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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
D2.2 Definition of battery system testing for automotive, off-road and stationary use cases
D2.3 Concepts for smart combination of physical and virtual testing
D3.1: Multiscale high-fidelity modelling paradigm for physical testing virtualization
D3.2 Reduced-order model development and validation
D3.3 Data-driven model development and validation for virtualization of performance and ageing testing
D3.4 Physical testing report for model validation and characterization
D4.1: Safety and reliability AI powered battery toolchain architecture and framework design
D4.2 High fidelity battery AI-powered battery multi-domain toolchain safety and reliability development
D4.3 Integration & optimisation of battery AI-powered battery multi-domain toolchain cell to system level
D4.4 Battery AI-powered toolchain validation and verification strategies
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
D5.4 Digital Twin Integration
D6.1: Resource scheduling concept
D6.2: Scheduling Software Solution
D6.3: Integration DoE algorithms hybrid platform
D6.4: LIMS integration with Digital Twin
D6.5: Real-time connection of physical and virtual bench
D6.6 Final validation in exemplary environment
D7.1: Dissemination and communication plan
D7.4: Update dissemination and communication plan
D7.5 Update dissemination and communication plan 2
D8.1 Data Management Plan