Fast-track hybrid testing platform for the development of battery systems

Informations projet

FASTEST

N° de convention de subvention: 101103755

DOI

10.3030/101103755

Projet clôturé

Date de signature de la CE 30 Mars 2023

Date de début 1 Juin 2023

Date de fin 31 Mai 2026

Financé au titre de

• Climate, Energy and Mobility

Coût total

€ 4 781 008,71

Contribution de l’UE

€ 4 781 008,71

Coordonné par

AVESTA HOLDING
Belgium

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (26)

D1.3 Requirements and Specifications for Digital Twins

This deliverable will present the listed requirements and specify the DTs to be used in the virtual testing plan.

D8.1 Data Management Plan

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This deliverable will include the actions developed during the last period of the project in terms of dissemination and communication

Publications

Peer reviewed articles (4)

PyBEP: An Open-Source Tool for Electrode Potential Determination from Battery OCV Measurements

Auteurs: Jon Pišek, Tomaž Katrašnik, Klemen Zelič
Publié dans: Batteries, Numéro 11, 2025, ISSN 2313-0105
Éditeur: MDPI AG
DOI: 10.3390/BATTERIES11080295

Parametrized physics-informed deep operator networks for Design of Experiments applied to Lithium-Ion-Battery cells

Auteurs: Philipp Brendel, Igor Mele, Andreas Rosskopf, Tomaž Katrašnik, Vincent Lorentz
Publié dans: Journal of Energy Storage, Numéro 128, 2025, ISSN 2352-152X
Éditeur: Elsevier BV
DOI: 10.1016/J.EST.2025.117055

Developing a Model-in-the-Loop Testbench for Battery Management Systems: Advancing Test Methodologies for State-of-X Estimation

Auteurs: I. Sanz-Gorrachategui, A. Barrutia, A. Martín, X. Arraztoa-Lazkanotegi, D. Marcos
Publié dans: The 26th European Conference on Power Electronics and Applications, 2025, ISSN 0000-0000
Éditeur: GDR SEEDS France & EPE Association
DOI: 10.34746/EPE2025-0070

Physics-Informed Operator Learning for Parameter Estimation in Lithium-Ion-Battery Models Enhanced by Global Experimental Design and Local Identifiability Analysis

Auteurs: Philipp Brendel, Christopher Straub, Andreas Rosskopf, Vincent Lorentz, Felix Dietrich
Publié dans: Energy and AI, 2026, ISSN 2666-5468
Éditeur: Elsevier
DOI: 10.1016/j.egyai.2026.100847

Conference proceedings (3)

Methodology for Digital Twin-Based Product Testing in Industrial Applications: Batteries as a Use Case

Auteurs: Marco Rodrigues
Publié dans: 2026
Éditeur: International Conference on Industrial Engineering and Industrial Management

Electrode State-of-Health Estimation for Lithium-Ion Batteries Using Physics-Informed Deep Operator Networks

Auteurs: Philipp Brendel, Christopher Straub, Andreas Rosskopf, Vincent Lorentz, Felix Dietrich
Éditeur: IECON26

Towards Developing an Ontology for a Digital Twin in Battery Testing

Auteurs: Nuno Marques, Marco Rodrigues, Mannin Himanshu, Foad Gandoman
Publié dans: Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2024, ISSN 2184-3228
Éditeur: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0013082300003838

Droits de propriété intellectuelle

Patent (1)

Testsystem zum Verteilen von Tests auf Simulationsrechner und Prüfstände unter Verwendung statistischer Versuchsplanung

Numéro de demande/publication: 10 2023133703
Date: 2023-12-01
Demandeur(s): FEV IO GMBH