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Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data
Accurate and rapid state of health (SOH) estimation is crucial for battery management
systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and …
systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and …
A survey on few-shot learning for remaining useful life prediction
R Mo, H Zhou, H Yin, X Si - Reliability Engineering & System Safety, 2025 - Elsevier
The prediction performance of most data-driven remaining useful life (RUL) prediction
methods relies on sufficient training samples, which is challenging in few-shot scenarios …
methods relies on sufficient training samples, which is challenging in few-shot scenarios …
[HTML][HTML] State of the Art in Electric Batteries' State-of-Health (SoH) Estimation with Machine Learning: A Review
The sustainable reuse of batteries after their first life in electric vehicles requires accurate
state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies …
state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies …
Deep learning-based Remaining Useful Life Prediction of Lithium-ion Battery Considering Two-phase Aging Process
W Ma, H Zhu, J Wu, S Zhang - Journal of The Electrochemical …, 2024 - iopscience.iop.org
The aging process of lithium-ion batteries is typically nonlinear, characterized by a knee
point that divides it into two distinct phases: a slow aging phase and a rapid aging phase …
point that divides it into two distinct phases: a slow aging phase and a rapid aging phase …