Prediction of remaining useful life for lithium-ion battery using recurrence plot analysis

doi:10.19799/j.cnki.2095-4239.2024.0398

Abstract

Abstract:

During the long-term cycling of lithium-ion batteries (LIBs), performance degradation is inevitable, which directly impacts the stable operation of the battery system. To address this, this paper proposes a novel for predicting the remaining useful life of LIBs based on multi-scale features derived from recurrence plots. This approach aims to overcome the limitations of extracting key degradation features from one-dimensional (1D) signals, such as voltage data. By leveraging the rich spatiotemporal degradation features captured in recurrence plots, we first develop a deep learning architecture for effective multi-scale feature extraction. This architecture detects temporal variations within the same voltage region across multiple cycles and examines the spatial evolution of recurrence plots between adjacent voltage regions using variable-sized receptive fields. This approach enables the extraction of deep multi-scale features, which are then mapped to RUL modeling. Comprehensive evaluation experiments were performed to systematically validate the predictive effectiveness of the proposed method. The results suggest that by using a limited number of charge process recurrence plots as input, the model achieves rapid convergence and accurate predictions. Additionally, in cross-rate prediction scenarios, the proposed method improves performance metrics, with absolute error (MAE) and root mean squared error (RMSE) reduced by approximately 7-fold and 5.7-fold, respectively, at a 2C rate compared to shallow indicators. Finally, comparative experiments with 1D sequence inputs further validate the effectiveness of predicting the RUL of LIBs using multiscale features of recurrence plots. This approach achieves performance improvements of approximately 50% and 43% in various evaluation metrics, while requiring relatively minimal time-series voltage sampling data for imaging.

Key words: lithium-ion batteries, remaining useful life prediction, recurrence plot, multi-scale feature extraction

CLC Number:

TM 912

Yuguang XIE, Jinzhong LI, Wenhao ZOU, Lei MAO. Prediction of remaining useful life for lithium-ion battery using recurrence plot analysis[J]. Energy Storage Science and Technology, 2024, 13(10): 3630-3641.

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层名称	具体参数及输出尺寸
输入层	(time_steps, 224, 224)
卷积层1	卷积核-11×11，通道-48，输出-(48, 55, 55)
最大池化层1	卷积核-3×3，步长-2，输出-(48, 27, 27)
卷积层2	卷积核-5×5，通道-128，输出-(128, 27, 27)
最大池化层2	卷积核-3×3，步长-2，输出-(128, 13, 13)
卷积层3	卷积核-3×3，通道-192，输出-(192, 13, 13)
卷积层4	卷积核-3×3，通道-192，输出-(192, 13, 13)
卷积层5	卷积核-3×3，通道-128，输出-(128, 13, 13)
最大池化层3	卷积核-3×3，步长-2，输出-(128, 6, 6)
全连接层	(128×6×6, 1024)
全连接层	(1024, 1024)
全连接层	(1024, 1)

超参数项目	参数设置情况
输入图像尺寸	(batch_counts, time_steps, 224, 224)
学习率	10^-4
丢弃率	0.3
批量大小	48
优化器	AdamW
最大训练次数	200

名称	循环数
	1		2		3		4
	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
Cell-2-1	5.43	6.88	5.83	8.29	5.40	10.67	6.44	10.90
Cell-2-2	5.59	7.48	4.13	5.34	4.72	5.64	4.39	5.56
Cell-2-3	3.89	5.36	3.62	4.94	3.94	4.83	4.73	5.80
Cell-2-4	4.08	5.16	5.38	5.82	3.45	4.45	5.15	6.19
Cell-2-5	9.36	11.56	6.23	7.94	5.45	6.95	8.10	9.32
Cell-2-6	4.70	6.11	4.48	5.68	4.73	6.41	5.39	7.93
Cell-2-7	8.78	11.21	10.13	13.14	9.39	11.90	10.29	12.10
Cell-2-8	8.39	11.44	7.58	10.21	6.99	9.31	8.92	10.62

平均值	6.28	8.15	5.92	7.67	5.51	7.52	6.68	8.55

工况	深层多尺度特征		浅层特征
工况	MAE	RMSE	MAE	RMSE
1.5C	6.28	8.62	22.51	24.28
2C	7.27	9.19	72.15	77.75
平均值	6.78	8.91	47.33	51.02

名称	所提方法		DWM-Trans		1D-MSCNN
名称	MAE	RMSE	MAE	RMSE	MAE	RMSE
Cell-2-1	5.40	10.67	6.90	9.67	12.02	15.40
Cell-2-2	4.72	5.64	5.59	6.51	9.39	11.50
Cell-2-3	3.94	4.83	3.85	4.71	6.54	7.83
Cell-2-4	3.45	4.45	3.39	4.54	6.15	7.34
Cell-2-5	5.45	6.95	4.12	4.80	10.90	12.36
Cell-2-6	4.73	6.41	4.21	5.35	6.96	8.16
Cell-2-7	9.39	11.90	13.17	16.13	20.39	25.04
Cell-2-8	6.99	9.31	12.58	15.16	15.16	18.56

平均值	5.51	7.52	6.73	8.36	10.94	13.27