Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation

doi:10.19799/j.cnki.2095-4239.2021.0623

Abstract

Abstract:

The State of Health (SOH) of lithium batteries is a key parameter to characterize the actual useful life. SOH is not directly measurable, and a combined CNN-LSTM and GRU estimation method based on health feature parameters is proposed to further improve the accuracy of SOH estimation. Firstly, the health feature parameters are initially selected from the Li-ion battery charging curve, and the health features are extracted by Spearman correlation coefficient. Secondly, Convolutional Neural Network (CNN) is used to extract local features of health features and Long Short-Term Memory (LSTM) to mine data time series features to construct a CNN-LSTM fusion neural network. Subsequently, the CNN-LSTM and the Gated Recurrent Unit (GRU) are combined to form a combined SOH estimation model by adaptive weighting factors. Finally, the validation is based on the NASA lithium battery dataset 5, 6, 7, and 18 battery parameters. The experimental results show that the estimation accuracy of the proposed combined model is better than that of the single model, and the estimation error is further reduced.

Key words: lithium-ion battery, state of health, health feature, CNN-LSTM, GRU

CLC Number:

TM 911

Yanwen DAI, Aiqing YU. Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation[J]. Energy Storage Science and Technology, 2022, 11(5): 1641-1649.

Figures/Tables 12

Fig. 1

Fig. 2

Table 1

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Table 2

SOH estimation results for the four methods"

型号	预测起点	SOH	CNN-LSTM&GRU			CNN-LSTM			LSTM			GRU
型号	预测起点	SOH	E_SOH	E_rr	$E E r r$ /%	E_SOH	E_rr	$E E r r$ /%	E_SOH	E_rr	$E E r r$ /%	E_SOH	E_rr	$E E r r$ /%
B5	80	31	31	0	0	28	3	9.7	26	5	16.1	26	5	16.1
B6	80	19	18	1	5.2	16	3	15.7	14	5	26.3	15	4	21.1
B7	80	66	67	1	1.5	64	2	3.0	62	4	6.1	62	4	6.1
B18	80	32	33	1	3.1	29	3	9.4	24	8	25.0	27	5	15.6

Table 2

Table 3

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型号	预测起点	RUL	CNN-LSTM&GRU		CNN-LSTM		LSTM		GRU
型号	预测起点	RUL	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
B5	80	31	0.0096	0.0152	0.0124	0.0273	0.0341	0.0957	0.0255	0.0755
B6	80	19	0.0332	0.0390	0.0418	0.0624	0.0502	0.0566	0.0752	0.1038
B7	80	66	0.0162	0.0212	0.0426	0.0468	0.0681	0.0850	0.0470	0.0569
B18	80	32	0.0260	0.0322	0.0387	0.0742	0.1038	0.1122	0.0753	0.0900

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