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

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。

Abnormal Battery On-line Detection Method Based on Dynamic Time Warping and Improved Variational Auto-Encoder

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.