Project Results
CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Deliverables
This deliverable will present the listed requirements and specify the DTs to be used in the virtual testing plan.
D8.1 Data Management Plan (opens in new window)Description of the data management life cycle for the data to be collected, processed and/or generated during the project. The data will be collected in a specific database according to the rules of tracebility.
D6.5 Real-time connection of physical and virtual bench (opens in new window)This deliverable will describe the concept work, implementation, stabilization, testing, and real-life validation of a connection between a physical and virtual test bench in the cloud. Therefore, the first matching and feasible technologies for the connected execution of a test in the virtual and physical domains will be investigated.
D4.1 Safety and reliability AI-powered battery toolchain architecture and framework design (opens in new window)This deliverable will demonstrate the design, which will contain the fundamental architecture of the toolchain, input and output parameters, and interfaces with other components of the DT according to the different types of domains. In addition, the needs specifications included in WP1 and WP2 serve as the foundation for the baseline of the toolchain.
D5.3 Integration plan for Digital Twin on the platform (opens in new window)This deliverable will define the system architecture, communication, data structure, software and hardware requirements, as well as the data pipeline for the integration of DT on the platform. The specification of the integration plan will identify technical restrictions and dependencies for the integration phase.
D2.1 Use case specific battery testing boundary conditions and DOE methods (opens in new window)This deliverable will provide the formulation of the objectives, success criteria and boundary conditions of the three project use cases automotive, off-road and stationary battery systems. Additionally, DoE approaches and methods are summarized and evaluated.
D6.3 Integration of DoE algorithms to hybrid platform (opens in new window)This deliverable will describe the operation and technical limitations of virtual benches similar to physical benches. Moreover, it will investigate the integration of the deliverables of T2.2 and T2.3 with the capabilities of a test centre in order to demonstrate how the software technically connects the DoE service to the final usage concept.
D3.3 Data-driven model development and validation for virtualization of performance and ageing testing (opens in new window)In this deliverable, the data-driven model development, validation and robustness assessment are demonstrated for performance and ageing testing. The applied techniques for data-driven modelling will be described and justified.
D2.3 Concepts for smart combination of physical and virtual testing (opens in new window)This deliverable will provide concepts and investigations for smart combination of virtual and physical testing. The provided testing approaches and the smart combinations will be described and evaluated.
D4.3 Integration & optimisation of battery AI-powered battery multi-domain toolchain cell to system level (opens in new window)This deliverable will conduct an FMEA, hazard analysis, and risk assessment on crucial components and analyze the various subsystems and innovative concepts, such as the integrated approach and how the innovative toolchain approach can help increase the lifetime and reduce the cost of battery system maintenance (e.g., the partitions in the case of the integrated approach, specific virtual elements, physical components, etc.).
D6.2 Scheduling software solution (opens in new window)The mathematical descriptions for the examination and selection of appropriate optimization technologies will be presented in this deliverable. Also, the optimization methods and the best matching methods will be put into place as a prototype in order to make D6.1 work as a software module.
D7.4 Update dissemination and communication plan 1 (opens in new window)This deliverable includes the actions developed during the first period in terms of dissemination and communication
D3.1 Multiscale high-fidelity modelling paradigm for physical testing virtualization (opens in new window)This deliverable will determine the different multiscale high-fidelity modelling paradigms that allows the physical testing virtualization of performance and ageing testing. As the main output to the deliverable, the modelling approach that best suits the project and case studies will be defined.
D3.2 Reduced-order model development and validation (opens in new window)This deliverable will include the reduced-order model development and validation to ensure a good compromise between computational cost and accuracy (with respect to T3.1). The ROM model will be ready for transferability to the WP4-WP6
D4.4 Battery AI-powered toolchain validation and verification strategies (opens in new window)The toolchain is tested in this delivery using the degradation scenarios provided in T4.1. It specifies the data required to assess the system's functional safety in various circumstances. Furthermore, the safety, age, and reliability of the battery pack system will provide DT criteria-compliant toolchain certification.
D6.4 LIMS integration with Digital Twin (opens in new window)This deliverable will define the test centres’ link to the DT system to retrieve critical UUT information. Furthermore, the carries of the measurement recordings obtained by DT during the several measurement campaigns of a UUT's lifecycle will be studied to demonstrate the integration of LIMS with the DT approach.
D6.6 Final validation in exemplary environment (opens in new window)This derivable description of all produced systems with the LIMS in an exemplary environment by selected test fields must be prepared from the perspective of the IT infrastructure and the LIMS's relationship to the particularly employed bench automation system. As a result, this deliverable operates the whole system and validates whether the project's global goals and proposal reduction potential can be met.
