Künstliche Intelligenz in der Entwicklung von Material, Zellen und Batteriesystemen
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KI01
Segmentierung und Augmentierung von Rasterelektronenmikroskopie-Daten mittels Deep Learning-Methoden
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KI02
Improving battery development by leveraging representative and dynamic load profiles derived from cloud customer data
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KI03
From Variance Attribution to Causal Inference: Understanding Impedance Variance Drivers for Robust Battery State Estimation
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KI04
Machine learning for battery quality classification and lifetime prediction using formation data
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KI05
Modeling Swelling Behavior in Nano-Silicon Coated Graphite Anodes Using Discrete Element Method: Insights into Particle-Level Interactions and Irreversible Volume Changes
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KI06
Physics-informed Modeling Framework for AI-based Battery Pack Design and Diagnostics Optimization
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KI07
AI-Readiness von Batteriedaten: Datenvorverarbeitung für ML-basierte Batteriezelldiagnostik
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KI08
AI-Assisted Mapping of Life Cycle Inventory Data for Life Cycle Assessment
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KI09
Defect Analysis of as-assembled All-Solid-State Batteries via AI enhanced Scanning Acoustic Microscopy