arXiv Machine Learning
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
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arXiv Machine Learning
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Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
arXiv:2608. 14764v1 Announce Type: new Abstract: With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical.
arXiv Machine Learning
Sep 22
Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation
arXiv:2609.22843v1 Announce Type: cross Abstract: Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applicat...
arXiv AI
Aug 18
Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
arXiv:2608. 16612v1 Announce Type: cross Abstract: An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system.
arXiv Machine Learning
Aug 3
PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
Hugging Face Trending Papers
Sep 19
Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation
Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applications such as electric vehicles and energy storage s...
arXiv Machine Learning
Jul 24
AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries
arXiv:2607. 20577v1 Announce Type: new Abstract: Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs.