Abstract: Lithium-ion battery capacity prediction is critical for enhancing battery economy and safety,yet existing methods face challenges of incomplete local information capture and poor data quality.To address these issues,this paper proposes a novel framework integrating seasonal-trend decomposition using Loess(STL) with advanced modeling techniques.Firstly,a diffusion model(DMnet) is developed by embedding STL into the diffusion process,where STL-extracted features serve as prior conditions to refine data generation.Secondly,an encoder-decoder attention model(STLnet) is designed by incorporating STL to better capture temporal feature dependencies.Finally,an integrated model(SDMnet) is constructed by synergizing the diffusion and prediction models,leveraging STL-derived features to dynamically regulate both data augmentation and prediction processes for high-precision capacity forecasting.Comparative experiments demonstrate that STL-guided data augmentation via diffusion effectively enhances model generalization,while the improved STLnet achieves superior prediction accuracy.Collectively,SDMnet exhibits outstanding performance in both prediction precision and generalization capability.

Key words: Lithium-ion battery capacity prediction, Diffusion model, STL decomposition, Data augmentation, Encoding and decoding attention