Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition

doi:10.19799/j.cnki.2095-4239.2023.0298

Abstract

Abstract:

Accurately predicting the remaining useful life (RUL) of lithium batteries can ensure timely understanding of the internal performance degradation of the battery, reduce the risks associated with battery use, and provide a reliable theoretical basis for routine maintenance. To improve the accuracy and stability of prediction results, a lithium-battery RUL prediction model based on the combination of ensemble empirical mode decomposition (EEMD) and gated recurrent unit (GRU) with multiple linear regression (MLR) is proposed. First, the model decomposes the lithium-battery capacity data into several high-frequency and low-frequency components using the EEMD algorithm to reduce noise interference in the capacity data. Then, based on the characteristics of each component, the model builds prediction submodels based on the obtained high-frequency and low-frequency sequences using the GRU and MLR networks, respectively. Finally, the predicted values of each submodel are superimposed and fused to obtain the RUL of the battery based on the public data on lithium batteries provided by NASA and Oxford; furthermore, using different prediction starting points, the obtained results are compared with those of other single and combined models. The experimental results show that the EEMD-GRU-MLR prediction model can provide accurate RUL results, compared with LSTM, GRU, and EEMD-GRU prediction models, with the maximum mean absolute error decreased by 0.0311, 0.0234, and 0.0182, respectively, and the maximum root mean square error decreased by 0.0235, 0.0153, and 0.0098, respectively, This proves the satisfactory ability of the proposed EEMD-GRU-MLR model to predict the RUL of lithium batteries.

Key words: lithium battery, remaining useful life, ensemble empirical mode decomposition, gated recurrent unit network, multiple linear regression

CLC Number:

TM 912

Minghu WU, Chengpeng YUE, Fan ZHANG, Junxiao LI, Wei HUANG, Sheng HU, Jing TANG. Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition[J]. Energy Storage Science and Technology, 2023, 12(7): 2220-2228.

Figures/Tables 11

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实验方法	模型名称
M1	LSTM
M2	GRU
M3	EEMD-GRU
M4	EEMD-GRU-MLR

电池编号	预测方法	RUL预测值	AE	MAE	RMSE
B0005	M1	138	13	0.0402	0.0428
	M2	133	8	0.0325	0.0346
	M3	131	6	0.0273	0.0291
	M4	124	1	0.0091	0.0149
B0006	M1	114	5	0.0342	0.0395
	M2	113	4	0.0255	0.0307
	M3	112	3	0.0180	0.0245
	M4	109	0	0.0079	0.0193
B0007	M1	—	—	0.0361	0.0388
	M2	—	—	0.0277	0.0300
	M3	167	7	0.0103	0.0145
	M4	158	2	0.0075	0.0142

电池编号	预测起点	RUL预测值	AE	MAE	RMSE
B0005	60	118	7	0.0128	0.0178
	80	124	1	0.0091	0.0149
	100	124	1	0.0059	0.0099
B0006	60	108	1	0.0122	0.0200
	80	109	0	0.0079	0.0193
	100	109	0	0.0070	0.0118
B0007	60	156	4	0.0106	0.0158
	80	158	2	0.0075	0.0142
	100	159	1	0.0048	0.0080

电池编号	预测方法	RUL预测值	AE	MAE	RMSE
Cell1	M1	69	6	0.0054	0.0058
	M2	66	3	0.0034	0.0036
	M3	64	1	0.0018	0.0022
	M4	62	1	0.0013	0.0014
Cell2	M1	62	5	0.0059	0.0065
	M2	60	3	0.0045	0.0052
	M3	59	2	0.0034	0.0043
	M4	56	1	0.0018	0.0033

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