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.
Lithium batteryAnomaly detectionVariational Auto-Encoder(VAE)Dynamic Time Warping(DTW)Long Short-Term Memory(LSTM)Bayesian optimization
万方数据
维普
