3 citations · 3 across the 6 of their papers we have counts for
6 papers
Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
Thorsten Tegetmeyer-Kleine, Thomas Schmitt, Phillip Aquino +3
Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quantitative microscopy of this degr…
Cross-modal topology decodes battery faults from sparse voltage snapshots
Jinwen Li, Yunhong Che, Simona Onori +2
Battery safety remains the primary bottleneck for mass electric vehicle (EV) adoption, yet field monitoring is hamstrung by a fundamental asymmetry: complex electrochemical faults…
From inconsistency to decision: explainable operation and maintenance of battery energy storage systems
Jingbo Qu, Yijie Wang, Yujie Fu +3
Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagn…
Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation
Amir Ali Panahi, Daniel Luder, Billy Wu +3
Reliable digital twins of lithium-ion batteries must achieve high physical fidelity with sub-millisecond speed. In this work, we benchmark three operator-learning surrogates for th…
Fast data augmentation for battery degradation prediction
Weihan Li, Harshvardhan Samsukha, Bruis van Vlijmen +4
Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field,…
Forecasting battery capacity and power degradation with multi-task learning
Weihan Li, Haotian Zhang, Bruis van Vlijmen +2
Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capa…