D2.2 Definition of battery system testing for automotive, off-road and stationary use cases (opens in new window)This deliverable will provide the transfer of the investigated DoE approaches described in D2.1 to derive optimal battery system testing procedures and methods for the project use cases automotive, off-road, and stationary battery testing. The presented procedures are evaluated according to the specific use case objectives.
D3.4 Physical testing report for model validation and characterization (opens in new window)This deliverable will include a summary of the physical testing results for Gen3b and Gen4a that are performed during the project for model characterization and validation. The purpose is to ensure the validity of the models reducing to the minimum the physical testing that is required, to allow achieving the objectives of the project.
D6.1 Resource scheduling concept (opens in new window)This deliverable investigates whatever daily actions are performed today to do test resource scheduling in a test centre to efficiently utilize available hardware, resources, test requirements, boundary conditions, and other restrictions. As a result of this deliverable, representative task descriptions, test data, and real-life events are taken into account.
D5.1 Ontology definition and Data Mapping of Virtual Assets (opens in new window)This deliverable will describe and set the ontology principles, based on industry similar pre-existing standards, thus, allowing the definition of the vocabulary and characterizing the type of entities that exist in the environment. This will allow the identification of sources and relationships, as well as outline the Data to be fed to the DTs.
D4.2 High fidelity battery AI-powered battery multi-domain toolchain safety and reliability development (opens in new window)This deliverable will include the techniques for developing the high-fidelity multi-domain toolchain that will be used to assess the battery's reliability and safety at the cell-to-pack level while also taking into consideration the effects of aging. The functionality and interfaces between the internal models in the system and the subsystems of the toolchain at the cell-to-pack level are included in the description of the toolchain. This is perhaps the most significant aspect of the toolchain.
D7.1 Dissemination and communication plan (opens in new window)This deliverable will describe a primary Communication and Dissemination plan that will contain the main strategy for the project lifetime, as well as the expected activities to be carried out throughout the project. It will be updated on M18 and 36 to include the actions developed during those periods
D5.2 Digital Twin models definition (opens in new window)Design and structure the composition of each individual asset of the DTs needed for the defined use-cases. The definition of the DTs properties will allow the construction of the models and validate the integration plan with the required supporting modules (prediction/simulation).
D5.4 Digital Twin integration (opens in new window)This deliverable will describe the configurations, and steps required for the deployment of the DT solution within the integration with other systems. This will result in a transparent output about the technical specifics of the DT release.
D7.5 Update dissemination and communication plan 2 (opens in new window)This deliverable will include the actions developed during the last period of the project in terms of dissemination and communication
Publications
Author(s):
Jon Pišek, Tomaž Katrašnik, Klemen Zelič
Published in:
Batteries, Issue 11, 2025, ISSN 2313-0105
Publisher:
MDPI AG
DOI:
10.3390/BATTERIES11080295
Author(s):
Philipp Brendel, Igor Mele, Andreas Rosskopf, Tomaž Katrašnik, Vincent Lorentz
Published in:
Journal of Energy Storage, Issue 128, 2025, ISSN 2352-152X
Publisher:
Elsevier BV
DOI:
10.1016/J.EST.2025.117055
Author(s):
I. Sanz-Gorrachategui, A. Barrutia, A. Martín, X. Arraztoa-Lazkanotegi, D. Marcos
Published in:
The 26th European Conference on Power Electronics and Applications, 2025, ISSN 0000-0000
Publisher:
GDR SEEDS France & EPE Association
DOI:
10.34746/EPE2025-0070
Author(s):
Philipp Brendel, Christopher Straub, Andreas Rosskopf, Vincent Lorentz, Felix Dietrich
Published in:
Energy and AI, 2026, ISSN 2666-5468
Publisher:
Elsevier
DOI:
10.1016/j.egyai.2026.100847
Author(s):
Marco Rodrigues
Published in:
2026
Publisher:
International Conference on Industrial Engineering and Industrial Management
Author(s):
Philipp Brendel, Christopher Straub, Andreas Rosskopf, Vincent Lorentz, Felix Dietrich
Publisher:
IECON26
Author(s):
Nuno Marques, Marco Rodrigues, Mannin Himanshu, Foad Gandoman
Published in:
Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2024, ISSN 2184-3228
Publisher:
SCITEPRESS - Science and Technology Publications
DOI:
10.5220/0013082300003838
Intellectual Property Rights
Application/Publication number:
10
2023133703
Date:
2023-12-01
Applicant(s):
FEV IO GMBH
Searching for OpenAIRE data...
There was an error trying to search data from OpenAIRE
No results available