Leveraging the knee point: boosting remaining useful life prediction accuracy for lithium-ion batteries with virtual-enhanced normalizing flow

Abstract

Deep learning has emerged as a powerful tool for predicting the remaining useful life (RUL) of batteries, contingent upon access to ample data. However, the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges. To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods, we introduce a novel data augmentation framework for RUL prediction. This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point, a critical juncture marking a significant shift in the degradation trajectory. By focusing on this critical knee point, we leverage the power of normalizing flow models to generate virtual data, effectively augmenting the training sample size. Additionally, we integrate a Bayesian Long Short-Term Memory network, optimized with Box-Cox transformation, to address the inherent uncertainty associated with predictions based on augmented data. This integration allows for a more nuanced understanding of RUL prediction uncertainties, offering valuable confidence intervals. The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory. The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization, highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.