Periodic Reporting for period 1 - DigiBatt (DIGITAL SOLUTIONS FOR ACCELERATED BATTERY TESTING)
Reporting period: 2024-01-01 to 2025-06-30
The current paradigm for battery testing is fragmented, time-consuming, and expensive. To fully characterize the performance of a battery cell requires a wide variety of both destructive and non-destructive tests, some of which can last for months or years. A new paradigm for battery testing is needed that takes full advantage of the latest advances in automation, data science, and modelling to bring battery development firmly into the digital era. DigiBatt will slingshot the European battery industry forward by developing novel digital approaches to extract more value from fewer tests. This will save valuable time and resources in a highly competitive industry. DigiBatt has assembled a consortium representing some of the world's leading battery research institutions, Gigafactories, and integrators, and will apply recent advances in autonomous battery testing, model-based simulation, and data-driven semantics to promote digital battery testing from TRL 4 to TRL 6.
During the first reporting period, the project has made substantial progress towards its technical and non-technical objectives, laying the foundation for a fully integrated digital framework for battery testing, modelling, and regulatory compliance. Key achievements include the release of an open, community-driven standard for battery test data and the deployment of BattINFO-based metadata schemas to ensure semantic interoperability and FAIR compliance. These outputs underpin the automated exchange of data between experimental facilities, digital twins, and system-level simulation tools, marking an essential step toward universal interoperability across the battery value chain.
Progress has also been achieved in developing the digital twin framework and associated workflows for real-time parameterization, adaptive test reduction, and lifetime and safety prediction. Novel co-simulation capabilities now allow physical and virtual systems to interact, enabling faster, data-driven decision-making and reducing the experimental burden. First implementations of system-level integration via Functional Mock-up Units (FMUs) confirm the feasibility of plug-and-play digital twin deployment in vehicle and marine powertrain simulations. Together, these achievements set the stage for full validation in the next reporting period, supporting both industrial adoption and alignment with EU battery passport requirements.
Progress has also been achieved in developing the digital twin framework and associated workflows for real-time parameterization, adaptive test reduction, and lifetime and safety prediction. Novel co-simulation capabilities now allow physical and virtual systems to interact, enabling faster, data-driven decision-making and reducing the experimental burden. First implementations of system-level integration via Functional Mock-up Units (FMUs) confirm the feasibility of plug-and-play digital twin deployment in vehicle and marine powertrain simulations. Together, these achievements set the stage for full validation in the next reporting period, supporting both industrial adoption and alignment with EU battery passport requirements.
New testing and safety ontologies have been developed, based on existing ontologies in use in the European battery community, and have been made publicly available for free. We also have created open-source tools for data collection, storage and harvesting which use these ontologies to generate linked data.
The ontologies and tools we have created will reduce technical and economic barriers for linked-data collection and storage. Ultimately they will allow stakeholders to more easily classify and understand data which they have access to, improving reusability, increasing the value of the data and saving money and time.
The ontologies and tools we have created will reduce technical and economic barriers for linked-data collection and storage. Ultimately they will allow stakeholders to more easily classify and understand data which they have access to, improving reusability, increasing the value of the data and saving money and time.