Ultra-fast physics-based battery models with degradation leveraging machine learning and Bayesian inference

The overarching aim of the project is to develop novel simulation and parameterisation tools that can: (i) find solutions to physics-based models rapidly enough to unlock practical and realistic studies of degradation over full cell lifetimes and, (ii) solve the optimisation problems needed to parameterise these physics-based models from data and to find optimal device designs.