The EU aims to have at least 30 million zero-emission vehicles, primarily powered by lithium-ion batteries, on the roads by 2030. However, several fundamental scientific issues related to the safety of these batteries need to be addressed. There is currently a lack of control-oriented models for predicting the internal phenomena that can trigger thermal runaway. The EU-funded MoreSafe project will develop a comprehensive physics-based approach that will adequately incorporate a highly accurate description of battery electrochemistry and the accompanying subtle lithium plating phenomenon. The method will allow fast and accurate battery safety state prediction and analysis as well as seamless integration into a safety-guaranteed battery management system.
EU will aim to have at least 30 million zero-emission vehicles by 2030, primarily powered by the current energy storage technology of choice - lithium-ion batteries. Despite remarkable achievements in developing control strategies over recent decades, many fundamental scientic issues underpinning the safety of these batteries remain elusive, due to a lack of control-oriented models for predicting the internal phenomena that can trigger internal short circuits and the consequent thermal runaway. In this proposal, we document a wholistic new physics-based modelling approach, which adequately incorporates a highly accurate description of battery electrochemistry as well as the accompanying subtle lithium plating phenomenon. It will allow reliable prediction of the occurrence of safety accidents subject to battery operational conditions and the seamless integration into a safety-guaranteed battery management system. As an MSCA-PF fellow, Dr. Yicun Huang will receive crucial training at the Chalmers University of Technology and engage in detailed battery modelling works which span the areas of computational materials and electrical engineering. The interplay between the two areas will spark an innovative and productive throughput, including 1) a multiphase electrochemical model under normal battery operations, 2) a high-fidelity materials model for the growth morphology of lithium plating, and 3) control-oriented models for battery safety assessment. The result of this project will include: a ground-breaking multiple-model framework enabling fast and accurate battery safety state prediction and analysis; a new interdisciplinary research product amalgamating materials modelling and control algorithms; outreach and dissemination to crucial target audiences; and a solid foundation for a safety-guaranteed battery management system intended to revolutionise electric vehicles.