Gaussian-process based inference of electrolyte decomposition reaction networks in Li-ion battery failure

Bugryniec, P.J. orcid.org/0000-0003-3494-5646, Yeardley, A. orcid.org/0000-0001-7996-0589, Jain, A. et al. (3 more authors) (2022) Gaussian-process based inference of electrolyte decomposition reaction networks in Li-ion battery failure. In: Montastruc, L. and Negny, S., (eds.) 32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32. 32nd European Symposium on Computer Aided Process Engineering (ESCAPE-32), 12-15 Jun 2022, Toulouse, France. Computer Aided Chemical Engineering (51). Elsevier , pp. 157-162. ISBN 9780323958790

Abstract

Li-ion batteries (LIBs) are widely adopted in EVs and stationary battery energy storage due to their superior performance over other battery chemistries. But LIBs come with the risk of thermal runaway (TR) which can lead to fire and explosion of the LIB. Hence, improving our understanding of TR is key to improving LIB safety. To achieve this, we aim to develop a detailed model of LIB TR, as existing models are oversimplified and often lead to inaccuracies when compared to experiments. To build a realistic representation of the reaction network (RN) for LIB TR, we present a case study on the ethylene carbonate (EC) solvent component of the LIB electrolyte. We use a RN for EC identified from literature to build a micro-kinetic model and optimize it against experimental data. Parameters optimisation and sensitivity analysis for a complex RN is made possible by using Gaussian Processes (GPs). It is found that the only four of the 14 parameters influence the simulation output significantly. Also, this work highlights areas of GP development for improved surrogate modelling of this type of problem. From this the methodology can be scaled to larger networks and can be applied LIB TR models to improve their accuracy, which in turn will help the development of safer LIBs.