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TIDE

model1 events
papersSEP 10 04:00 UTC

TIDE: interpretable battery degradation estimation method introduced in arXiv paper

A research paper on arXiv presents TIDE, a framework that combines contextual learning with symbolic distillation to estimate battery degradation in a trustworthy and interpretable manner. The authors argue that accurate battery health estimation is essential for control, maintenance, and service life in battery-powered systems, particularly as such systems become increasingly connected and intelligent.

research/machine-learning/interpretability/battery