Project description
AI-powered battery management for greener energy
The EU’s goal of achieving a carbon-neutral economy by 2050 requires a significant expansion of renewable energy sources, with energy storage playing a crucial role. Second-life batteries can contribute to the advancement of an electrified, decarbonised society. However, to ensure safe and efficient battery operation, battery management systems (BMS) require improved data and battery models. To address this, the EU-funded ENERGETIC project leverages AI to develop an enhanced BMS suitable for both transportation and stationary applications. This innovative system optimises battery usage, ensuring reliability, power, and safety across all operational modes. The project employs cutting-edge approaches, blending physics and data-based methods at the software and hardware levels, allowing it to predict battery lifespan and diagnose degradation using transparent AI models.
Objective
The EU roadmap towards a climate-neutral economy by 2050 sets ambitious decarbonisation targets that shall be achieved by a massive deployment of renewable energy sources. Energy storage improves grid flexibility and allows higher penetration levels of renewable energy sources to create a decarbonised and more electrified society by means of leveraging second-life batteries. Battery management plays an essential role by ensuring an efficient and safe battery operation. However, current battery management systems (BMS) typically rely on semi-empirical battery models (such as equivalent-circuit models) and on a limited amount of measured data.
Therefore, ENERGETIC project aims to develop the next generation BMS for optimizing batteries’ systems utilisation in the first (transport) and the second life (stationary) in a path towards more reliable, powerful and safer operations. ENERGETIC project contributes to the field of translational enhanced sensing technologies, exploiting multiple Artificial Intelligence models, supported by Edge and Cloud computing. ENERGETIC’s vision not only encompasses monitoring and prognosis the remaining useful life of a Li-ion battery with a digital twin, but also encompasses diagnosis by scrutinising the reasons for degradation through investigating the explainable AI models. This involves development of new technologies of sensing, combination and validation of multiphysics and data driven models, information fusion through Artificial Intelligence, Real time testing and smart Digital Twin development. Based on a solid and interdisciplinary consortium of partners, the ENERGETIC R&D project develops innovative physics and data-based approaches both at the software and hardware levels to ensure an optimised and safe utilisation of the battery system during all modes of operation.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencessoftware
- natural scienceschemical scienceselectrochemistryelectric batteries
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
67084 Strasbourg
France