| 基于动态规整与改进变分自编码器的异常电池在线检测方法 |
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| 引用本文： | 郭铁峰, 贺建军, 申帅, 王翔, 张彬汉. 基于动态规整与改进变分自编码器的异常电池在线检测方法[J]. 电子与信息学报, 2024, 46(2): 738-747. doi: 10.11999/JEIT230084 |
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| 作者姓名： | 郭铁峰 贺建军 申帅 王翔 张彬汉 |
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| 作者单位： | 中南大学自动化学院 长沙 410083 |
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| 基金项目： | 国家重点研发计划(2020YFB1710600)~~； |
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| 摘 要： | 针对电池生产成组过程中，传统异常检测方法对混入的容量及压差异常电池检测精度低及生产结束后离线异常检测方法效率低等问题，该文提出一种集合长短期记忆变分自编码器与动态时间规整评价的锂电池异常在线检测方法(VAE-LSTM-DTW)，实现了异常电池的在线检测，避免了离线异常检测所造成的时间和能源的浪费。该方法首先将长短期记忆网络(LSTM)引入变分自编码器(VAE)模型，训练电池时序数据重构模型；其次，在电池异常检测的度量标准中引入动态时间规整值(DTW)，并基于贝叶斯寻优获得最优检测阈值，对每个单体电池重构数据的动态规整值进行异常辨别。实验结果表明，相较该领域传统异常检测方法，VAE-LSTM-DTW模型性能优越，查准率和F1值都得到了较大的提升，具有较高的有效性和实用性。
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| 关 键 词： | 锂电池 异常检测 变分自编码器 动态时间规整 长短期记忆网络 贝叶斯优化 |
| 收稿时间： | 2023-02-22 |
| 修稿时间： | 2023-06-16 |
Abnormal Battery On-line Detection Method Based on Dynamic Time Warping and Improved Variational Auto-Encoder |
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| GUO Tiefeng, HE Jianjun, SHEN Shuai, WANG Xiang, ZHANG Binhan. Abnormal Battery On-line Detection Method Based on Dynamic Time Warping and Improved Variational Auto-Encoder[J]. Journal of Electronics & Information Technology, 2024, 46(2): 738-747. doi: 10.11999/JEIT230084 |
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| Authors: | GUO Tiefeng HE Jianjun SHEN Shuai WANG Xiang ZHANG Binhan |
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| Affiliation: | School of Automation, Central South University, Changsha 410083, China |
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| Abstract: | In the process of battery production, the traditional detection accuracy of abnormal batteries is poor, and the offline anomaly detection method after production is inefficient. To solve these problems, a lithium battery anomaly online detection method integrating Long Short-Term Memory Variational AutoEncoder and Dynamic Time Warping evaluation (VAE-LSTM-DTW) is proposed, which realizes the online detection of abnormal battery conditions and prevents the time and energy wastage caused by offlize anomaly detection. Firstly, the Long Short-Term Memory (LSTM) is introduced into the Variational Auto-Encoder (VAE) model to train the battery time series reconstruction model. Secondly, in battery anomaly detection, the Dynamic Time Warping value (DTW) is introduced into the evaluation index, and the optimal detection threshold is obtained based on Bayesian optimization, and the dynamic warping value of each single battery reconstruction data is abnormally identified. The experimental results indicate that, compared with the traditional anomaly detection methods in this field, the VAE-LSTM-DTW model has superior performance, the accuracy rate and F1-score have been greatly improved, and it has high effectiveness and practicability. |
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| Keywords: | Lithium battery Anomaly detection Variational Auto-Encoder(VAE) Dynamic Time Warping(DTW) Long Short-Term Memory(LSTM) Bayesian optimization |
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