A self-attention enhanced generative adversarial autoencoder for anomaly detection of self-discharge of lithium-ion batteries

Abstract

Self-discharge is a crucial parameter affecting the reliability and lifespan of lithium-ion batteries (LIBs). However, traditional methods for detecting self-discharge rely heavily on time-consuming experimental testing. To address this limitation, we propose a novel model, self-attention enhanced generative adversarial autoencoder (SAE-GAAE), for rapid and accurate LIB self-discharge anomaly detection. SAE-GAAE integrates recent advances in artificial intelligence into battery production scenarios by combining a dot-product self-attention mechanism within the encoder-which captures inter-feature dependencies and highlights key indicators-with a generative adversarial component in the latent space, which enhances generalization and robustness by regularizing feature representations. This end-to-end deep learning framework enables automatic extraction of informative representations from raw input data without relying on manual feature engineering. Moreover, a tree-structured Parzen estimator (TPE)-based Bayesian optimization algorithm is employed to efficiently fine-tune model hyperparameters, improving detection performance. Applied to the capacity grading stage in LIB production, the model uses 40 features – including voltage, current, capacity, and temperature – extracted from charge–discharge curves. Experimental evaluation on real-world production data demonstrates that SAE-GAAE achieves a 26. 27% improvement in the average area under the receiver operating characteristic curve (AUC-ROC) in four models based on the autoencoder, with a detection accuracy of 99.05% and a recall rate of 100%. These results highlight the model’s practical value in enhancing battery screening efficiency while reducing reliance on long-duration standing tests.