Battery Modelling, Analytics and Lifetime Prediction
The division Modelling, Analytics and Lifetime Prediction aims to develop a fundamental understanding of the electrochemical processes occurring in batteries, flow-cells and double-layer capacitors. The findings are transferred to models which can then for example predict aging, perform diagnostics and optimize operational strategies. Electro-thermal models for real-time applications and physico-chemical models for an accurate prediction of the batteries characteristics are currently being developed and used focusing on lithium-ion and lead-acid batteries as well as supercaps.
Our battery test center enables us to test the electrical and electrochemical characteristics (e.g. impedance spectroscopy) in addition to assessing the lifetime behavior in large test matrices.
We investigate the degradation, e.g. structural and chemical changes of active materials, cover layers and inhomogeneities, by performing cell openings and Post-Mortem Analyses to gain a more in-depth understanding of the underlying processes.
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Research Focus
- Development of physico-chemical and electro-thermal battery models for aging prediction
- Accelerated lifetime testing
- Researching the physical-chemical degradation mechanisms
- Development of efficient parameterization experiments for different model classes