Area 1: Prediction and Optimization
This area focuses on understanding, predicting, and optimizing the behavior of energy storage systems, especially the battery cells inside, under both normal operating conditions and abusive loads. Additional to new characterization methods for analyzing the mechanical, thermal, and electrochemical responses, we develop advanced simulation models that support improvements in safety, performance, and sustainability. These models form the foundation for virtual development workflows that reduce physical testing efforts, accelerate design cycles, and enable more robust and reliable battery systems throughout their lifecycle. This research area is structured into four complementary dimensions.
1. Scale-up and Virtual Development
We bridge the gap from single-cell behavior to higher scales by building scalable, physics-based models. These allow virtual prototyping of modules and packs, supporting rapid design iterations and early identification of safety-critical behavior.
2. Component-Based Cell Behavior Prediction
By breaking down cells into their constituent components and material properties, we generate first-principles predictions of electrochemical, thermal, and mechanical responses. This enables early-stage forecasting of battery behavior even before the physical cells are available for characterizations through experiments.
3. New Battery Technologies
We investigate next-generation chemistries such as solid-state and sodium-ion batteries. Here we are analyzing if the methodologies which are developed for well-known batteries such as lithium-ion ones with liquid electrolyte are usable for those newer ones. Our models and characterization methods guide technology comparison, maturity assessment and the identification of potential performance and safety advantages.
4. Multi-Physical Characterization and Modeling
We develop and integrate models capturing the interactions between mechanical deformation, heat generation and dissipation, and electrochemical processes. This multi-physics approach provides a deeper understanding of the complex battery behavior under real-world conditions.
Area 2
Degradation Mechanisms
Area 2 investigates the aging and degradation mechanisms of battery systems and safety-relevant components, developing predictive models and accelerated aging methods that link degradation to safety-critical thresholds in order to reliably assess and forecast changes in safety margins over the entire battery lifetime.
Area 3
Monitoring and Failure Prognosis
Area 3 develops diagnostic strategies and physics-based as well as data-driven models to monitor battery state and define a State of Safety (SOS), enabling the prediction of safety evolution and Remaining Safe Useful Life (RSUL) in order to support early risk detection, predictive maintenance, adaptive operation, and optimized second-life deployment.
