Validate framework on experimental data

Background: Developing robust forecasting models for lithium-ion battery state-of-health (SOH) is crucial for energy systems, but models trained on one set of cells often perform poorly on cells with different manufacturing characteristics or operating conditions.

Question / Future Work: Future work involves validating the developed uncertainty-aware, transfer learning framework on actual experimental aging datasets to evaluate its robustness against real-world measurement noise and variability that synthetic simulations might not fully capture. Benchmarking against established public battery cycling data sets is specifically mentioned as part of this validation.