Remaining life prediction of lithium-ion battery based on VMD-PSO-GRU model

doi:10.19799/j.cnki.2095-4239.2022.0491

Abstract

Abstract:

Accurately predicting the remaining useful life (RUL) of lithium batteries plays a significant role in reducing battery risk and ensuring the safe operation of the system. To reduce the influence of battery capacity regeneration, and improve the accuracy and stability of RUL prediction, an integrated prediction model of variational modal decomposition and gate recurrent unit (GRU) network with particle swarm optimization algorithm was proposed. First, the capacity sequence of the lithium battery was decomposed into a series of stationary components using the variational modal decomposition algorithm. Then, the multiple GRU network was used to predict the capacity component on each sub-sequence. To deal with inconsistent prediction results, particle swarm optimization was used to optimize the parameters of the GRU model before model training. Finally, the predicted results of each sub-sequences were integrated as the final battery capacity estimation, followed by the prediction of RUL. The experimental results on the National Aeronautics and Space Administration dataset showed that the maximum mean absolute percentage error and root mean square error of the results were controlled within 0.88% and 0.0148, respectively, and the maximum error of RUL prediction was less than two charging cycles. It has high robustness and prediction accuracy.

Key words: lithium battery, remaining life prediction, variational modal decomposition, particle swarm optimization, gated recurrent neural network

CLC Number:

TM 912

Qiantong LIU, Yuanxiu XING. Remaining life prediction of lithium-ion battery based on VMD-PSO-GRU model[J]. Energy Storage Science and Technology, 2023, 12(1): 236-246.

Figures/Tables 13

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Prediction results of different layers of GRU neural network"

$m$	$n$	MAE	RMSE	RA
1	15	0.0114	0.0145	0.9911
	20	0.0103	0.0123	0.9919
	25	0.0101	0.0119	0.9923
2	15	0.0116	0.0181	0.9915
	20	0.0087	0.0112	0.9933
	25	0.0085	0.0113	0.9934
3	15	0.0198	0.0216	0.9854
	20	0.0168	0.0211	0.9866
	25	0.0160	0.0208	0.9876

Table 1

Fig. 5

Fig. 6

Table 2

Fig. 7

Table 3

Fig. 8

Table 4

Table 5

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电池型号	MAE	RMSE	MAPE/%	RA	RUL_e
B0005	0.0118	0.0148	0.83	0.9914	2
B0006	0.0091	0.0121	0.71	0.9934	1
B0007	0.0130	0.0137	0.88	0.9914	2
B0018	0.0082	0.0098	0.58	0.9942	1

电池型号	MAE	RMSE	MAPE/%	RA	RUL_e
B0005	0.0150	0.0177	1.07	0.9895	2
B0006	0.0150	0.0170	1.13	0.9891	3
B0007	0.0166	0.0192	1.11	0.9893	3
B0018	0.0151	0.0167	1.06	0.9895	2

电池型号	模型	MAE	RMSE	MAPE/%	RA	RUL_e
B0005	GRU	0.0670	0.0707	4.76	0.9512	7
	EMD-GRU	0.0281	0.0346	1.96	0.9792	4
	VMD-GRU	0.0143	0.0189	0.99	0.9893	3
	VMD-PSO-GRU	0.0118	0.0148	0.83	0.9914	2
B0006	GRU	0.0555	0.0620	4.11	0.9574	6
	EMD-GRU	0.0353	0.0433	2.61	0.9728	4
	VMD-GRU	0.0199	0.0215	1.55	0.9854	3
	VMD-PSO-GRU	0.0091	0.0121	0.71	0.9934	1
B0007	GRU	0.0470	0.0539	3.09	0.9680	5
	EMD-GRU	0.0302	0.0356	1.99	0.9794	4
	VMD-GRU	0.0211	0.0189	2.58	0.9891	3
	VMD-PSO-GRU	0.0130	0.0137	0.88	0.9914	2
B0018	GRU	0.0706	0.0805	4.96	0.9494	7
	EMD-GRU	0.0327	0.0381	2.30	0.9766	4
	VMD-GRU	0.0232	0.0242	1.65	0.9836	3
	VMD-PSO-GRU	0.0082	0.0098	0.58	0.9942	1

电池型号	模型	MAE	RMSE	MAPE	RUL_e
B0006	VMD-LSTM^[22]	0.0127	0.0312	0.0094	—
	ABMS-CEEMDAN-LSTM^[21]	0.0240	0.0308	—	3
	AAFSA-ELM^[10]	0.0157	0.0231	—	2
	VMD-PSO-GRU	0.0091	0.0121	0.0071	1

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