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Deliverables

• D1.3: Requirements and Specifications for Digital Twins

• D2.1: Use case specific battery testing boundary conditions and DOE methods

• D3.1: Multiscale high-fidelity modelling paradigm for physical testing virtualization

• D4.1: Safety and reliability AI powered battery toolchain architecture and framework design

• D5.1: Ontology definition and Data Mapping of Virtual Assets

• D5.2: Digital Twin models definition

• D5.3 Integration plan for Digital Twin on the platform

• D6.1: Resource scheduling concept

• D6.2: Scheduling Software Solution

• D6.5: Real-time connection of physical and virtual bench

• D7.1: Dissemination and communication plan

• D7.4: Update dissemination and communication plan

• D8.1 Data Management Plan

Scientific Contributions

• Towards Developing an Ontology for a Digital Twin in Battery Testing

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

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

Posters

• Parametrized Physics Informed Neural Operators for Lithium Ion Battery Modeling

• Physics Informed Operator Learning for Design of Experiments Applied to Lithium-Ion-Batteries