RUL Prediction of Lithium-ion Batteries Based on TimeGAN-Pyraformer-BiLSTM

The capacity of lithium-ion batteries gradually degrades over time, presenting unforeseen risks and losses. Models based on data-driven approaches and neural network predictions can offer early warnings for battery failure. However, common models often face challenges with error accumulation in predicting future capacity changes, and insufficient data complicates model training. To address this, a novel method for predicting remaining battery life is proposed. This method incorporates IC analysis, DTV analysis, and Local Linear Embedding algorithms for efficient feature extraction and uses TimeGAN to augment training data, creating an integrated prediction framework. It combines the long-sequence prediction capabilities of the Pyraformer network with the dynamic multi-variable relationship capturing of the BiLSTM network, enhancing the model's understanding of capacity degradation trends. Comparative experiments demonstrate that this approach achieves higher prediction accuracy than traditional simple neural networks. Additionally, ablation studies further confirm the effectiveness of the introduced techniques in prediction tasks.