lithium cobalt oxide battery data set and the average root means a square error of prediction is 1.41%, and the average absolute error of remaining useful life of prediction is less than two cycles. The suggested technique provides strong generalization, high forecasting accuracy, and minimal prediction error and can successfully anticipate the deterioration process of lithium-ion batteries, according to experimental data.

Key words: remaining useful life prediction, lithium-ion battery, variational filtering, data wrapping, deep fusion network

中图分类号:

TM 911.3

易顺民, 谢林柏, 彭力. 基于VF-DW-DFN的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2022, 11(7): 2305-2315.

Shunmin YI, Linbo XIE, Li PENG. Remaining useful life prediction of lithium-ion batteries based on VF-DW-DFN[J]. Energy Storage Science and Technology, 2022, 11(7): 2305-2315.

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电池编号	EOM/Ah	T_EOM	EOL/Ah	T_EOL
B5	1.6	74	1.4	125
B6	1.6	62	1.4	109
B7	1.6	93	1.42	160

网络类型	网络参数	参数值
DFN	输入单元数	d
	卷积核个数	32/32
	卷积核大小	5/5
	卷积核步长	1/1
	循环隐藏单元数	64/64/64
	自回归阶次	5
	输出单元数	1
	学习率	0.001

电池编号	RUL_r	RUL_p	RMSE	MAE	AE
B5	51	52	0.0136	0.0101	1
B6	47	48	0.0203	0.0117	1
B7	67	69	0.0085	0.0063	2

电池编号	滑窗大小	RMSE	MAE	AE
B5	15	0.0136	0.0101	1
	5	0.0213	0.0187	5
	30	0.0340	0.0314	11
B6	13	0.0203	0.0117	1
	5	0.0265	0.0193	3
	30	0.0468	0.0413	9
B7	15	0.0085	0.0063	2
	5	0.0330	0.0313	—
	30	0.0432	0.0418	—

电池编号	方法	RMSE	MAE	AE
B5	VF-DW-DFN	0.0136	0.0101	1
	VF-DW-SVM	0.0350	0.0331	10
	VF-DW-LSTM	0.0341	0.0305	10
B6	VF-DW-DFN	0.0203	0.0117	1
	VF-DW-SVM	0.0612	0.0555	17
	VF-DW-LSTM	0.0607	0.0564	18
B7	VF-DW-DFN	0.0085	0.0063	2
	VF-DW-SVM	0.0194	0.0177	—
	VF-DW-LSTM	0.0262	0.0247	—