FESD: Feature-Enhanced Structured-State-Space Diffusion Model for Battery SOH Prediction and Imputation

Accurate state-of-health (SOH) prediction and data imputation are critical for advanced battery management systems but remain challenging due to complex degradation patterns, diverse operating conditions, and varying battery materials. Although transfer learning methods have demonstrated potential in dealing with battery SOH prediction with various conditions, their need for fine-tuning limits real-world applications. To address these challenges, the feature-enhanced structured-state-space diffusion (FESD) model is proposed for SOH prediction and imputation across diverse working conditions. In FESD, the framework first extracts degradation features from raw battery cycle data through feature engineering. These features, along with SOH data, are then processed by a Transformer-based architecture to capture intricate degradation patterns and long-range dependencies. Finally, the processed features are fed into a diffusion model built upon structured-state-space equations, which effectively captures temporal dynamics and allows for simultaneous SOH prediction and imputation. Evaluations on the MIT, Center for Advanced Life Cycle Engineering (CALCE), NASA, and Hawaii Natural Energy Institute (HNEI) datasets demonstrate FESD’s superior performance in SOH prediction and imputation. Notably, when applied to a new dataset without fine-tuning, FESD achieves comparable performance to state-of-the-art transfer learning methods, illustrating its strong generalization capabilities.