State of Health Estimation for Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

doi:10.12096/j.2096-4528.pgt.260112

Abstract

Abstract:

Objectives Lithium-ion batteries, as key components in electric vehicles and energy storage systems, require accurate prediction of their state of health (SOH) to ensure system reliability, extend battery life, and optimize energy management. However, in actual operation, the performance of batteries deteriorates due to multiple factors. Therefore, a high-precision SOH prediction method based on electrochemical impedance spectroscopy (EIS) data is proposed. Methods Experiments are conducted using EIS data, and the linear Kramers-Kronig algorithm is employed to preprocess the EIS data. A convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM) network, and an attention-based combination model are adopted as prediction models. The real part, imaginary part, and magnitude of the impedance from the EIS data are used as inputs. CNN is utilized to extract important features, which are then integrated with the BiLSTM network model to predict the SOH. Additionally, the Attention mechanism and Dropout algorithm are applied to optimize the model. Results The prediction of SOH of lithium-ion batteries at different temperatures and the comparison with the CNN-BiLSTM and BiLSTM models show that the CNN-BiLSTM-Attention model has better SOH prediction effects at 25, 35, and 45 ℃, with accuracy improvements of 46.1% and 77.9% respectively compared to the two models. Conclusions SOH prediction based on EIS data is feasible. The CNN-BiLSTM-Attention combination model is able to achieve accurate and efficient prediction of the SOH of lithium-ion batteries, demonstrating strong practicality.

Key words: new energy, electric vehicle, lithium-ion batteries, state of health, electrochemical impedance spectroscopy, convolutional neural network, bi-directional long short-term memory, prediction model

CLC Number:

TK 02

Hongsi SHI, Xinwei SUN, Kai WANG. State of Health Estimation for Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy[J]. Power Generation Technology, 2026, 47(1): 133-144.

Figures/Tables 10

References 33

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温度/℃	电池	RMSE	MAE	R²
25	25C01	0.009 2	0.007 6	0.991 7
	25C02	0.036 7	0.028 8	0.943 8
	25C03	0.011 9	0.018 2	0.981 9
	25C04	0.027 7	0.018 6	0.969 5
35	35C02	0.036 8	0.039 7	0.917 6
45	45C02	0.011 1	0.008 6	0.998 1

温度/℃	电池	RMSE	MAE	R²
25	25C01	0.009 2	0.007 6	0.991 7
	25C02	0.036 7	0.028 8	0.943 8
	25C03	0.011 9	0.018 2	0.981 9
	25C04	0.027 7	0.018 6	0.969 5
35	35C02	0.036 8	0.039 7	0.917 6
45	45C02	0.011 1	0.008 6	0.998 1

模型	电池	RMSE	MAE	R²
BiLSTM	25C01	0.074 1	0.035 9	0.857 6
	25C02	0.092 9	0.081 1	0.776 9
	25C03	0.080 8	0.026 3	0.735 2
	25C04	0.080 5	0.040 1	0.718 1
	35C02	0.094 2	0.064 4	0.790 4
	45C02	0.182 1	0.151 4	0.512 9
CNN-BiLSTM	25C01	0.022 8	0.015 7	0.957 3
	25C02	0.043 2	0.035 8	0.951 6
	25C03	0.021 5	0.015 1	0.967 0
	25C04	0.065 2	0.033 0	0.826 4
	35C02	0.056 0	0.040 9	0.925 7
	45C02	0.039 0	0.031 2	0.957 6
CNN-BiLSTM-Attention	25C01	0.009 2	0.007 6	0.991 7
	25C02	0.036 7	0.028 8	0.965 1
	25C03	0.011 9	0.008 2	0.989 9
	25C04	0.027 7	0.018 7	0.969 5
	35C02	0.036 8	0.029 7	0.967 8
	45C02	0.011 1	0.008 6	0.998 1

模型	电池	RMSE	MAE	R²
BiLSTM	25C01	0.074 1	0.035 9	0.857 6
	25C02	0.092 9	0.081 1	0.776 9
	25C03	0.080 8	0.026 3	0.735 2
	25C04	0.080 5	0.040 1	0.718 1
	35C02	0.094 2	0.064 4	0.790 4
	45C02	0.182 1	0.151 4	0.512 9
CNN-BiLSTM	25C01	0.022 8	0.015 7	0.957 3
	25C02	0.043 2	0.035 8	0.951 6
	25C03	0.021 5	0.015 1	0.967 0
	25C04	0.065 2	0.033 0	0.826 4
	35C02	0.056 0	0.040 9	0.925 7
	45C02	0.039 0	0.031 2	0.957 6
CNN-BiLSTM-Attention	25C01	0.009 2	0.007 6	0.991 7
	25C02	0.036 7	0.028 8	0.965 1
	25C03	0.011 9	0.008 2	0.989 9
	25C04	0.027 7	0.018 7	0.969 5
	35C02	0.036 8	0.029 7	0.967 8
	45C02	0.011 1	0.008 6	0.998 1

电池	训练集比例/%	RMSE	MAE	R²
35C01	50	0.036 8	0.031 8	0.841 1
	60	0.021 5	0.018 9	0.914 8
	80	0.007 8	0.006 9	0.960 4
45C01	50	0.026 8	0.019 6	0.949 9
	60	0.018 5	0.013 6	0.965 6
	80	0.006 6	0.005 4	0.983 4