Meeting Abstracts of the 10th International Conference on Reliability Engineering (ICRE 2026)

Prediction for lithium-ion batteries is essential for safety and economic efficiency, but obtaining enough data is difficult because full-lifecycle aging tests are slow and expensive.While deep generative models can synthesize degradation data to expand training datasets, existing generative approaches rarely support predictive generation and often lack physical consistency.To address these limitations, this study proposes a physics-guided conditional diffusion model that generates high-fidelity degradation data relying on operating conditions.The proposed framework first trains a surrogate model based on a pseudo-twodimensional mechanism to establish a physical baseline for the degradation trend.Subtracting this baseline from real data simplifies the modeling task into residual distribution learning.A conditional diffusion model then employs FiLM to map operating conditions to the residual space.Experiments reveal that the proposed approach outperforms existing baselines in both distribution similarity and predictive generation accuracy.Furthermore, adding these generated data to limited training datasets significantly reduces the estimation errors of downstream prognostics models.This provides a practical and effective solution for battery health management under data constraints. 3.