Area 3: Monitoring and Failure Prognosis
This Area focuses on developing diagnostic strategies alongside empirical, physics-based, and data-driven models to characterize battery state and predict safety under diverse operating conditions. Research activities focus on understanding how batteries respond to different loads, identifying intrinsic factors that influence failure mechanisms, and quantifying how safety margins evolve over time. These efforts enable the definition of safety-relevant indicators, early detection of emerging risks, and informed strategies for safe operation, predictive maintenance, and optimized second-life use. The core research activities include:
Battery State Monitoring — Quantifying Safety
Estimating the battery state and defining measurable indicators of battery safety is a central element in the formulation of the State of Safety (SOS), a universal metric for assessing the intrinsic safety of a battery. Work includes developing online and offline qualification methodologies, real-time diagnostic concepts, and damage-specific safety indicators, as well as determining optimized signal requirements, measurement settings, and sensor strategies for accurate state assessment.
Failure Prognosis — Predictive and Adaptive Operation
Evaluating how the safety state evolves under operational conditions and predicting the Remaining Safe Useful Life (RSUL). Work also supports adaptive strategies for load and energy management and the definition of replacement strategies and safe, optimized second-life deployment.
Area 1
Prediction and Optimization
Area 1 focuses on developing advanced models and simulation methods to predict, optimize, and improve the safety and performance of battery cells and systems. By combining multi-physics modeling, virtual development, and research on next-generation battery technologies, it reduces testing effort and accelerates reliable battery design across the entire lifecycle.
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.
