Fast-track hybrid testing platform for the development of battery systems
Efficient Li-Ion Battery Testing Solutions
Current methods to evaluate Li-ion batteries safety, performance, reliability and lifetime represent a remarkable resource consumption for the overall battery R&D process. The time or number of tests required, the expensive equipment and a generalized trial-error approach are determining factors, together with a lack of understanding of the complex multi-scale and multi-physics phenomena in the battery system. Besides, testing facilities are operated locally, meaning that data management is handled directly in the facility, and that experimentation is done on one test bench.
Revolutionizing Battery Testing Technology
Accelerating Battery R&D with Advanced Testing Methods.
Interconnected approach. From a global test facility perspective, additional services like smart DoE algorithms,
The project’s prototype of a fast-track hybrid testing platform aims for a new holistic and virtualised benches, and DT data are incorporated into the daily facility operation to reach a new level of efficiency. The test facility management system retrieves all required information about its test objects from respective DT available data. Before and continuously during the test, it will decide together with the smart DoE algorithm service, if the next test measurement can be virtualised or needs to be executed on a physical test bench. Selected results are reported to the DT, to publish them globally. In this way, available resources are utilised to a maximum and seamless cooperation and communication between test requester, testing service suppliers and other incorporated parties are ensured. The work plan will advance the virtual testing platform from a concept whose components have been partially validated and well known (TRL3-4) to a complete prototype of the hybrid platform proven against the two project chemistries and three use cases (TRL6).
Within FASTEST, DoE approaches tailored to each use case will be developed to ensure that only experiments that provide high-quality information are performed. In addition, for minimising the physical testing effort, a smart combination between physical and virtual testing will be implemented that incorporates DoE methodology guidelines as well as the virtual Li-ion battery specifications. For this purpose, extended heuristic algorithms as well as AI-driven methods will be developed to have an optimal experiment distribution between virtual and physical testing, from TRL4 to TRL6.
The work of the project to implement a DT architecture virtualizing the battery system and its components will:
- Generate an adaptable and scalable ontology used for battery systems development and testing domain. This allows for a maximised precision and confidence in any analysis, relying on massive and well-structured quantities of data, thus providing comprehensive output of behavioral results to be validated in comparison with a reduced number of physical tests.
- Describe the battery system through connected discrete virtual models of battery components, mapping all the variables relevant on development and structuring these in an accessible and systematical way. Thisallows the future integration between different agnostic virtual models of each component and setting.
Eventually, the development of the battery system DT will allow for an ordered, robust data management of all the components, allowing for information management and exchange with the other elements of the fast-track hybrid testing platform. A sound data management system based on this digital replica will allow for quicker and more reliable product validation. This architecture implementation is expected to evolve from a TRL 4 to a TRL 6, given that virtual models of each component are not new themselves, but the innovation is focused on the creation of a data structure capable of virtually representing the battery system and development tests with a high degree of fidelity, as well as the entire architecture integration and communication flow with the purposed of accelerated R&D in the field of battery development where such level of virtualisation is not yet available.
FASTEST considers digitalisation approaches based on compatible (automotive, stationary energy storage, and off-road mobile devices) physics-based and data-driven models, as well as an AI-powered toolchain for assessing safety and reliability in the framework of the DT concept, to address the aforementioned challenges for battery modelling and testing.
On one hand, the project will work on developing accurate and faster virtual models for substituting
performance, thermal, ageing, and mechanical testing at cell and module levels. To do so, physics-based models will be validated (TRL4 to 5) for a wide range of operational range. Then, for a more operational use of such models without compromising model accuracy, reduced-order model techniques will be applied, also incorporating the update of model parameters to consider ageing (final TRL5). FASTEST will continue its work through data-driven models aided by experimental data and the results from physics-based enhanced models. All in all, the combination of both approaches will lead to a reduction in resources without compromising the accuracy of the results and will provide the benefit of physics-based valuable information.
On the other hand, FASTEST will work on the virtualisation of battery safety and reliability behaviour critical experiments (cell to system level) as defined by test standardisation groups. This will be done by developing an AI-powered ‘multi-physics’ toolchain, based on the different degradations' modes and conditions during the lifetime of the battery. The toolchain will be finally integrated (TRL5), considering FMEA analysis, by validating and verifying the system’s functional safety in various battery system situations within the framework of the battery standardization test matrix group.
FASTEST will ensure the protection and exploitation of research outputs in commercial (e.g., patent licensing and/or providing services to battery manufacturing industries) and non-commercial activities (e.g., policy feedback and standardisation activities). In addition, the consortium guarantees that data, scientific information and other relevant knowledge gathered from this deployment will be publicly disseminated and communicated by appropriate means (Section 2.2). The Dissemination, Exploitation and Communication (DEC) strategy of the project will follow an Open Science (OS) approach based on the principle “as open as possible and as closed as necessary” and will also enhance the involvement of relevant stakeholder groups in activities such as the definition of requirements, identification of bottlenecks for uptake or co-assessment of the piloted technologies. Moreover, the strategy aims to improve scientific cooperation by engaging with key EU initiatives such as Batt4EU and the Battery 2030+ initiative.