Integrated Safety Modelling and Imaging for Batteries

Fundamental, multi-scale methods for predictive battery safety

Electrification is accelerating, but knowledge about the origin and propagation of Thermal Runaway (TR) remains limited. Policy frameworks (PGS/UNECE) demand demonstrable safety, while industry and government require reliable, traceable methods. Key challenges are multi-scale: uncertain kinetics of exothermic reactions at the cell level, strong coupling between cells in modules and packs, and limited validation datasets. InterSafe is developing methods for linking cell-level modeling to modules and packs, utilizing GPU-accelerated X-ray/CT data for parameter identification and testing with EIS signatures, and a single controlled TR trial. The goal: transferable, physically sound assessment frameworks.

Collaboration & Impact

InterSafe unites TNO and INNER with clear roles: TNO leads fundamental modelling, TR validation, and coupling to BMST; INNER provides the GPU AI platform for X-ray/CT processing, the datasets, and AI expertise. Impact: testable, transferable methods for design, qualification, and triage; input to BCC-NL/PGS practice; and faster, safer, circular deployment.