Public Funding for Ion-O Tech Ltd

Lithium-ion batteries are an essential component for electric vehicles, however they degrade over time. This degradation is complex and depends on factors such as: load profile, temperature and state-of-charge limits. To maintain performance, operational limits are often imposed, however these are often static and suboptimal. In a more ideal scenario these limits should change with operating conditions and battery state-of-health. This project will address the issue of extending battery lifetime through the development of an innovative battery digital twin. This digital twin fuses state-of-the-art battery models, with real-time data measurements and machine learning algorithms. In doing this, key health metrics of the battery can be tracked, with this information being used to dynamically optimise its use and extend the batteries lifetime. This battery digital twin will be implemented onto a modular and swappable battery for micro emobility applications such as escooters and ebikes; allowing for asset optimisation and unlocking new business opportunities such as second life battery applications.