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abnormal state detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1757-1781页

作者： Haoyi Zhong Yongjiang Zhao Chang Gyoon Lim Department of Computer Engineering Chonnam National UniversityYeosu59626South Korea

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series *** the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy *** are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP ***,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission *** methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB *** response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB *** method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal *** is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time *** tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

关键词： Lithium-ion battery abnormal state detection autoencoder virtual power plants LSTM

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