LLM-enhanced transformer framework for accurate lithium battery remaining useful life prediction

Accurate prediction of lithium-ion battery remaining useful life (RUL) is critical for ensuring operational reliability, optimizing energy utilization, and preventing safety hazards in applications ranging from electric vehicles to grid-scale energy storage. This paper presents an innovative deep learning framework that synergizes the representational power of Large Language Models (LLMs) with sequential modeling capabilities of Transformer architectures for enhanced RUL estimation. Our methodology employs a three-stage processing pipeline: (1) Variational Autoencoder (VAE)-based tokenization to generate compact, physically meaningful representations of battery degradation patterns; (2) GLM-4 LLM feature extraction that captures complex, long-range dependencies in multi-modal battery data (voltage, current, temperature, etc.); and (3) a constrained Transformer decoder that generates accurate, electrochemically plausible RUL predictions while quantifying uncertainty. Comprehensive evaluations conducted on the EVBattery dataset reveal that the proposed framework outperforms state-of-the-art methods, achieving a prediction error of 1.27 RMSE and maintaining a low mean absolute percentage error (MAPE) of 2.97%. The proposed approach not only advances prediction accuracy but also provides interpretable insights into degradation mechanisms through its attention patterns and token representations. This work establishes a new paradigm for battery health monitoring that effectively bridges data-driven artificial intelligence with fundamental electrochemical principles, offering significant improvements in reliability and safety for energy storage systems.