Physics-informed statistical learning of battery lifetime

Project aims

Several challenging objectives are anticipated to be achieved. Understanding and modelling the relevant physics that drives battery degradation is a primary objective. The physics will inform how the remaining energy of a battery gradually diminishes with use followed by a sudden decrease of the energy. Known as the 'knee-point', statistical learning of its underlying mechanisms and predicting when it will occur is another key objective of the project. This will require advances in physics-informed machine learning in order to combine traditional mechanistic approaches with Bayesian statistical learning for an overall semi-parametric framework.

Demonstrating and validating the developed methods on real battery ageing data, that will be performed at WMG University of Warwick, to predict the battery life-time will be a key success. The dissemination of the methodologies, through publications and conferences, and it's uptake from industry and other academic groups will also signify key success of the project.

The theoretical developments and implementations will benefit wider academic groups from both engineering and computer science working in both theory and application of dynamical systems. Combining the strengths of predictive battery modelling together with the latest statistical learning approaches also offers an attractive solution to address a pressing challenge in the automotive sector. Reliably predicting when a battery 'knee-point' can occur will render major opportunities to de-risk the sector's warranty and battery pack design phase.