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Prediction of SOH and RUL for Lithium-ion Batteries Using EIS Data Augmentation Based on VAE Model
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Wei CHANG, Zhichao HU, Duozhao PAN, Jiwen SHI
Journal of Power Supply | 2025, 23(8) : 293 - 301
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Journal of Power Supply | 2025, 23(8): 293-301
• Battery and Energy Storage •
Prediction of SOH and RUL for Lithium-ion Batteries Using EIS Data Augmentation Based on VAE Model
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Wei CHANG, Zhichao HU, Duozhao PAN, Jiwen SHI
Affiliations
  • Nantong LeChuang New Energy Co., Ltd., Nantong 200233, China
Published: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.293
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The state-of-health (SOH) and remaining useful life (RUL) of batteries are important indicators for measuring their performance and conditions, and an accurate prediction of these indicators has high practical value. The electrochemical impedance spectroscopy (EIS) test of batteries can reveal the electrochemical reactions and processes inside them, providing important information for evaluating their SOH. EIS can be used to predict SOH and RUL, but it has high testing costs and limited data volume. The use of variational autoencoder (VAE) for data augmentation can expand the volume of EIS data, which helps improve the model’s generalization capability and reduce the risk of overfitting. Due to the fact that the EIS data is usually high-dimensional and may contain redundant information, principal component analysis (PCA) can be used to reduce the dimensionality of data while retaining the key information. Convolutional neural network (CNN) can recognize the spatial features in the EIS data, while bidirectional long short-term memory (BiLSTM) network can capture features within the time series data. The Attention mechanism can enhance the model’s understanding of data contextual relationships. The research results indicate that the model combining PCA, CNN, BiLSTM network and Attention mechanism can more effectively predict the SOH and RUL of batteries with the addition of augmented data. The proposed EIS data augmentation method can also be applied to other models to improve the predictive performance.

Variational autoencoding  /  impedance spectrum  /  electrochemical impedance spectroscopy (EIS)  /  state-of-health (SOH)  /  remaining useful life (RUL)  /  principal component analysis (PCA)  /  convolutional neural network (CNN)  /  bidirectional long short-term memory (BiLSTM) network
常伟, 胡志超, 潘多昭, 师继文. 基于VAE模型的EIS数据增强的锂电池SOH和RUL预测. 电源学报, 2025 , 23 (8) : 293 -301 . DOI: 10.13234/j.issn.2095-2805.2025.8.293
Wei CHANG, Zhichao HU, Duozhao PAN, Jiwen SHI. Prediction of SOH and RUL for Lithium-ion Batteries Using EIS Data Augmentation Based on VAE Model[J]. Journal of Power Supply, 2025 , 23 (8) : 293 -301 . DOI: 10.13234/j.issn.2095-2805.2025.8.293
Year 2025 volume 23 Issue 8
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doi: 10.13234/j.issn.2095-2805.2025.8.293
  • Receive Date:2024-01-18
  • Online Date:2026-10-26
  • Published:2025-12-30
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  • Received:2024-01-18
  • Revised:2024-02-26
  • Accepted:2024-03-27
Affiliations
    Nantong LeChuang New Energy Co., Ltd., Nantong 200233, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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