Open Access

ARTICLE

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim^*

Department of Computer Engineering, Chonnam National University, Yeosu, 59626, South Korea

* Corresponding Author: Chang Gyoon Lim. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences 2024, 140(2), 1757-1781. https://doi.org/10.32604/cmes.2024.049208

Received 30 December 2023; Accepted 27 February 2024; Issue published 20 May 2024

Abstract

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data. In 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 data. The 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 frequencies. This 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 series. Empirical 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.

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