Development of Advanced Diagnostic and Control Strategies for Li-ion Batteries using Machine Learning - Next-Generation BMS

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While Li-ion battery chemistry continues to advance apace, the ability to control and monitor batteries when assembled into packs is lacking in many respects. The battery management system (BMS) monitors and controls cells within a pack, ensuring that they do not get too hot, balancing charge, dealing with safety aspects, etc. This aspect of battery engineering often lets down the significant effort that goes into improving battery performance at the cell level. The aims of this research project are:- Develop new methodologies based on machine learning to accurately describe the performance of Li-ion cells.- Develop diagnostic tools that can be deployed on battery assets to predict future performance based on real-time and historical data.- Produce an advanced BMS capable of controlling a battery pack to a new level of accuracy and functionality, ready for implementation in a commercial vehicle.To achieve this, the project will acquire commercial modules, work closely with industry (e.g. Horiba MIRA) to learn how to implement new algorithms into commercial BMS systems and combine modelling insight with experimental practice to bring new BMS technologies to a point of investment/commercialisation potential.