Künstliche Intelligenz in der Entwicklung von Material, Zellen und Batteriesystemen

Segmentierung und Augmentierung von Rasterelektronenmikroskopie-Daten mittels Deep Learning-Methoden

Improving battery development by leveraging representative and dynamic load profiles derived from cloud customer data

From Variance Attribution to Causal Inference: Understanding Impedance Variance Drivers for Robust Battery State Estimation

AI-Readiness von Batteriedaten: Datenvorverarbeitung für ML-basierte Batteriezelldiagnostik

AI-Assisted Mapping of Life Cycle Inventory Data for Life Cycle Assessment

Physics-informed Modeling Framework for AI-based Battery Pack Design and Diagnostics Optimization

Defect Analysis of as-assembled All-Solid-State Batteries via AI enhanced Scanning Acoustic Microscopy

Machine learning for battery quality classification and lifetime prediction using formation data

Modeling Swelling Behavior in Nano-Silicon Coated Graphite Anodes Using Discrete Element Method: Insights into Particle-Level Interactions and Irreversible Volume Changes